![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.001.png) ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.002.png) **Mastering Claude Code: End‑to‑End Python Project Development with an AI CLI Agent** **Executive Summary:** Claude Code is Anthropic’s AI coding agent that operates through a command-line interface (CLI), empowering developers to go from a blank repository to a production-ready Python project with AI-assisted workflows. This guide provides a comprehensive walkthrough of using Claude Code (as of Q1/Q2 2025) to ideate, generate, test, deploy, and maintain software via an **internet-connected CLI**. We cover the latest capabilities of Claude Code – including its integration of Anthropic’s Claude 4 models (Opus 4 and Sonnet 4), extended reasoning modes, and tool integrations – and delve into agent configuration, prompt engineering techniques, iterative development practices, CI/CD setup, and production monitoring. We also address known limitations and failure modes of AI agents and present robust strategies to mitigate them. A concluding checklist and glossary are provided for quick reference. Throughout, the guide emphasizes a formal, developer-centric approach, citing official documentation and sources for accuracy. Using Claude Code, a solo developer can leverage AI as a “coding partner” in the terminal to accelerate project velocity. The agent can read and write code, run tests, perform static analysis, handle git operations, and even fetch documentation from the web – all through natural language commands. By following best practices in prompt engineering (e.g. being specific and instructive, utilizing planning commands like “think harder” for complex tasks, and iteratively refining outputs), developers can maximize Claude’s effectiveness while minimizing errors. We explore how to configure the agent’s tool permissions and memory (via![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.003.png) CLAUDE.md project files) to ensure security and context awareness. The end-to-end workflow chapters demonstrate how to begin with project requirements, scaffold initial code, adopt test-driven development (TDD) with Claude running tests and fixing bugs, set up continuous integration pipelines, containerize and deploy the application, and incorporate logging and monitoring for production. Crucially, we outline limitations such as potential tool misuses, hallucinated outputs, or context scope issues, and how to counter them by using Claude’s permission system, verifying agent suggestions, or splitting tasks[ 1 ](https://www.anthropic.com/engineering/claude-code-best-practices#:~:text=e,often). We also introduce advanced techniques like running multiple Claude instances in parallel for independent code reviews, and extending Claude with custom tools through the Model Context Protocol (MCP).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.004.png) In sum, this guide equips developers with the knowledge to confidently integrate Claude Code into their development lifecycle. By treating Claude Code as a powerful but fallible collaborator, and by applying the strategies and safeguards detailed here, one can accelerate development from zero to production while maintaining quality and control. All recommendations are cross-verified with official sources and real-world usage insights, ensuring that the guide reflects state-of-the-art practices in 2025 for AI-assisted software development. **Table of Contents** - 1. Claude Code Models and Capabilities (Q1/Q2 2025) - 2. Installation and Setup - 3. Configuration and Project Initialization - 4. CLI Invocation Modes: Interactive vs. Headless - 5. Built-in Tools and Permission Management - 6. Extended Reasoning and “Thinking” Mode - 7. Task Chaining and Modular Workflows - 8. Prompt Engineering Strategies for Claude Code - 9. Project Ideation and Requirements Gathering - 10. Code Generation and Scaffolding Patterns - 11. Testing and Validation (TDD with Claude Code) - 12. Debugging, Linting, and Static Analysis - 13. Continuous Integration and Deployment (CI/CD) - 14. Infrastructure-Agnostic Deployment Strategies - 15. Monitoring, Logging, and Observability - 16. Long-Term Maintenance and Code Evolution - 17. Known Limitations and Failure Modes - 18. Mitigation Strategies and Best Practices - 19. Extending Claude Code with Auxiliary Tools - 20. Conclusion and Next Steps - **Deployment Checklist** - **Glossary** 1. **Claude Code Models and Capabilities (Q1/Q2 2025)** Claude Code is powered by Anthropic’s latest **Claude 4** family of models (released in 2025), which significantly advance coding assistance and agentic behavior. As of Q1/Q2 2025, the flagship model **Claude Opus 4** is touted as one of the best AI coding models available, demonstrating high performance on complex programming tasks and sustained reasoning over long sessions. Alongside Opus 4, **Claude Sonnet 4** serves as a faster, instruction-following model for general usage, and a smaller **Claude Haiku 3.5** model exists for lightweight tasks or background processes[ 2 ](https://docs.anthropic.com/en/docs/claude-code/settings#:~:text=,class%20model%20for%20background%20tasks). Claude Code leverages these models in a hybrid way: it can use the powerful Opus/Sonnet models for primary tasks, and fall back to a faster “Haiku- class” model for simple background operations if configured![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.005.png)[ 2 ](https://docs.anthropic.com/en/docs/claude-code/settings#:~:text=,class%20model%20for%20background%20tasks).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.006.png) Key model capabilities as of early 2025 include an *expanded context window* (tens of thousands of tokens) and an *“extended thinking” mode* that allows Claude to reason in depth, using tools in between reasoning steps[ 3 ](https://techcrunch.com/2025/05/22/anthropics-new-claude-4-ai-models-can-reason-over-many-steps/#:~:text=Opus%204%20and%20Sonnet%204,as%20%E2%80%9Ctacit%20knowledge%E2%80%9D%20over%20time). In extended reasoning mode, Claude can pause to perform computations or web searches, then resume answering – effectively letting it handle workflows that span multiple steps or require external information. The model can maintain a focused chain of thought across these steps, extracting and saving facts to a short-term memory to build up “tacit knowledge” about the task![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.007.png)[ 4 ](https://techcrunch.com/2025/05/22/anthropics-new-claude-4-ai-models-can-reason-over-many-steps/#:~:text=Opus%204%20and%20Sonnet%204,as%20%E2%80%9Ctacit%20knowledge%E2%80%9D%20over%20time). This means Claude Code can work on large codebases or multi-step problems without losing context, provided it uses tools to fetch relevant pieces on the fly.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.008.png) **Claude Code** specifically refers to the CLI-based agent that wraps these models with coding-specific tools and behaviors. It was introduced in early 2025 (research preview in February 2025, general availability in May 2025). Claude Code’s design enables developers to run AI-driven coding tasks directly from a terminal environment. Notably, Claude Code can execute shell commands, edit files, read documentation from the web, and manage version control operations autonomously (with appropriate user permissions) to assist in software development. According to Anthropic, Claude Code integrates tightly with development workflows, offering “agentic” capabilities – meaning it can act on the developer’s behalf in performing actions (like modifying code or running tests) rather than just suggesting text. Some headline capabilities of Claude Code as of Q2 2025 are: - **In-Depth Code Assistance:** It can write and edit code in multiple files, refactor functions, and fix bugs when instructed in natural language. The underlying model is proficient in Python (the focus of this guide) and other languages, leveraging its training on code to generate syntactically correct and logically coherent solutions. - **Codebase Understanding:** Claude Code can answer questions about a codebase’s architecture or logic by reading relevant files on demand. For example, you can ask “How does the payment processing system work?” and Claude will locate and summarize the pertinent code (using its file search and read tools). - **Automated Testing and Debugging:** The agent can execute tests and interpret their output. It is capable of running a test suite via shell commands, analyzing failures, and then autonomously editing code to address the issues. It similarly can perform linting or static analysis by running tools (like flake8, mypy, etc.) and then fix the highlighted problems. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.009.png) - **Git Integration:** Claude Code is aware of version control context. It can git add/commit changes, generate commit messages, create pull requests, and even help resolve merge conflicts. It uses the project’s Git history to inform answers (e.g., identifying when a bug was introduced or why a decision was made, by searching commit messages). - **Web Browsing and Documentation Fetching:** Through integrated tools, Claude can perform web searches and fetch content from URLs when it needs external information. For instance, it can look up Python library documentation or Stack Overflow solutions relevant to the task. This is done in a controlled manner, requiring user permission for internet access (more on the permission system in Section 5). - **IDE and Platform Integration:** Although this guide focuses on CLI usage, it’s worth noting that Claude Code also offers integrations with popular IDEs (Visual Studio Code, JetBrains suite) and GitHub Actions as of 2025. In IDEs, Claude’s suggestions appear inline as it edits your files, enabling a pair-programming experience. In CI/CD pipelines (GitHub Actions), Claude Code can be invoked to automatically address code review feedback or fix issues when a PR is opened. These integrations reflect the versatility of Claude Code, though they operate on the same core agent described here. In summary, Claude Code (2025 edition) brings the power of the Claude 4 AI models into the developer’s terminal. It operates with an extendable toolset and a high degree of autonomy, all while keeping the developer in control via a permission and configuration system. The following sections will detail how to install and configure Claude Code, and then leverage these capabilities throughout a project’s lifecycle. 2. **Installation and Setup** To get started with Claude Code, you need to install the CLI tool and authenticate it with access to the Claude AI models. The system requirements are modest: **Node.js 18+** is required (the CLI is distributed as an npm package), along with a minimum of 4 GB RAM and a Unix-like environment (macOS or Linux, or Windows via WSL). Git is recommended (for version control features), and optional tools like the GitHub CLI (![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.010.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.011.png) gh ) and **ripgrep** ( rg ) can enhance certain capabilities (PR creation and faster file search, respectively). **Installation Steps:** 1. **Install Node.js 18+** – Ensure you have Node.js (v18 or later) installed. This provides the runtime for the Claude Code CLI. If on Windows, set up a WSL (Ubuntu or Debian recommended) environment to run Claude Code, as it’s not native on Windows. 1. **Install the Claude Code package** – Use npm to install globally. Run: npm install -g @anthropic-ai/claude-code![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.012.png) This will add the claude command to your PATH. *Note:* Do **not** use sudo when installing globally, to avoid permission issues. If you encounter EACCESS errors, consider using Node’s ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.013.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.014.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.015.png) nvm or adjust directory permissions per Node recommendations. 3. **Launch the CLI and authenticate** – Navigate to a project directory (or an empty directory for a new project) and run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.016.png) claude for the first time. Claude Code will prompt you to authenticate your API access. There are multiple authentication methods: 3. *Anthropic API (Console)* – By default, Claude Code can open a browser to authenticate via the Anthropic Console. You’ll need an Anthropic account with an API key or OAuth login and an active billing plan (Claude Code access is included for Claude Pro/Max plans). After logging in and granting access, the CLI will store credentials (e.g., an API token). 3. *Claude.ai Max Plan* – If you are a Claude.ai subscriber (Max plan, which bundles Claude Code and the chat interface), you can log in with those credentials, selecting the appropriate option on launch. This ties the CLI usage to your subscription. 3. *Enterprise (Bedrock/Vertex)* – For enterprise users, Claude Code can be configured to use Amazon Bedrock or Google Vertex AI endpoints instead of Anthropic’s API. In this case, you supply cloud- specific credentials (AWS or GCP) and set environment variables to point Claude Code to those services (covered in Section 3). This allows running Claude Code in environments where direct Anthropic API access might be restricted for compliance reasons. 3. **Verify Access** – Upon successful auth, you’ll see a message that Claude Code is ready (often it may output a welcome or context summary). If authentication fails or you need to use an API key directly, you can set the environment variable ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.017.png) ANTHROPIC\_API\_KEY with a valid key (from the Anthropic Console) before running ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.018.png) claude . 3. **Troubleshooting Setup** – If on Windows via WSL, ensure Node and npm are the Linux versions (sometimes WSL might inadvertently call Windows Node). Running ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.019.png) which node inside WSL should point to a ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.020.png) /usr/bin/node path. If not, reinstall Node in WSL or use npm config set os=linux and reinstall Claude Code as per docs. Also, confirm you are in a supported region (Claude Code is geofenced to certain countries due to regulations). Once installed and logged in, the claude CLI is your gateway to interacting with the AI agent. In subsequent steps, we’ll configure it and use it to create a project. By default, if you run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.021.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.022.png) claude inside a directory containing a Git repository, it will recognize the project context (files, git history, etc.) automatically. If it’s a new project (no files yet), Claude will operate with a blank slate (aside from any instructions we provide). **Project Initialization:** For first-time use in a project, a helpful built-in command is **/init** , which generates a ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.023.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.024.png) CLAUDE.md file – a project guide for Claude (explained in Section 3) – pre-populated with information about your repository. After starting a Claude session in your project directory, simply entering /init will cause Claude to scan your project and create a starter CLAUDE.md (if one doesn’t exist). This file typically includes an overview of the repository and placeholders for conventions. You should commit this file to your repo for future reference. For a new project (blank repo), ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.025.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.026.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.027.png) CLAUDE.md will be mostly empty or contain template sections for you to fill in (or for Claude to update as the project grows). We’ll leverage this in the next section during configuration. 3. **Configuration and Project Initialization** Claude Code is highly configurable, allowing you to tailor its behavior, memory, and permissions to fit your project’s needs. Key configuration elements include ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.028.png)**settings files**, the **CLAUDE.md memory file**, and environment variables for things like model selection or enterprise mode. In this section, we set up the agent’s memory and preferences for our new Python project. 1. **Settings Hierarchy:** When you run claude config (or the in-CLI /config command), Claude Code allows you to adjust global or project settings. Configuration is stored in JSON format with a hierarchical override system:![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.029.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.030.png) - *User-Level Settings:* ~/.claude/settings.json – Defaults for your user across all projects. For example, you might allow certain safe commands globally or set a theme for the CLI UI.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.031.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.032.png) - *Project-Level Settings:* Each project can have a .claude/settings.json (committed to the repo for team-shared settings) and a .claude/settings.local.json (ignored by git, for personal settings in that project). Project settings override user settings. - *Enterprise Policy:* In managed environments, an administrator can deploy an enforced ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.033.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.034.png)policies.json (e.g., under /etc/claude-code/ on a server) that overrides user/project settings for compliance. This might disable certain features or enforce usage limits. A common configuration to set up initially is the **permissions allow/deny list**. You can specify which tools or commands Claude is allowed to run without prompting, and which are disallowed entirely. For example, ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.035.png)you might allow running your test suite and linter, but deny any command that attempts to use curl (preventing data exfiltration). This can be done by editing the JSON settings. For instance, adding the following to your ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.036.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.037.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.038.png) settings.json will permanently allow the npm run lint and npm run test:\* commands and reading your shell config, but deny any Bash usage of curl : {![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.039.png) "permissions": { "allow": [ "Bash(npm run lint)", "Bash(npm run test:\*)", "Read(~/.zshrc)" ], "deny": [ "Bash(curl:\*)" ] } } In this JSON, we see how specific commands are identified (the format Bash(command) or![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.040.png)![ref1]![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.042.png) Bash(prefix\*) for wildcards, and Read(path) for file reads). With this config, Claude won’t even ask for approval to run tests or lint (it’s pre-allowed), but if it ever tries to execute a curl command, it will be blocked without bothering the user. Fine-tuning these settings can greatly streamline your workflow while maintaining safety. 2. **Project Memory ( CLAUDE.md):** Claude Code uses special markdown files to persist project knowledge and personal preferences. The most important is ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.043.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.044.png) CLAUDE.md in the project root, which serves as *project- specific memory*. Whenever you start a session in a project, Claude automatically loads the content of ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.045.png)CLAUDE.md into context (as part of its initial prompt). This file is your place to write down key details that Claude should always remember about the project. Good things to include are: - **Project Overview:** A short description of the project’s purpose and architecture.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.046.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.047.png) - **Common Commands:** e.g., how to build, test, run the project ( make build , pytest , etc.). - **Code Style Guidelines:** Preferred coding patterns, naming conventions, or frameworks to use.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.048.png) - **Important Modules/Files:** Pointers to core parts of the codebase (e.g., “ auth.py handles user authentication”) to help Claude find relevant code. - **Project Conventions:** Branch naming rules, commit message style, deployment procedures – any conventions you want the AI to follow. - **Known Issues or Warnings:** For example, “Library X has a known bug in version Y – avoid that function” or “We use a custom patched version of Z; see patch in ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.049.png) patches/ folder.” There is no strict format for CLAUDE.md – it’s for your benefit and Claude’s context. Clarity and brevity are valuable; a concise list of bullet points or short sections is recommended. By maintaining this file, you equip Claude with the context it might not otherwise infer from code alone. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.050.png)*Tip:* You can use Markdown headings and bullet lists in ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.051.png) CLAUDE.md – Claude will parse it as instructions or reference info. If you ran /init as suggested in Section 2, Claude already created an initial CLAUDE.md . Open it and refine it with the information mentioned above. For example, you might add:![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.052.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.053.png) - **Project Overview![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.054.png)** This is a CLI tool to process financial data and generate reports. It reads CSV files, applies business rules, and outputs aggregated statistics. - **Common Commands** - `make test` – run the test suite (uses pytest) - `make lint` – run flake8 and mypy linters - **Code Style** - Follow PEP8 style guidelines. - Use type hints in all new functions. - Prefer f-strings for string formatting. - **Architecture Notes** - `data\_ingest.py` – handles reading and validating input files. - `report\_gen.py` – contains logic to compute report metrics. And so on. Save this file in the repository. Now, every time you launch Claude in this directory, it will read these notes, so you don’t have to repeat such details in prompts. In addition to project memory, Claude Code supports a *global user memory* file: ~/.claude/CLAUDE.md ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.055.png)for personal preferences or tips you want across all projects. For instance, you might note “I prefer using![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.056.png) requests library for HTTP in Python” or your favorite code style quirks. This user CLAUDE.md is automatically loaded too, but it’s not shared with others and can import your personal notes. **Memory Imports:** A powerful feature is that CLAUDE.md supports imports of other files using an @ ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.057.png)syntax. For example, in CLAUDE.md you could write: See @README.md for project overview and @docs/style-guide.md for coding standards. Claude Code will replace those with the contents of the referenced files when initializing context. This prevents duplication of information. It also allows each developer to have private additions: e.g., ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.058.png) - @~/.claude/my-project-instructions.md can import a user-specific file (like your own notes) without sharing them in the repo. Note that imports won’t be processed if written inside code blocks or code spans in the markdown – they only work in normal text. 3. **Model and Platform Configuration:** By default, Claude Code will use the Anthropic API with the Claude model associated to your account (Claude 4 for paid plans, possibly Claude 3.7 for free tier, etc.). In enterprise settings or by preference, you can configure it to use different model endpoints or versions: - To explicitly choose the model, use the --model flag or set the environment variable ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.059.png)ANTHROPIC\_MODEL. For example: claude --model claude-opus-4 or in settings JSON set "model": "claude-sonnet-4-20250514" to pin to a specific model release. Claude Code’s defaults as of May 2025 are claude-sonnet-4-20250514 for most users (Anthropic console default) and it will switch to claude-opus-4-20250514 for Claude Max users when extended tasks demand it. You can override this if you have access and want Opus 4 always, or even use older models if needed. - If using **Amazon Bedrock** or **Google Vertex AI**, you must set some environment flags. For Bedrock, set ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.060.png) CLAUDE\_CODE\_USE\_BEDROCK=1 and ensure your AWS credentials are configured so Claude Code can call Bedrock’s endpoints. You’ll also need to specify the model ARN or Bedrock model ID in ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.061.png)ANTHROPIC\_MODEL (since Bedrock’s Claude models have specific IDs). Similarly for Vertex, set CLAUDE\_CODE\_USE\_VERTEX=1 (and GCP credentials) – Vertex AI’s Claude uses the same model IDs as Anthropic’s API for Sonnet 4 by default. Enterprise documentation from Anthropic covers the exact steps, but in short: you configure environment variables or settings.json to point to the alternate provider, and Claude Code’s SDK calls will route through those services. - **Proxies:** If your organization uses a proxy or an intermediate service (like *LiteLLM* or a logging proxy) to access Anthropic, Claude Code can be pointed to a custom API URL via environment variables. For example, ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.062.png) ANTHROPIC\_API\_HOST or ANTHROPIC\_BEDROCK\_BASE\_URL can be set to a proxy address[ 5 ](https://docs.anthropic.com/en/docs/claude-code/bedrock-vertex-proxies#:~:text=). There is also a CLAUDE\_CODE\_SKIP\_BEDROCK\_AUTH=1 flag for cases where you channel requests through a proxy that handles auth, bypassing Claude Code’s own credential requirement. Always refer to the latest Anthropic docs for these enterprise configs, as they may update frequently. With installation complete, authentication done, and your settings and memory configured, Claude Code is ready to work on your project. In the next section, we’ll cover how to **invoke** the agent in different ways (interactive REPL vs one-shot commands) and how to interact with it effectively on the command line. 4. **CLI Invocation Modes: Interactive vs. Headless** Claude Code’s CLI provides multiple invocation modes to suit different tasks: an **interactive REPL** for conversational development and a **headless (one-shot) mode** for scripting and automation. Knowing how to use both is essential for integrating the agent into your workflow. 1. **Interactive REPL Mode:** The default usage is an interactive session where you can have a back-and- forth conversation with Claude, similar to a chat interface but in your terminal. To start it, simply run the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.063.png)claude command with no arguments in your project directory. You’ll see a prompt (usually prefixed by something like ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.064.png) > ) where you can type natural language instructions or questions. Claude will stream its responses in the terminal. - *Providing an initial prompt:* You can also start the REPL with an initial query by typing it after the command. For example: claude "summarize this project’s architecture"![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.065.png) This launches Claude, it reads context (like CLAUDE.md and key files) and immediately answers the query. After that, you remain in interactive mode for follow-up questions. This is handy to save one step when you know the first thing you want.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.066.png) - *Continuing sessions:* Claude Code automatically saves recent conversation context (usually within the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.067.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.068.png).claude/ directory). You can exit the REPL (by pressing Ctrl+D or typing /exit ) and later resume. Use ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.069.png) claude --continue to continue the most recent session. If you have multiple sessions or want to choose, claude --resume will list recent sessions for you to pick from. Each session has an ID (which you may see in logs); you can also do claude -r "" to resume a specific one. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.070.png) - *In-REPL commands:* Within the interactive prompt, commands prefixed by a slash / are interpreted![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.071.png) as control commands, not sent to Claude’s model. We already encountered /init and /config . Others include ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.072.png) /clear (to clear Claude’s context – effectively resetting conversation while staying in the same session; more in Section 6) and ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.073.png) /exit to quit. You can type /help to see available commands. **Usage Example:** Once in the REPL, you might start by asking something like: - How should we design the data ingestion module for this project?![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.074.png) Claude might respond with a plan or suggestions, possibly after using its tools to scan files or search the web if needed. Then you can continue with new instructions based on its answer. 2. **One-shot (Headless) Mode:** Sometimes you want Claude to do a single task and then exit, especially in non-interactive contexts (scripts, CI jobs) or just to quickly get an answer in the terminal. The ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.075.png) -p (prompt) flag allows this. For example: claude -p "generate a requirements.txt for this project"![ref2] Claude will output the result and then the process ends (no persistent session). This mode is useful for quick queries or integrating Claude Code into other automation. One-shot mode also allows piping data in via standard input. For instance, if you have a log file and want Claude to analyze it: cat error.log | claude -p "find the root cause of these errors" ![ref2]Claude will take the piped content as context (accessible via its tools) and answer accordingly. Additionally, for automation, Claude Code offers a **“headless mode”** with machine-readable output. By adding ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.077.png) --output-format flag, you can get JSON output (either all at once or streaming). For example: claude -p "Generate a JSON schema for a user profile" --output-format json![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.078.png) Claude will output a JSON object containing the result and some metadata. With --output-format stream-json![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.079.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.080.png) , it will stream tokens as JSON messages (useful for long responses). This is valuable for CI pipelines or tools that want to post-process Claude’s response. 3. **Examples of CLI Commands:** Here’s a quick reference table of common ways to invoke Claude Code from the terminal (as documented): - claude – Start interactive mode in current project. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.081.png) - claude "your prompt" – Interactive mode, but starts with an initial question or task. - claude -p "your prompt" – One-shot mode: prints answer and exits. - echo "text" | claude -p "instruction" – Pipe input to Claude with a prompt (one-shot). - claude -c – Continue the last conversation interactively. - claude -c -p "prompt" – Continue last conversation but just print output for that prompt (non- interactive continuation). ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.082.png) - claude -r "session-id" "prompt" – Resume a specific past session and ask a question in one go. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.083.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.084.png) - claude config ... – Shortcut to manage configuration (e.g., claude config set theme dark ). - claude update – Update Claude Code CLI to the latest version. - claude mcp ... – Manage MCP servers (for tool extensions; see Section 19). These flexible invocation methods let you use Claude Code both as an interactive assistant and a backend service for automation. *Figure: Claude Code CLI in interactive action – here the developer asked Claude to commit changes, and Claude is running ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.085.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.086.png)* git status *,* git diff *, and preparing a commit message based on the diff (screenshot from Anthropic, 2025).* 4. **Editor/IDE Usage (FYI):** While not the focus of this CLI-centric guide, note that if you prefer an IDE, Claude Code can operate there too via official plugins. In VS Code or JetBrains IDEs, Claude can be summoned in a panel and will apply edits directly to open files. The core concepts (prompts, tools) remain the same. Likewise, GitHub integration (Claude Code GitHub Action) allows tagging @claude in pull requests to have it provide reviews or follow instructions in CI. These are essentially alternate frontends to the same agent. Having covered how to invoke Claude Code, we will next explore the tools at its disposal and how the permission system keeps its actions in check. 5. **Built-in Tools and Permission Management** One of Claude Code’s distinguishing features is its ability to perform actions through **tools**. Instead of just passively suggesting text, Claude can call upon a suite of tools to read files, execute code, search the web, etc., enabling it to act as an autonomous coding agent. However, with great power comes the need for control: a **permission system** governs tool usage to ensure the AI doesn’t do anything destructive or undesired without approval. 1. **Toolset Overview:** Claude Code has a predefined set of tools (actions) it can use during a session. These include: - **Read:** View the contents of a file. Claude uses this to fetch code from your repository when answering questions or making edits. (No permission required – reading files is generally safe). - **Write:** Create a new file or overwrite an existing file with new content. (Requires permission – because it modifies the filesystem). - **Edit:** Make targeted edits to an existing file (inserting, modifying, or deleting specific lines). This is often used for surgical code changes. (Requires permission). - **MultiEdit:** Perform multiple edits in one tool call, applied atomically to a file. (Requires permission; used for batch updates). ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.087.png) - **LS (List):** List directory contents (basically ls command). (No permission needed just to read directory structure). - **Grep:** Search for a pattern in files (like using grep to find text). (No permission needed for read-only search). ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.088.png) - **Glob:** Find files by pattern (e.g., get all \*.py files). (No permission needed). - **Bash:** Execute an arbitrary shell command. This is extremely powerful – it could run tests ( pytest ), start a server, install packages, or potentially do harm (e.g. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.089.png) rm -rf / ). (Requires **explicit permission![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.090.png)** each time by default). - **WebSearch:** Perform an internet search query. Claude uses an Anthropic-provided search API with domain filters to get information from the web. (Requires permission). - **WebFetch:** Fetch the content from a given URL (like downloading a webpage or raw data). Often used after a WebSearch to retrieve a page. (Requires permission). - **NotebookRead / NotebookEdit:** Specialized tools to read or modify Jupyter notebooks (treating them as segmented into cells). (Read doesn’t need permission; Edit does). - **TodoRead / TodoWrite:** Tools for managing a to-do list of tasks in the session. Claude sometimes uses these to keep track of sub-tasks. (These operate on an internal list; typically no permission needed for reading the list, and writing might be internal). - **Agent (Sub-agent):** This “tool” allows Claude to spawn a sub-agent (another instance of the model) to handle a sub-task. (No permission needed for that, since it’s just using more of the model). We will discuss sub-agents more in Section 7. This toolset enables Claude to do things like: open a file, read it, decide what changes are needed, open an editor to apply a patch, run tests to verify, search online for an error message, and so on – forming an autonomous loop towards accomplishing a goal. 2. **Permission System:** Claude Code uses a tiered permission model to ensure safety. The default rules are: - *Read-Only Operations:* (Reading files, listing directories, searching text) do **not** require user approval. Claude can browse your code freely. This is convenient – you wouldn’t want to approve every file read. It’s deemed safe since it’s not altering anything. - *Write/Modify Operations:* (Writing or editing files) require one-time approval per session. By default, the first time Claude tries to edit a file in a session, it will ask “Allow Claude to edit files X, Y? (y/n/yes to all)”. If you approve, it won’t ask again for subsequent edits to files in that session (unless you chose a “just once” option). The idea is you stay in control of file changes. - *Shell Commands:* (Bash tool) require approval **each time**, unless whitelisted in settings. When Claude proposes to run a shell command, you’ll see it (in the CLI output, often in a different color or italic text) and you must confirm. You can approve once or choose “Yes, don’t ask again for this exact command in this directory”. For example, you might permanently allow ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.091.png) pytest to run, but you would scrutinize something like ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.092.png) rm or external calls. The CLI will remember approvals per project as well if you allow permanently. - *Internet Access:* Both WebSearch and WebFetch need permission. Typically, on the first attempt to use them, Claude Code will prompt “Allow internet access to domain X?” and you can say yes/no (and yes always for that domain if you trust it)[ 6 ](https://www.anthropic.com/engineering/claude-code-best-practices#:~:text=d). This prevents the AI from arbitrarily sending data out or fetching untrusted info without you knowing.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.093.png) - *Sensitive Commands:* Certain potentially dangerous actions (like installing packages, running database migrations, etc.) might also trigger extra warnings or be blocked by default policy. You can override by explicit allow rules if needed. Importantly, you can always manually intervene. If Claude attempts to do something unintended, you can simply respond “no” to the permission prompt. If it’s stuck or not proceeding without permission, you can redirect it or use the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.094.png) /allowed-tools command to adjust settings on the fly. **Security Safeguards:** This permission mechanism, combined with Claude’s internal checks, mitigates risks of things like prompt injections or the AI going rogue. Claude Code is designed to detect if some instruction in a prompt might be malicious (e.g., if an external webpage tries to trick it into revealing your files) and will refuse or seek confirmation. The user’s role is to supervise the critical steps – the agent will not, for instance, deploy your app to an unknown server or delete files without your consent (unless you explicitly disabled permissions, which we *do not* recommend except in safe sandbox scenarios). 3. **Approving and Denying Actions:** In practice, when Claude is working on a task, you will see italicized lines such as: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.095.png)*Claude: (running tests...)* or a prompt [user@host]$ pytest indicating it wants to execute that command. The CLI will then ask for input ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.096.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.097.png) [Allow/deny] . You can type y or n (or just press Enter for a default, which is usually “deny” to be safe). If you trust a command, you can type ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.098.png) yes followed by ! ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.099.png)(e.g., yes! ) to always allow that exact action going forward in the session. You can also allow all actions temporarily with a special flag (discussed as "YOLO mode" in Section 7, which should only be used in disposable environments due to risk). For example, if you instructed Claude to “run the test suite and fix any failures,” you might see something like: *Claude: I will run the test suite to see failures.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.100.png)* *(Claude executes: pytest)* – CLI prompts: Allow Claude to run "pytest"? [y/n]![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.101.png) You type y (approve). Tests run and output streams... then Claude identifies a failure and decides to edit a file. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.102.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.103.png) *(Claude proposes to edit* utils.py *)* – CLI: Allow Claude to edit file "utils.py"? [y/n] You can open a diff preview (Claude usually shows the diff it wants to apply in its response) and then type![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.104.png) y . Claude applies the patch. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.105.png) *(Claude finishes editing)* – Now maybe it wants to run tests again. If it’s the same pytest command and you had said “yes always” earlier, it wouldn’t ask again. Otherwise, you approve again. This interactive approval loop ensures you have oversight. It is one of the main differences between using Claude Code and, say, an unconstrained script – you are kept in the loop for any impactful actions. 4. **Best Practices for Permissions:** At the start of using Claude Code on a new project, it’s useful to proactively allow some safe, repetitive actions. For example, running tests and linters frequently: you might add those to your ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.106.png) settings.json allow list (as shown earlier) or simply respond “Yes, don’t ask again” on the first run. Conversely, for anything that could be risky (deploy commands, file deletions, network calls), let the prompts come each time so you can verify context. If you ever accidentally allowed something and want to revoke it, you can edit the ~/.claude/ permissions![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.107.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.108.png) file or relevant settings, or just restart the session (approvals reset per session unless you globally allowed in config). The ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.109.png) /allowed-tools command in the REPL can also list current allow/deny rules and let you update them on the fly. In summary, Claude’s tools make it extremely capable, and the permission system + config allow you to strike a balance between automation and safety. Next, we’ll look at how Claude actually uses these tools in the context of reasoning, especially with the “extended thinking” mode that lets it chain multiple tool uses for complex tasks. 6. **Extended Reasoning and “Thinking” Mode** For simple queries, Claude Code can respond almost instantly using the model’s standard capabilities. However, complex software tasks often benefit from deeper reasoning – considering multiple approaches, exploring the codebase, and planning steps. Claude 4 introduces an **extended reasoning mode** (sometimes called “extended thinking”) where the model is allowed to spend more time deliberating and can intermix tool usage during that process[ 7 ](https://techcrunch.com/2025/05/22/anthropics-new-claude-4-ai-models-can-reason-over-many-steps/#:~:text=Both%20Opus%204%20and%20Sonnet,a%20given%20problem%20before%20answering). As a user, you can explicitly trigger this mode with certain prompt keywords, and you will see Claude “thinking” with an output of its thought process.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.110.png) 1. **What is Extended Thinking?** Normally, an AI model responds within a fixed computational budget, but Claude’s extended mode gives it a larger token budget (up to 64k tokens in Claude Opus 4’s case) and more internal “steps” to reason. In CLI terms, when extended thinking is active, Claude might print interim thoughts in *italic gray text* (this is configurable) as it works through a problem. These aren’t final answers but notes like “*I will search for where this function is defined…*”. It’s a peek into the agent’s decision-making. During extended reasoning, Claude can call tools in between thought steps – e.g., do a web search, read a file, run a snippet of code – then incorporate the results into its final answer. This ability to alternate reasoning and tool use is crucial for solving multi-faceted tasks. 2. **Triggering Deep “Think” Mode:** You can encourage Claude to engage extended thinking by explicitly asking it to “think” or “think hard” about a task. In fact, Anthropic has mapped certain phrases to allocate more reasoning time: - “think” – triggers some extended thinking. - “think hard” – more extended. - “think harder” – even more. - “ultrathink” – maximum extended reasoning. For example, if you have a complicated design problem, you might prompt:![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.111.png) - Think hard about how we should architect the new plugin system, considering scalability and security. Claude will then enter a more contemplative mode. The CLI will indicate this (often with a message that it’s using extended thinking). Claude might output a series of italicized thoughts or a progress indicator, and it may perform tool actions like reading multiple files or searching for design references, before giving you a structured and well-considered answer. The difference between a normal answer and an extended one can be significant in quality. For example, a normal prompt “How to design X?” might yield a decent but surface-level answer in 10 seconds. “Think harder about how to design X” could take 30-60 seconds but yield a more comprehensive plan that weighs alternatives, as Claude had time to simulate a deeper brainstorming (potentially even spawning sub-agents to examine sub-problems as needed). 3. **Using Sub-agents for Verification:** One fascinating aspect of Claude Code’s agentic behavior is the ability to use the **Agent** tool (spawn sub-agents) to tackle sub-tasks concurrently. Essentially, Claude can spin up another instance of itself (often using a smaller model or the same model in parallel) with a specific query. This might happen implicitly during extended thinking if it wants to verify something independently. For example, while planning, Claude could use a sub-agent to check “Does module A already have function B? If not, maybe we need to create it.” The sub-agent would run, answer that question (by reading code), and then main Claude incorporates that info. As a user, you can also explicitly instruct Claude to use sub-agents. You might say: “Use sub-agents to double-check the critical parts of this plan” – though often just phrasing tasks in a way that implies verification will prompt Claude to do it. According to Anthropic’s guidance, using sub-agents early for complex problems helps preserve the main context and offload some memory usage. Sub-agents have their own isolated context window and can search files independently, then return results to the main agent. From the user perspective, you’ll notice sub-agent activity in the thought output, usually indicated by Claude saying it’s using an “agent” or you might see multiple tasks running in parallel (if the CLI indicates parallel tool use). This advanced feature largely operates under the hood, but it’s useful to be aware that Claude can effectively “think concurrently” by splitting tasks. 4. **Planning Before Coding:** A major best practice with Claude Code is to have it outline a plan in natural language *before* diving into writing code. Because the model can rush to coding (especially if not explicitly told otherwise), you should prompt it to formulate a plan when tackling anything non-trivial. For example: User: We need to implement a caching mechanism for user data. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.112.png) Claude: (might start coding immediately by default) User: Actually, think step-by-step. First, plan the approach, then we’ll implement. By doing this, you harness the model’s ability to do higher-level reasoning. Claude might respond with a numbered list of steps or design considerations: e.g., “1. Decide where to store cache (in-memory vs on disk). 2. Determine invalidation strategy. 3. Implement a Cache class... etc.” You can then review this and discuss or refine the plan. This upfront thinking often saves time by preventing false starts. As the Anthropic team notes, *“Steps #1-#2 are crucial—without them, Claude tends to jump straight to coding... asking Claude to research and plan first significantly improves performance for problems requiring deeper thinking upfront.”*. Encourage Claude’s planning by saying things like: *“Don’t write code yet. Consider what needs to be done and propose a plan.”* – The model will oblige with a thoughtful analysis. You can even ask for multiple options or have it debate pros/cons (Claude is adept at evaluating alternatives when asked). 5. **Viewing and Adjusting Thoughts:** By default, the CLI shows Claude’s *italic gray thoughts* when it’s in reasoning mode. If you find this verbose or distracting, you can toggle the verbosity or theme in settings (for instance, setting a less conspicuous color). However, many developers appreciate seeing the thought process as it provides transparency – you can catch if the agent is going down a wrong path and intervene sooner. For instance, if you see in its thoughts “*I will delete all log files to solve disk space issue*” (hypothetically), you could stop it right there. If needed, you can interrupt extended thinking by pressing Esc . Pressing Esc once typically interrupts the current action but preserves context for you to issue a correction![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.113.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.114.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.115.png)[ 8 ](https://www.anthropic.com/engineering/claude-code-best-practices#:~:text=,to%20take%20a%20different%20approach). Pressing Esc twice quickly triggers a special mode where you can actually edit your last prompt and re-submit it (Claude will roll back to that point)[ 9 ](https://www.anthropic.com/engineering/claude-code-best-practices#:~:text=,to%20take%20a%20different%20approach). These are powerful “course-correction” features: you might realize mid-answer that your question was ambiguous or leading Claude astray; with double-Escape, you can fix your question rather than starting over completely.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.116.png) In summary, extended reasoning mode is like giving Claude Code a longer attention span and a toolkit to thoroughly investigate a problem space. Use prompts like “think deeply” to activate it for design discussions, architecture decisions, or any complex logic. Coupled with sub-agents for verification and your own ability to guide or interrupt, this mode can significantly improve the quality of outcomes for hard problems. Now, with a grasp on how to get Claude to *reason* and plan, we are ready to embark on the actual end-to- end workflow of a project, starting from ideation. 7. **Task Chaining and Modular Workflows** Building a full software project is a complex undertaking that can’t be solved in one prompt. Claude Code is designed to handle **multi-step workflows** and even parallelize tasks when possible. In this section, we’ll discuss strategies for chaining tasks together and dividing work into modules, both sequentially and concurrently, using Claude Code. 1. **Step-by-Step Task Lists (To-Do Lists):** For lengthy tasks with many sub-tasks (e.g., “migrate our entire codebase to a new framework” or “fix 50 linter errors”), it’s wise to have Claude break down the work. One method is using a *to-do list or scratchpad file* as an anchor for progress. You can ask Claude to create a Markdown checklist of steps and then execute them one by one. In practice: - Tell Claude something like: *“List all the things we need to do to achieve X. Make it a checklist in a file Todo.md.”* Claude will perhaps use its TodoWrite tool or just create a new file with checkboxes. - Then you can instruct: *“Now work through the checklist in Todo.md step by step, checking off each item after completion.”* - Claude can then systematically focus on one sub-task at a time, mark it done (perhaps editing the file to put an “x” in the checkbox) and move on. This ensures thoroughness and helps when there are many small fixes – you and the AI have a shared external plan. For example, say you have dozens of flake8 errors across files. Instead of “fix all lint errors” in one go (which Claude might attempt but could get overwhelmed), have it compile an itemized list: “- [ ] Fix unused import in file1.py line 10; - [ ] Fix naming convention in file2.py function foo\_bar,” etc. Then proceed sequentially. This approach is more transparent and debuggable if something goes wrong on item 7 of 50, you know where to continue. 2. **Modularizing by Feature or Component:** When developing multiple features, you don’t have to do them strictly one after another; Claude Code can handle multiple sessions for isolated features. A recommended practice at Anthropic is to use **multiple git worktrees or checkouts** for parallel tasks. Here’s how that works: - Create separate branches (or worktrees) for different features or fixes you’re working on concurrently. For instance, branch “feature-a” for a new UI component, and “refactor-auth” for cleaning up authentication code. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.117.png) - Open separate terminal windows/tabs, each cd into a different worktree/branch directory. Launch ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.118.png)claude in each. - Now you essentially have two Claude Code instances running in parallel, each focusing on a specific subset of the project. They won’t interfere because one’s context is the “feature-a” code and the other is “refactor-auth” code. You can switch between them, letting Claude work in one while you review the other. - This is especially useful if one task is waiting on a slow process (like a long test run or an external API call) – you can approve actions in the other session in the meantime. Using multiple instances like this mirrors how a team of developers might work on different modules at once. Since each Claude has its own memory and context limited to the branch directory, they won’t confuse tasks or mix up goals. If you prefer not to set up multiple checkouts, you can still do a form of multi-tasking by using the /clear ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.119.png)command to context-switch within one session sequentially (clear context to start a new task, then maybe come back). But truly parallel sessions as described are cleaner for isolation. 3. **Multi-Claude Collaboration:** Another advanced technique is deliberately using two Claude instances to collaborate, as if they were two developers reviewing each other’s work. For example: 1. Use the first Claude (Instance A) to generate code for a feature. 1. Take the output or the committed changes from A, and in another terminal, ask a second Claude (Instance B) to review that code critically or write tests for it. 1. If B finds issues or suggests improvements, you can feed those back into A (or start a third clean session C to integrate the feedback). 1. Continue this ping-pong until the code is robust. This method leverages the fact that each instance might pick up on different aspects – one might be better at generation, another at critique. It’s akin to pair programming with an AI pair. The best practices note that *“having separate context is beneficial… This separation often yields better results than having a single Claude handle everything.”*. For example, in TDD (Test-Driven Development), you could have one Claude exclusively write tests, while another exclusively writes code to pass those tests. The test-writer might think of edge cases the code- writer didn’t, and you as the human mediator ensure both are consistent. 4. **Chaining Workflows in CI:** Claude Code also supports **headless automation** where you chain tasks programmatically. Suppose you have a migration script to apply to hundreds of files; you can use a script or GitHub Actions workflow that calls Claude in a loop for each file (with the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.120.png) -p one-shot mode). Anthropic’s team describes two patterns: - **Fanning Out:** e.g., have Claude generate a list of tasks (like a list of files needing changes), then for each item, call ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.121.png) claude -p "perform X on item Y" in a script. By capturing Claude’s success/ failure output (perhaps instruct Claude to output a simple “OK” or “FAIL” for each task), the script can iterate until all tasks are done. This is useful for large-scale repetitive tasks (mass refactors). - **Pipelining:** Use Claude’s output as input to another command in a shell pipeline. For example, ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.122.png) claude -p "summarize this log in JSON" --output-format json | jq '.analysis' > report.json . Here Claude’s structured output flows into another tool. To facilitate chaining, Claude Code’s --verbose flag can be toggled; in scripts, you might turn it off to get clean outputs without the extra interactive prompts except those you intend. Also, ensuring certain tools are allowed (or disabled) can be crucial when running in CI; e.g., you might pre-set ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.123.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.124.png) --allowedTools option in headless calls to limit Claude to read/write and nothing else for safety. 5. **Scratchpad Files for Modular Work:** In addition to to-do lists, sometimes you may want Claude to use an external file as a “scratchpad” for reasoning – for instance, generating a design document or writing pseudocode outline first. You can instruct: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.125.png)*“Open a scratch file* design.md *and brainstorm the module design there.”* Claude will then treat that file as a workspace: writing to it, possibly revising it multiple times (using MultiEdit), and you can open that file to review the progress. Once satisfied, you tell Claude to proceed to code. This keeps the main conversation history cleaner and gives a persistent artifact of the planning stage, which is useful for documentation. In conclusion, Claude Code is not just a single-turn Q&A bot; it’s built to handle *workflows*. By breaking big jobs into smaller tasks, using to-do lists, or even running parallel agent sessions, you can significantly speed up development while maintaining clarity. Think of Claude as a team of junior developers at your command – you can assign them different tasks or have them check each other’s work. The next sections will walk through applying these practices to our Python project: from initial idea through coding, testing, and deployment. 8. **Prompt Engineering Strategies for Claude Code** Interacting effectively with Claude Code requires good **prompt engineering** – crafting your instructions and questions to guide the AI towards correct and efficient outcomes. Claude Code in 2025 is quite advanced and follows instructions well, but the way you phrase your requests can dramatically impact the results. In this section, we outline prompt strategies optimized for Claude’s behavior, with examples. 1. **Be Specific and Unambiguous:** The single most important rule is to clearly state what you want Claude to do. Vague prompts can lead to incomplete or incorrect outputs, whereas specific prompts yield better alignment. Consider these examples: - **Vague:** “Add tests for foo.py.” ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.126.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.127.png) **Specific:** “Write a new unit test function in foo\_test.py to cover the edge case of foo() when the user is not logged in. Use pytest style assertions. Avoid using any mocks.” - **Vague:** “Why is ExecutionFactory’s API weird?” ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.128.png) **Specific:** “Trace through the git history of the ExecutionFactory class to summarize how its API evolved. Explain the reasons behind any major design changes you find.” - **Vague:** “Add a calendar widget.” **Specific:** “Implement a new calendar widget component. First, check how existing UI widgets are structured (see ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.129.png) HotDogWidget.php for reference). The calendar widget should allow the user to pick a month and navigate years. Follow the same pattern as other widgets (MVC separation, i18n support). Do not introduce new dependencies; use existing libraries only.” In each case, the “Good” prompt provides concrete details and constraints. Claude doesn’t have to infer your intent – it’s spelled out. As Anthropic notes, *“Claude can infer intent, but it can't read minds. Specificity leads to better alignment with expectations.”*. So when prompting, mention file names, function names, desired behaviors, and any constraints or hints about how to solve it. 2. **Use Step-by-Step Instructions:** If a task involves multiple steps, you can include that structure in your prompt. For example: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.130.png)*“To add logging, 1) Identify where in* app.py *to insert log calls, 2) Use Python’s logging module to log user ID and timestamp on each API call, 3) Ensure logs are written to ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.131.png)* logs/app.log *. Please proceed with these steps.”* Breaking it down in the prompt helps Claude follow the intended order and not skip parts. Claude Code is particularly good at following numbered instructions or checklist-like prompts, because it can mirror that structure in its plan and execution. You could even format it as a checklist in markdown in your prompt – Claude will then often tick them off as it goes (though note, the actual check-off might not persist unless you ask it to record progress). 3. **Provide Context Within Prompts:** Even though Claude Code can fetch files itself, it can be helpful to quote small snippets in your prompt if you want Claude to focus on them. For instance, *“Here is the current function signature: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.132.png)* def process\_data(file: str) -> int: *. We need to change it to accept an optional logger. Modify this function accordingly.”* By giving that code snippet, you reduce ambiguity about which function or where to look. However, avoid overloading the prompt manually – you generally don’t need to copy entire files into the prompt like with some other AI coding tools, because Claude can read from the repository on demand. A better approach is to ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.133.png)*reference* the file or function by name as shown: “the function process\_data in data.py .” Claude will then likely open that file. This saves your prompt tokens and lets the agent use its tools.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.134.png) 4. **Use Special Directives for Claude Code:** Claude Code recognizes certain patterns or commands that can guide its behavior: - The /think or simply using the word “think” as mentioned triggers deeper analysis (Section 6). So if you anticipate a complex answer, literally say “Claude, think step-by-step about X.”![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.135.png) - The agent’s system prompt (hidden to you) already includes some instructions about being concise and such, but you can override that to a degree by saying “Provide reasoning” or “explain each step” if you want a verbose answer, or conversely “give the solution only, no extra commentary” if you want minimal output. - When generating code, you can request it in parts: e.g., “First, show me a function stub, then we will fill it in.” This can be useful if the code is long – you maintain control after seeing the outline. - If Claude’s answer is too verbose or going on a tangent, you can say something like, “Focus strictly on the implementation details next, and keep responses under 100 words.” Claude 4 models follow these style instructions well due to improved instruction following [10 ](https://techcrunch.com/2025/05/22/anthropics-new-claude-4-ai-models-can-reason-over-many-steps/#:~:text=instant%20responses%20and%20extended%20thinking,a%20given%20problem%20before%20answering).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.136.png) 5. **Leverage Markdown and Formatting:** Claude Code will often output in Markdown format by default (especially for code blocks). You can explicitly ask for certain formatting. For instance, “List the steps as a markdown numbered list.” Or “Put the code in a single markdown block with ```python syntax.” This can help when you plan to copy the output somewhere or use it in documentation. On the flip side, if you want plain text (say you intend to pipe the output to a file or script), use --output- format![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.137.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.138.png) or instruct “Output only the code, no markdown formatting.” In headless mode, you might combine this with the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.139.png) --output-format flag to get JSON. 6. **Iterative Prompting (Refinement):** It’s rarely one-and-done. Use Claude’s memory of the conversation to refine results. For example, after an initial code generation: “The function looks good. Now optimize it to run in O(n) time instead of O(n^2).” Because Claude can recall what it just wrote (it has the code in context from either its own memory or can re-read it), this kind of iterative improvement is effective. Or if tests fail: “The tests still fail for edge case X. Investigate why and fix it.” Claude will dutifully debug, essentially performing a trace or adding printouts if needed. If Claude misunderstood something, clarify in the next prompt rather than starting over from scratch. E.g., “I realize my previous instruction was unclear: we need a *yearly* summary, not monthly. Please adjust the code accordingly.” This way you correct course without losing progress. 7. **Encourage Self-Verification:** You can ask Claude to double-check its work as part of the prompt. For example: “Implement the function and then verify that it handles the example input correctly by running it.” Claude might then generate code and simulate a run (or actually execute if allowed) to show the output. This is useful to catch mistakes. Another phrasing: “After writing the code, think of at least one test case and show that the code passes it.” This uses the model’s reasoning to do a quick self-test. Claude’s sub-agent ability can be invoked by such prompts: it might spawn a sub-agent to run the function with an example, which increases confidence in the solution. In TDD context, we already have it run tests and iterate until passing (Section 11 covers this). 8. **Handling Ambiguity:** If you ask an open-ended question like “How should we implement authentication?”, Claude might need more context. If you sense it’s unsure, provide guidance. One technique is to present multiple options and ask Claude to evaluate them. E.g., “Which approach is better for caching: using Redis or an in-memory dictionary? Think through pros and cons of each for this project.” Claude will produce a comparative analysis. You can then decide or instruct which path to implement. Claude is less likely than earlier models to say “I cannot make decisions” – it will often pick one approach if forced. If you want a specific decision, state the criteria or go ahead and pick and then instruct to implement the chosen one. 9. **Use Domain Language:** Tailor your language to the domain when possible. For Python projects, use Python terminology. Instead of “make it faster,” say “optimize the algorithm (perhaps use a set for O(1) lookups instead of list scanning).” Claude has been trained on a lot of programming knowledge; mentioning known functions or patterns triggers that knowledge. For instance, saying “use a list comprehension” or “apply vectorization with NumPy for this part” guides Claude to produce idiomatic code in that style. Similarly, if your project uses certain libraries or frameworks, incorporate those terms in the prompt. “Use pandas to read the CSV” or “make it a Django management command.” This way you avoid generic answers and get solutions that fit your stack. 10. **Example-Driven Guidance:** If you want output in a particular format, giving a small example can help. For documentation generation, you might say “Document this function in Google style docstring. For example, ‘Args: ..., Returns: ...’ format as shown here: ...” and Claude will follow that format for the actual function. Or provide a short pseudo-code snippet “e.g., ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.140.png) # pseudocode: iterate over rows ... ” and ask to flesh it out. Claude Code was designed to be relatively “unopinionated” and flexible, meaning it doesn’t force a single workflow. This puts the onus on you to steer it appropriately with prompts. The good news is by 2025, these models are quite good at understanding natural language instructions in a coding context, so you often don’t need extremely elaborate prompts (like the system role messages one might use in GPT models) – a straightforward, specific instruction in plain English suffices, as long as it’s clear and contextual. Finally, remember that Claude Code automatically has a notion of the conversation’s history and the project’s files. So your prompt doesn’t have to repeat things already in context (don’t repeat the entire specification every time; just refer back to it). Use pronouns carefully though – e.g., “optimize it” should obviously follow a prompt that made “it” clear (such as “the sorting function”). With effective prompting covered, let’s proceed to the practical workflow. We will start our project from scratch with ideation and then code generation. 9. **Project Ideation and Requirements Gathering** Every successful software project starts with clear **requirements and design**. In this stage, we use Claude Code to brainstorm ideas, outline requirements, and establish a plan for our Python project before writing actual code. The goal is to leverage Claude as a partner in fleshing out what to build, not just how to build it. 1. **Describing the Vision:** Begin by explaining the project to Claude in broad strokes. Since we are starting from a blank repository, give context about the intended functionality, target users, and any constraints. For example: *“I want to create a Python CLI application that fetches weather data from an API and generates a weekly weather report for a given city. The report should include temperature trends and an ASCII bar chart. Let’s discuss the requirements and design.”* This initial description does a few things: - It tells Claude the domain (weather data, API usage, CLI output). - It hints at features (fetch data, produce report with a chart). - It invites Claude to discuss requirements. Claude might respond with clarifying questions or a summary. It could ask which weather API, what format for the chart, etc. This is good – answer these questions to refine the requirements. Essentially, you can have a *requirements Q&A session* with Claude: - Claude: *“Which weather API will we use (e.g., OpenWeatherMap)? Should the tool output to console or file?”* - You: *“Let’s use the OpenWeatherMap API. The CLI should print to console. Also, it should allow choosing units (Celsius/Fahrenheit).”* - Claude: *“Understood. So requirements: 1) Fetch data from OpenWeatherMap (probably via their HTTP API). 2) Parse into a weekly report. 3) CLI argument for city name and unit. 4) ASCII chart for temperatures.”* This iterative dialogue helps ensure nothing critical is overlooked. Claude Code is quite capable of serving as a **business analyst** in this sense, drawing on general knowledge to ask about edge cases. Encourage it by asking, *“What other requirements or edge cases should we consider?”* 2. **Documenting Requirements in CLAUDE.md or Other Files:** It’s useful to record the decisions somewhere persistent. You can either manually note them in ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.141.png) CLAUDE.md under a “Project Plan” section, or ask Claude to do it. For instance: *“Summarize these requirements into a file REQUIREMENTS.md.”* Claude will then create or update that file with a neat list of requirements. This could include: - Functional requirements (what the app should do). - Non-functional (performance, reliability, etc., if any). - Example usage (like a sample CLI command and output). Having this artifact is helpful as a reference for later development and testing. It’s essentially your spec. If Claude’s summary misses something, you can edit it or tell Claude to add it. 3. **High-Level Design Brainstorming:** With requirements in place, ask Claude to propose a high-level design or architecture. For a Python CLI, it might suggest: - Using the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.142.png) requests library to call the API. - Using ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.143.png) argparse for CLI argument parsing. - Data classes or structures to hold weather data. - Perhaps drawing the ASCII chart with simple loops or a small library. For example, prompt: *“How should we structure the application? What modules or functions will we need?”* Claude might respond with a plan: - A module for fetching API data (with a function![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.144.png) get\_weather\_data(city) ). - A module for processing and formatting the report (maybe ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.145.png)format\_report(data) ). - A main cli.py that uses argparse to handle input and output. This is effectively Claude helping to **scaffold the project architecture** in natural language. Discuss trade- offs: e.g., if it suggests writing an HTTP wrapper vs. using an existing SDK for the API, ask which is better given the scope. Claude can weigh options, e.g., reliability of an official SDK vs. simplicity of direct requests. Encourage Claude to “think” if needed: *“Think about the design and list potential modules or classes we should create.”* Using the *extended thinking* abilities here yields thorough results. You might get a list like: 1.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.146.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.147.png) weather\_api.py – handles API calls (with an API key maybe). 2. report\_generator.py – creates the ASCII chart and summary text. 3. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.148.png) cli.py – parses arguments and ties things together. 4. Possibly a config or constants file for API keys/URLs. Feel free to iterate on the design. If you have preferences (say, you want an object-oriented approach with a![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.149.png) WeatherReport class), mention that. *“I prefer an object-oriented approach, maybe a class to represent the WeatherReport. Adjust the design accordingly.”* Claude will adapt its plan: it might now propose a![ref1] WeatherReport class with methods to fetch and to print. All this ideation dialogue can happen within the interactive session. If it gets long, remember you can use / clear![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.150.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.151.png) after saving important points in a file, to refresh context. But usually, keeping the planning discussion in one session is fine as it’s all relevant context. 4. **External Research via Claude:** Claude Code can search the web for you if you need information while planning. For example, if neither you nor Claude recall the exact endpoint or JSON structure of the OpenWeatherMap API, you can say: *“Search the web for OpenWeatherMap current weather API format.”* If internet access is permitted, Claude will perform a ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.152.png) WebSearch and retrieve relevant documentation. It might pull in the API docs snippet showing how the JSON looks (temperature fields, etc.). This can be incredibly useful to ground the planning in reality. Always double-check such fetched info, but it saves you alt-tabbing to a browser. Similarly, if you’re brainstorming tech choices (“Should we use library X or Y?”), Claude can search for comparisons or the latest version. The extended knowledge cutoff is not a big issue if web search is available, since it can get up-to-date info on libraries or standards as of 2025. 5. **Defining Scope and Milestones:** If this project were larger, you might break the work into milestones or phases. Claude can help outline these. For instance: Phase 1 – basic data fetch and print text, Phase 2 – add chart, Phase 3 – packaging for PyPI, etc. Having a roadmap ensures both you and Claude keep track of what’s done and what’s next. You might even tag some items as “future enhancements” and explicitly tell Claude not to do them now. E.g., “We will not implement caching in this version, that’s out of scope.” By recording that in requirements, you prevent the AI from wandering into those tasks during coding. At this point, we have a solid shared understanding of *what* we’re building. We likely have one or more of: - A requirements list. - A design outline (possibly in ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.153.png) CLAUDE.md or a design doc). - Decisions on libraries and structure, cited from documentation. This sets the stage for coding. We will next proceed to actual code generation, leveraging the scaffolding patterns discussed in the design. 10. **Code Generation and Scaffolding Patterns** With requirements and a design in place, we now turn to **generating the code** for our Python project. Claude Code excels at producing boilerplate and implementation code, but to use it effectively, it helps to follow certain patterns: generating code in stages (scaffolding), verifying each part, and leveraging prompt techniques to structure the output. 1. **Creating the Project Scaffold:** If the project is blank, a first step might be to generate the basic file structure. You can instruct Claude to set up initial files. For example: *“Create a basic project structure with the modules and classes we discussed: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.154.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.155.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.156.png)* weather\_api.py *,* report\_generator.py *, and* cli.py *. Put a simple placeholder in each (like a function stub or class definition) so we can fill them in.”* Claude will then likely create those files one by one. It might output something like: - Creating![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.157.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.158.png) weather\_api.py with a stubbed function get\_weather\_data(city) . - Creating report\_generator.py with perhaps a placeholder generate\_report(data) function. - Creating cli.py with a main() that calls these (but maybe just a skeleton). It will ask permission to write these files (since new file creation triggers the Write tool). Approve each as needed. After this step, you have the skeleton of the project in code form. This is easier for Claude to work with than a completely blank state, because now it can open these files to implement details in subsequent steps. Alternatively, you could create empty files yourself (touch them in the OS) and then ask Claude to open/edit them. But letting Claude do it via its own internal plan is fine and saves keystrokes. 2. **Implementing Step by Step:** When generating significant code, it's beneficial to proceed in logical segments rather than all at once. For instance, you might first focus on the API fetching logic: *“Implement the![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.159.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.160.png)* get\_weather\_data(city) *function in* weather\_api.py *. It should call the OpenWeatherMap API (assume we have an API key constant), retrieve the next 7 days forecast, and return a structured data (like a list of daily temperatures and descriptions).”* Claude will then open weather\_api.py in its editor tool and fill in the function. It might also at this time ask to add an![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.161.png) API\_KEY constant or function parameters for units etc., depending on how the conversation went. It may even suggest installing ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.162.png) requests if needed, by adding an import at top. This is where the *agentic* part shines: Claude might attempt to run pip install requests via Bash if it doesn't find it, or at least mention you need it. If it tries to install and you allow it, it could modify your environment (or a virtualenv if you have one). For now, either pre-install requests yourself or allow it if you trust that command. Once get\_weather\_data is written, you should review it. Check if it matches the API docs (maybe Claude fetched them earlier). If something looks wrong or simplistic (maybe it didn’t handle errors or units), this is the time to refine: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.163.png)*“Add error handling: if the API response status is not 200, raise an exception.”* or *“The API returns temperatures in Kelvin by default; convert to Celsius or Fahrenheit based on a ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.164.png)* units *parameter.”* This iterative refinement ensures each piece is solid. Next, move to the report\_generator.py . Prompt: *“Implement* generate\_report(data) *which takes the data structure from get\_weather\_data and returns a nicely formatted multi-line string with the weekly report and an ASCII bar chart of temperatures.”![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.165.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.166.png)* This is a bit complex (formatting a chart), but see what Claude does. It might produce something: iterate through the data list, use a certain character (like![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.167.png) # ) repeated according to temperature values for bars, assemble strings. If the output doesn’t look ideal, you can ask to change the style. For example: *“Use '=' instead of '#' for the bar, and label each bar with the day of week.”* Claude can adjust accordingly. Because it’s writing code, you can even have it simulate the output. Maybe ask: *“Assume data for Mon-Sun of 10,12,... degrees, show me what the ASCII chart would look like with the current code.”* If allowed, Claude might run the code (or just mentally simulate) and print an example. If that example is off, you know to adjust the code. Continue to cli.py : likely this just glues things together. Tell Claude to use argparse for arguments (city name and units). Have it call the functions and print the result. Possibly also handle if an API key is needed, maybe instruct to read from an env var or config file.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.168.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.169.png) 3. **Ensuring Modularity:** One of the advantages of using Claude Code is that it can maintain context of the entire project, so it will generally write code that works together. Still, it’s good to remind or instruct it to keep modules decoupled. For example, ensure ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.170.png) weather\_api.py doesn’t do printing (that should be in ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.171.png)cli.py or report generator). If Claude mistakenly does something out of place, correct it: *“Don’t print inside get\_weather\_data; instead return the data and let the caller handle output.”* Claude will follow this instruction and adjust the code accordingly. Because you have the design outlined, you can use it to keep Claude on track: if it deviates, just point it back to the design (“Recall our design: the CLI is responsible for output, the API module just fetches data”). 4. **Handling Multiple Files and Refactoring:** As the codebase grows, you might need to update multiple files in tandem (e.g., adding a new feature that touches more than one module). Claude Code can handle multi-file edits in one go using its MultiEdit tool, but typically it will do one file at a time unless instructed otherwise. A strategy: - Perform changes in one file, then ask Claude to propagate any needed changes to others. For instance, if you change the data structure returned by![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.172.png) get\_weather\_data , say “Update report\_generator.py to work with the new data structure (which now includes humidity as well).” - Claude will open report\_generator and adapt it. If a refactor is more sweeping (rename a function, move something), you can explicitly ask Claude to search usage across the codebase and fix it.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.173.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.174.png) *“We renamed* generate\_report *to* format\_report *. Find all references to ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.175.png)* generate\_report *and update them.”* It might use grep under the hood to find references, then open each file to edit. This might produce several permission prompts, but you can allow them as needed. 5. **Verbalizing Constraints and Edge Cases:** While coding, mention edge cases to Claude so it accounts for them. For example: *“What if the API call fails or times out? Make sure to handle that by retrying once and then erroring out with a message.” “If the city is not found, the API returns a 404. Ensure our program catches that and prints 'City not found.' to the user instead of crashing.”* Claude can integrate these into the code. Often it will be proactive about some of these if the documentation hints at them, but it’s good to be explicit. Tests (coming next section) will also help catch these, but building robustness from the start saves iteration. 6. **Use of Comments and Docstrings:** You can ask Claude to add docstrings or comments for clarity. E.g., *“Add a docstring to the generate\_report function explaining its output format.”* Or simply, “Please include inline comments in the code for clarity.” The model is quite capable of writing descriptive comments as it codes. This can be useful for you and also to ensure it fully understands what it’s writing (sometimes explaining in comments solidifies the logic). Claude can also auto-generate documentation strings adhering to conventions. If you prefer Google style vs Sphinx, specify that. 7. **Reviewing Generated Code:** After significant sections of code are generated, do a quick review. Claude may not always be 100% correct or optimal. Look for: - Logical errors or missing branches. - Off-by- one errors in loops (for charts maybe). - Adherence to style (if you have a style guide in CLAUDE.md, check that). - Performance issues if any (though for small scripts, not a huge concern unless algorithmic). If you notice something, address it with Claude: “There’s a bug in the loop – it should iterate 7 days but your range is off, fix that.” or “Refactor this nested if into a simpler structure using a dict mapping.” Providing such direction will yield improved code quickly. At this point, you should have a initial working version of the core functionality. It’s a good time to run the code (if you trust it, or in a sandbox). You can either manually run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.176.png) python cli.py --city London -- units C to see if it works, or instruct Claude to run it via Bash: *“Run* python cli.py --city London ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.177.png)*to test it.”* Claude will use the Bash tool to execute the program (if allowed). It will then capture the output and show it to you. This is extremely handy: you can conduct an initial manual test within the Claude session itself. If something fails (perhaps you forgot to set ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.178.png) API\_KEY ), Claude will show the error. Then you can fix it: e.g., “Oh, we need to read API key from an env var, implement that in weather\_api.py.” 8. **Saving Progress:** At junctures, consider committing the code. You can ask Claude to create a git commit: *“Commit all changes with message 'Initial implementation of weather report generator.'”* Claude will use the Git tools to add/commit and craft a commit message. It often writes good commit messages that summarize changes, using context of the diff. Committing via Claude ensures if you mess something up later, you have a point to revert to. Also, it’s part of treating the process professionally – incremental commits. The code generation phase often interweaves with testing, which we’ll discuss next. Many developers prefer to write some tests first (TDD style) or concurrently, which Claude can also do. In our guide, we’ve done an initial code pass; now we proceed to validate it through testing. 11. **Testing and Validation (TDD with Claude Code)** Testing is a crucial part of moving from a working prototype to a **production-ready** release. Claude Code can significantly assist with writing and running tests, following the principles of Test-Driven Development (TDD) or simply adding tests after implementation. In this section, we’ll explore using Claude to create and run tests, and to iteratively refine the code until all tests pass. 1. **Writing Test Cases with Claude:** You can ask Claude Code to generate tests based on the project requirements and expected behaviors. For example: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.179.png)*“Write a unit test module* test\_report.py *for the report generation. Include tests for: correct ASCII output for a known small data set, handling empty data input, and ensuring the output contains the city name.”* Claude will then create test\_report.py (likely using a testing framework like pytest by default, as it’s common in Python). It will write test functions such as ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.180.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.181.png) test\_ascii\_chart\_output() with some dummy data and expected string snippet. You should verify these expected values logically. If needed, provide them explicitly: *“Use the following sample data for tests: data = [ {'day':'Mon','temp':20}, ... ]; the expected chart for Mon should be 'Mon: ====================' (20 '=' characters).”* Supplying the expected output often helps ensure tests are correct. Claude might either literally produce the known correct output or at least a pattern it thinks is correct. There’s a chance it might guess incorrectly how many ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.182.png) = characters correspond to temperature units (depending on how you coded it). If so, we’ll catch that when running tests. Additionally, have it write tests for the API function in isolation by mocking the HTTP call (or better, by using a sample JSON). For instance: *“Write tests for get\_weather\_data: simulate a sample API JSON response and test that the function returns the expected data structure, and test that it raises an exception when a 404 is encountered.”* Claude might use unittest.mock or pytest’s monkeypatch to simulate the requests.get call returning a known JSON. It will include that JSON inline in the test likely. This is great because it anchors what shape of data we expect.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.183.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.184.png) 2. **Running Tests via Claude:** Once tests are written, we can instruct Claude to run them. E.g.: *“Run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.185.png)*pytest -q *to execute all tests.”* Claude will use the Bash tool to run pytest (if you have it installed in env). If not installed, it might attempt to run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.186.png) pip install pytest if clever, or you handle that. When tests run, we’ll see output. Suppose some tests fail (very likely on first go). The output will show assertion errors or exceptions. Claude will capture that and present it to you. For instance, test might fail saying "Expected 'Mon: ===' but got 'Mon: ====' or something. Or maybe an exception "City not found exception not raised" if logic missing. 3. **Iterative Fixes Until Green:** This is where Claude Code shines by automating the *fix-run cycle*. You can instruct Claude to fix the issues: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.187.png)*“The test* test\_ascii\_chart\_output *failed because the bar length is off by one. Adjust the code in report\_generator.py to make the bar length exactly equal to the temperature value.”* Claude will edit report\_generator.py , adjusting maybe the loop or range. Then you can say: *“Run tests again.”![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.188.png)* It runs pytest again. Maybe now that test passes. Another test fails: e.g., "expected exception for 404 not raised." You then prompt: *“Add a check in get\_weather\_data: if response code is 404, raise a CityNotFoundError. Update tests accordingly if needed.”* Claude adds that. Also probably needs to define CityNotFoundError (either custom or reuse HTTPError). It will do so. It might also update the test to expect CityNotFoundError specifically instead of generic Exception, which is good. Run tests again. Continue this loop. **Claude will usually automatically continue running tests and fixing until all pass if instructed properly.** In fact, Anthropic mentions a workflow: *“Tell Claude to keep going until all tests pass”*. You can say: *“Keep iterating: run the tests and fix any failures, until everything passes.”* Claude may then do an autonomous loop: run tests, see failure, fix code, run again, etc., without needing your manual approval each time (except possibly tool permission if new). However, careful with letting it loop too freely; supervise the changes to ensure it doesn’t introduce new issues or take hacks to get tests green (rare but possible if test expectations were wrong, it might change tests instead of code logic if easier). Usually, if tests are well-specified, it's fine. 4. **Augmenting Tests:** If all tests pass too easily or quickly, consider if tests are thorough. You might ask Claude, *“Can you think of any edge cases we haven’t tested?”* It could suggest some. For example: input with negative temperatures (if any?), extremely large values, etc. Or concurrency if relevant. If plausible, have it add those tests too. This way, you validate not only the mainline scenario but also rare conditions. With the agent’s help, you might catch things like: - Very high temperatures causing wide bars (maybe out of console width?). - Special characters or Unicode handling if any (probably not here, but in other projects perhaps). - Multi-city handling if we extended to that. 5. **Test-Driven Development (TDD) Approach:** We generated implementation first then tests, which is common. Alternatively, you could have started with tests (TDD classic approach). Claude Code is capable of that: you could have first asked for tests given the spec, let them fail, then implement code to make them pass. In fact, Anthropi’s engineers enjoy that workflow because it gives a clear target. If doing TDD: - Write tests (Claude writes them based on requirements). - Run them (they will fail). - Then instruct Claude to implement the minimum code to pass the tests. Possibly doing one test at a time or all together. This can sometimes produce cleaner, simpler code, because the agent doesn’t over-engineer beyond what tests demand. Even without explicitly doing TDD, we essentially did a similar loop, just writing code first but still refining to meet tests. 6. **Static Analysis and Linters:** Alongside functional tests, run linters to catch style or simple bugs: *“Run flake8 and mypy on the code.”* If you have those installed, Claude will execute them. It may output warnings or errors (like unused imports, type issues if any). Claude can fix those too: *“Fix the flake8 warnings (line too long, etc.) and any mypy type issues.”* It will open files and adjust (split long lines, add type hints if necessary or adjust types). This ensures the code not only works but also meets style guidelines and type safety. Because we possibly instructed to use type hints, mypy might catch things like missing return type or mismatch. 7. **Human Review:** Even if tests pass and linter is clean, do a quick human sanity check: - Is the code logically correct and efficient enough? - Are there any obvious security concerns (like printing API key or not validating input lengths, etc.)? - Did we handle all scenarios specified in requirements? If something is off, address it: *“The ASCII chart doesn’t label the y-axis; although not required, perhaps add a simple temperature scale reference on top.”* This might be a new feature, but you can still add if desired. Or note it as a future improvement. 8. **Documentation Tests (if any):** If applicable, run any documentation or example tests. For instance, if we put example usage in README, ensure the output matches (some do documentation testing, but that’s more for libraries). Now we should have a robust set of automated tests giving confidence in correctness. This is key for a production-ready release. Our workflow with Claude Code encompassed: 1. Writing tests with Claude. 2. Running tests through Claude’s integration. 3. Fixing code repeatedly until tests are green. 4. Running static analysis and fixing issues. All done within the Claude Code environment – demonstrating how it can act as a full dev assistant, not just coding but verifying its own work. Next, we’ll integrate these steps into a continuous integration (CI) process and discuss deployment. 12. **Debugging, Linting, and Static Analysis** Even with tests in place, real-world software often surfaces bugs or issues that slip through. Claude Code can assist with **debugging** runtime errors and performing comprehensive static analysis to catch problems early. We touched on linting in the testing section; here we dive deeper into debugging workflows and tools for code quality. 1. **Debugging with Claude Code:** Suppose a user reports a bug in our application after release, or we notice something odd when running it manually. You can employ Claude to diagnose the problem. Strategies include: - **Reproduce and Log:** Ask Claude to run the program with certain inputs and capture logs. For example, ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.189.png)*“Run* python cli.py --city NonexistentCity *and show me the full trace/log.”* If an error occurs (like an uncaught exception), Claude will show the traceback. You can then copy that traceback into a prompt: *“We got this error ... what is the likely cause?”* Claude, having context of the code, will analyze and guess the cause. This is akin to pair-debugging where you show a colleague the error. - **Search Codebase for Clues:** You might suspect the bug is due to some function. Use Claude’s grep tool: *“Search for 'divide by zero' in the project”* or *“Open weather\_api.py and highlight where we handle HTTP errors.”* Claude can quickly navigate the code to find relevant spots. - **Add Debug Prints via Agent:** If needed, you can instruct Claude to temporarily insert debug print statements or logging in the code to gather more info, then run the code, then remove them. E.g., *“Insert a print in generate\_report to output the data it receives, then run the test that fails, then remove the print.”* Claude can do that fairly surgically using its edit tools. Because Claude maintains state and memory, it will remember the context of an ongoing bug investigation. It can chain the thought: error -> possible cause -> propose fix. Many times, if you simply present a stack trace or error message, the model will pinpoint the likely code issue and even propose a fix right away (especially for common errors). 2. **Using Linters and Code Analysis Tools:** We already used flake8 and mypy . Other tools could include: - ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.190.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.191.png)**Bandit** for security linting in Python (to catch insecure usage). - **pylint** for more comprehensive style checks. - **black** or **autopep8** for auto-formatting (Claude can effectively play the role of autopep8 by following style rules, but you might still run black for consistency). You can run these via Claude as well: *“Run bandit -r . to check security issues.”* If output appears, ask Claude to address each issue it flags. For example, if bandit says "Use of subprocess without shell=False", Claude can modify code accordingly and then cite that the warning is addressed. 3. **Type Checking and Documentation:** Use mypy (already done). If types are inconsistent, fix them. Claude can often infer correct type usage. After code changes, run mypy again to confirm no errors. Documentation can be validated too if using docstring linter or Sphinx build. You can ask Claude to build docs if you have doc config (though that might be beyond initial scope, it's possible in bigger projects). 4. **Interactive Debug Sessions:** Claude Code allows an interactive REPL with the program. You can drop into a Python shell in Claude: *“Start a python REPL and import our module to try something.”* It can run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.192.png)python -i or so. However, this is slightly meta – it's possible, but often it's easier to run small code via Claude's ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.193.png) bash tool directly or use the !python -c "import stuff; print(func())" technique. If something is truly perplexing, you might spawn an actual debugger (like pdb ). One way: *“Run the program under pdb and break at generate\_report.”![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.194.png)* This is advanced; not sure if Claude Code would seamlessly do a step-by-step debugging since that requires interactive stepping which might confuse the agent. Instead, rely on logging and mental reasoning from the code. 5. **Log Analysis:** If the application produces log files (maybe we instruct it to log to file), Claude can analyze logs too. For instance, if a production log shows an error stack, copy it in and ask: *“Analyze this log output and identify the error and possible cause.”* Claude will parse it and refer back to code to guess cause. This is similar to giving it a stack trace. 5. **Performance Profiling (Basic):** If performance is a concern (maybe not for our small app, but for others), you can ask Claude to suggest optimizations or analyze algorithm complexity. E.g., *“Our data list can be 100k items and generate\_report is slow. What part is O(n^2)?”* It might identify if building the string is the bottleneck and suggest using ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.195.png) join rather than incremental concatenation (just an example). You can then implement that fix with Claude’s help. 5. **Fixing Memory/Resource Issues:** Similarly, if there’s a memory leak or an open file handle left, Claude can track it down from description. For example: *“After running the program 1000 times in a loop, it crashes. Investigate possible resource leaks.”* Claude might inspect where we open network connections or files and see if something’s not closed properly. 5. **Using /undo or Version Control:** If a debugging attempt or fix goes wrong, recall you can revert to a previous state. If you committed changes, you can instruct Claude to checkout the previous commit: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.196.png)*“Revert to commit abc123 where tests were passing.”![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.197.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.198.png)* Claude will git reset or git checkout accordingly. Alternatively, its ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.199.png) undo command in CLI can revert last edit if not committed. This safety net is helpful – sometimes the agent might fix one thing but break another. Running full test suite after each fix helps catch that quickly. 5. **Continuous Code Quality Monitoring:** Beyond debugging current issues, set up a practice to run linting and tests regularly (which leads to CI in next section). Claude Code itself has a **monitoring usage** section in docs that tracks token usage and cost, which is more about API usage. But for code quality, you can integrate that into CI and we'll discuss soon. To recap, Claude Code can assist not only in writing code but also in diagnosing and fixing code issues. It’s like having a debugging partner who can quickly search the code and recall all context. By combining automated tools (tests, lint, type-checking) with Claude’s reasoning, we can maintain a high code quality bar. In the next section, we’ll incorporate these verification steps into a formal Continuous Integration pipeline and talk about preparing the code for deployment. 13. **Continuous Integration and Deployment (CI/CD)** Moving towards a production-ready release, we need to set up **Continuous Integration (CI)** to automatically run our tests and checks on every change, and prepare for **Continuous Deployment (CD)** (or at least deployment packaging). We will use Claude Code to help configure CI pipelines and deployment scripts in an infrastructure-agnostic way. 1. **Setting up CI with GitHub Actions (or similar):** Given our project is on Git (assume GitHub for concreteness), we can have Claude generate a GitHub Actions workflow YAML to run tests and linters on push. For example: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.200.png)*“Create a GitHub Actions workflow YAML file named* ci.yml *that runs on push: set up Python 3.9, install dependencies (requests, pytest, etc.), run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.201.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.202.png)* pytest *, run* flake8 *and* mypy *.”* Claude will produce the YAML content. It knows common patterns (like using actions/setup-python to get Python, caching pip, etc.). The output might look like: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.203.png) name: CI![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.204.png) on: [push, pull\_request] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - uses: actions/setup-python@v2 with: python-version: '3.9' - name: Install dependencies run: pip install -r requirements.txt - name: Run tests run: pytest -q - name: Run linters run: | flake8 . mypy . Claude might not automatically include every detail (like caching pip packages for speed) unless prompted. You can refine: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.205.png)*“Add caching for pip in the workflow.”* Claude will update YAML to use actions/cache with appropriate keys pointing to pip cache directories. Review the YAML for correctness (since a small indentation error can break it). It's often correct, but double- check things like actions versions and any needed ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.206.png) requirements.txt . If we didn’t have a requirements file, perhaps create one listing needed packages (Claude can infer from imports: requests, etc.). Ask: *“Generate a requirements.txt with all needed dependencies for this project.”* It may list: requests>=2.0 pytest>=7.0 flake8>=5.0 mypy>=0.9![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.207.png) Pin versions as desired. 2. **Self-Hosted or Alternative CI:** If not using GitHub, similar config can be done for GitLab CI or others. Claude can adapt to GitLab’s ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.208.png) .gitlab-ci.yml format if asked. 2. **Continuous Deployment Strategies:** Our app is a CLI tool. Deployment could mean: - Packaging it for distribution (like on PyPI). - Containerizing it into a Docker image for use in a larger system. - Or deploying it as a scheduled job on a server (e.g., a daily weather report email? But that’s beyond initial spec). Focus on packaging: *“Write a setup.py (or pyproject.toml) to package this tool for distribution.”* Claude might produce a![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.209.png) setup.py with metadata: name, version, description, dependencies. Or if using modern standards: *“Generate a pyproject.toml with Poetry configuration for this project.”* It will list dependencies and such. Ensure author, license, entry points (for CLI, define a console\_script entry so that ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.210.png) pip install weather-reporter![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.211.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.212.png) gives a weather-report command, for example). If containerization: *“Provide a Dockerfile for this application.”* Claude will likely base on python:3.X-slim , copy the code, install requirements, and set entrypoint to ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.213.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.214.png) python cli.py or create a small wrapper. Adjust as needed. 4. **Infrastructure-Agnostic Deployment:** The user request said infrastructure-agnostic, meaning we should describe deployment not tied to a specific cloud. Possibly talk about container vs bare-metal: - We could mention that since it’s a CLI, deployment might simply be publishing it as a pip package or running in a container. - If it were a web app, we’d discuss cloud services, but here perhaps not. 4. **CI for Deployment:** If we plan to publish to PyPI, we could integrate that into CI: *“Extend the CI workflow to publish to PyPI when a new tag is pushed.”* Claude may add a job that triggers on tags, uses ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.215.png)pypa/gh-action-pypi-publish , etc. Would need PyPI API token secret configured – mention in docs to add that. If deploying via Docker, we could push to a registry similarly. 6. **Claude Code in CI Context:** Interestingly, Anthropi suggests using Claude in CI to triage issues or PRs. For example, one could set up a GH Action that, on a new issue, calls Claude (via headless mode) to label it or suggest fixes. This is an advanced use: not required but worth noting: We could have a step in CI like: - name: AI Code Review![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.216.png) run: claude -p "Review the changes in this PR for potential issues." However, that requires careful prompting and an API key in CI, likely experimental. Our focus is more on using CI to ensure our code remains correct as we develop. 7. **Multi-Environment Testing:** We might want to test on multiple OS or Python versions. In GH Actions, that means a matrix job. Ask Claude: *“Modify CI to test on Python 3.8, 3.9, 3.10 on both Ubuntu and Windows.”* It can produce a matrix strategy. Confirm YAML format correctness. 7. **Static Analysis Tools in CI:** We already included flake8, mypy. Could add others: *“Also run Bandit in CI.”* Claude adds a step. 7. **Continual Monitoring:** We should mention how after deployment, continuous monitoring (next section) will track performance, logs, etc. For now, our CI ensures code quality at build time. 7. **Save CI Config:** All these config files (workflow YAML, Dockerfile, etc.) should be committed. Claude can do: *“Commit the CI workflow and Dockerfile.”* It will add and commit them with a message. Now, with CI pipelines in place, any new commit triggers tests and checks. This greatly helps collaborative scenarios or ensuring reliability before hitting production. Next, we will address deployment and operations considerations like monitoring and observability. 14. **Infrastructure-Agnostic Deployment Strategies** Deploying a Python project can take many forms. “Infrastructure-agnostic” here means our deployment approach should not be tied to a specific platform (like AWS, Azure, etc.), but rather be general enough to apply anywhere. We’ll discuss packaging, containerization, and general best practices so our CLI tool can be easily deployed or distributed. 1. **Packaging for Distribution:** One straightforward way to deploy a CLI tool to users is via PyPI packaging. By creating a proper ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.217.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.218.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.219.png) pyproject.toml or setup.py , we allow anyone to pip install our tool. Claude has helped generate these configuration files in the CI step. Key points: - Ensure the package has a unique name (e.g., ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.220.png) weather-reporter ). - Include a console script entry point so that after installation, the user can run weather\_report (for example) from the command line without invoking Python explicitly. - Include all dependencies in the package metadata so they install automatically. We can test this locally (or via CI) by building the package ( python -m build using build module) and possibly uploading to TestPyPI as a dry run. Claude can help: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.221.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.222.png)*“Add to the CI a step to build the package and (optionally) upload to TestPyPI on pushes to main branch.”* For now, ensure the packaging works by building locally: pip install build![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.223.png) python -m build Claude can execute this if asked. It will produce a wheel and sdist. If there are issues (like missing README or version not set), fix accordingly. 2. **Containerization:** For deploying in a server environment or as part of a larger system, a Docker container might be useful. We asked Claude for a Dockerfile: FROM python:3.9-slim WORKDIR /app![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.224.png) COPY . . RUN pip install -r requirements.txt ENTRYPOINT ["python", "cli.py"] This container can be run anywhere Docker is available, making it platform-agnostic. If we intend to schedule this job (e.g., daily report email), a container could be run by a cron in Kubernetes or similar. To keep it generalized: - Don’t bake in environment-specific details; e.g., the API key can be passed via env variable at runtime rather than hard-coding in image. We should modify our code to read ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.225.png) API\_KEY from environment (if not done). Claude can easily incorporate: *“Modify weather\_api.py to read API\_KEY from an environment variable named OPENWEATHER\_KEY, and throw a clear error if not set.”* That way, our container or installed package relies on an external config for secrets – good practice. 3. **Deployment on Various Infrastructures:** - **On-Prem or VM:** If someone wants to run on a VM or bare metal, they can install Python and pip install our package (or use Docker). - **Cloud VM (AWS EC2, etc.):** Same approach – nothing special, just treat it as a normal Python app or container image. - **Serverless or Managed Cron:** If we wanted to deploy on AWS Lambda or GCP Cloud Run (serverless), the code likely needs modifications (e.g., no long-running process). For our CLI, not too relevant unless turning it into a function triggered on schedule or HTTP. That’s beyond initial scope but possible if extended to a web service. 3. **Configuration Management:** For deployment, externalize any config (like API endpoint, API key, default city maybe). We have at least the API key external. One could also allow environment variables for default units or such. This ensures the same artifact can be deployed with different configs in different envs (dev/staging/prod) without code changes. 3. **Logging and Monitoring Hooks:** While not platform-specific, consider adding logging that integrates with infrastructure: - Using Python’s ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.226.png) logging module with INFO/ERROR logs means if running on a server, logs can be collected by standard means (log files or forwarded to log management). - If we containerize, we might just rely on stdout logging and let Docker logging drivers or orchestrator capture them. We should verify our CLI uses logging appropriately instead of print for important messages. Possibly swap some prints to logging calls, which can be configured. 6. **Scalability Considerations:** For our CLI, not much – it’s a single process fetching one city’s data at a time. If one wanted to run it for many cities concurrently, one could parallelize or use multiple containers. 6. **Documentation for Deployment:** Provide a README section or docs/DEPLOYMENT.md describing how to deploy. Claude can help write that: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.227.png)*“Draft a short Deployment guide in DEPLOYMENT.md explaining how* *to install or run the container, how to set env var for API key, etc.”* This is mostly explanatory text; ensure to cite any needed commands (like ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.228.png) docker run -e OPENWEATHER\_KEY=... image:tag --city ... ). 8. **Verify on a Fresh Environment:** A robust check is to simulate deploying to a brand new environment: - Use Docker container on a local machine or CI to run the app and ensure it works end-to-end. - Or use a Python virtual environment with only published package installed and run it. Claude can simulate environment by running in its isolated tool, but an actual outside test is ideal. 9. **Security Considerations for Release:** Before deploying publicly, ensure secrets are not in code repo (the API key is external, good). Also consider if the code is safe from typical issues: - Any injection risk? (Not really in our CLI). - Dependencies up to date? We could check ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.229.png) pip index for latest versions or rely on dependabot in GH. 9. **Rollback Plan:** If deploying new versions, have versioning. We gave a version in packaging, e.g., 0.1.0, and can bump it via Claude in future changes. At this stage, we have covered how to get our application packaged or containerized, ready to deploy on any infra from local machine to cloud. We emphasized keeping config external and not relying on any one provider’s tools, which satisfies being infrastructure-agnostic. Now, in the concluding sections, we will discuss monitoring and maintenance to ensure the software remains reliable over time. 15. **Monitoring, Logging, and Observability** After deployment, a production-ready system requires **monitoring and observability** to ensure it runs correctly and to catch issues early. For a CLI tool like ours, this might involve capturing logs, tracking usage metrics, and handling errors gracefully so they can be alerted. 1. **Logging Best Practices:** We already considered using Python’s logging module. Ensure that our application logs important events: - Successful completion of tasks (e.g., "Report generated for London, 7 days, unit=C"). - Warnings or recoverable errors (e.g., "City not found, exiting with error"). - Unexpected exceptions should at least be logged with stacktrace and a clear message before the program exits (if it doesn’t crash in a controlled way).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.230.png) Claude can help ensure this: *“Replace print statements with appropriate logging calls (info for normal operations, error for exceptions). Configure logging to output timestamps and log levels.”* It might add something like: import logging![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.231.png) logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s:% (message)s") at program start, and use logging.info(...) , logging.error(...) , etc.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.232.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.233.png) Now, when running in production, these logs can be collected. On a server, they might go to a file or to stdout (which for Docker goes to container logs). Infrastructure can aggregate them (e.g., with ELK stack or cloud log services). 2. **Monitoring Metrics:** If this were a web service or more complex app, we’d consider metrics like request count, latency, etc. For a simple scheduled CLI, relevant metrics could be: - Success vs failure count of runs. - How long it took to run (duration). - For deeper insight, maybe how many days of data processed or any unusual data values. We could integrate a metrics library or just log durations. For instance, log an INFO "Processing took X seconds". Then an external system can scrape logs to see if X is trending up. Alternatively, one could instrument the code with a statsd client or cloud monitoring SDK. But that starts tying to specific platforms. To remain agnostic, logging metrics (or outputting metrics to stdout in a known format) is fine. 3. **Alerting on Failures:** Our program should exit with a non-zero status on failure (like if an exception not caught). In many systems, that triggers an alert or at least a log entry. If we want to be fancy, we could catch exceptions and send an email or notification when something goes wrong (but that might duplicate what infrastructure does). Better approach: ensure any unhandled scenario is at least logged as error before exit. Then rely on external scheduler or monitoring to see "job failed" and alert. 4. **Observability Tools Integration:** If using a platform, one could integrate with that: - For example, if on AWS, we might push logs to CloudWatch, metrics to CloudWatch Metrics. - But since agnostic, we just design outputs that any platform's agent can pick up. For instance, writing a line "METRIC: generation\_time\_seconds=5.2" in the log can be parsed by a monitoring system if configured. 5. **Using Claude Code for Monitoring Setup:** We can have Claude generate config for monitoring, but that is usually platform-specific (Prometheus config, etc.). Instead, focus on code’s own observability: *“Ensure the code wraps main execution in try/except to log exceptions and exit with code 1.”* Claude will implement something like: if \_\_name\_\_ == "\_\_main\_\_":![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.234.png) try: main() except Exception as e: logging.exception("Unhandled error") sys.exit(1) This makes sure any error is logged with full stacktrace (logging.exception does that), then program exits with 1. Now monitoring just needs to catch that exit or log. 6. **Simulate a Failure:** As a test, we could intentionally cause a failure (like call with invalid city) and see that our logging and exit code works as expected. Claude can help: *“Run the program with an invalid city and confirm the exit code is 1 and an error is logged.”* It might do that and show output (error log line and maybe how it captured exit code; capturing exit code might not be trivial in the conversation, but we can trust if we coded sys.exit(1) that’s fine). 6. **Observability during Development:** During dev, Claude’s ability to analyze logs was mentioned. We can also use it to monitor changes by hooking into git commit hooks or CI: For instance, a **pre-commit hook** could run Claude Code in headless mode to enforce certain patterns. But that's advanced (like "explain the diff" or ensure commit message quality). Probably out of scope for now, but interesting. 6. **Long-Term Data Considerations:** If our tool writes a report to console, maybe we want to store outputs historically. If so, we could incorporate writing to a file or database. But let's assume not required; observability would then include verifying the outputs. One could potentially use Claude to periodically verify the output’s correctness by cross-checking a different source (that's a bit meta: an agent verifying its own output with another API or so). Interesting but beyond this. 9. **Keep Monitoring Config Agnostic:** We won't tie to one vendor. Instead, instruct user that you can deploy this anywhere and use local system tools: - On Linux, use cron and monitor ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.235.png)/var/log/ourapp.log for errors. - Or use a container orchestrator’s health checks (if container fails, it restarts and raises alarm after X retries). - Provide recommendations like: "Use supervisord or systemd to run the script daily and restart on failure." Claude can even generate a systemd service file if asked. 10. **Feedback Loops for AI Agent Limitations:** One might consider that if Claude Code was integrated continuously, we could let it analyze live issues and propose fixes on the fly. For caution, typically we wouldn’t allow an AI to auto-deploy code fixes without human review, but it could assist engineers by analyzing any new exceptions that come up in logs (similar to how some APM tools do root cause analysis). This ties into limitations we will cover in next section about unknown tools or incomplete info. Now our project is instrumented to be observable and maintainable in production. Next, we address known limitations of Claude Code and how to mitigate them to ensure reliability when using such an AI agent in our workflow. 16. **Long-Term Maintenance and Code Evolution** Software is never truly “done” – requirements change, dependencies get updates, and new issues arise over time. In this section, we discuss how Claude Code can support the **long-term maintenance** of a project and how to approach updates safely. 1. **Dependency Updates:** Over time, libraries like requests or pytest will release new versions. We should keep dependencies updated for security and compatibility. Claude Code can help: - If a GitHub Dependabot alert or similar indicates a needed upgrade (e.g., requests 2.x to 3.x), you can ask Claude to handle the upgrade. For example:![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.236.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.237.png) *“Upgrade the code to be compatible with requests v3.0.0. Fix any deprecations.”* Claude might know (via its training or by searching docs) what changed in requests (just hypothetical). If not, it can search the web for "requests 3.0 migration". After adjustments, run tests to ensure all still pass. - It can also update ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.238.png) requirements.txt with new pinned versions. Possibly you might integrate a periodic Claude-run to update dependencies (though careful: major upgrades need manual check). But minor version bumps it can handle. 1. **Fixing Newly Discovered Bugs:** New bug reports can be handled similar to how we debugged earlier. For maintainers: - If a user files an issue on GitHub, we could involve Claude by tagging it (with the GitHub Action integration, we could have Claude automatically respond or propose a fix via a PR as Anthropi’s example suggests). This is advanced but possible: Claude Code’s GitHub Action can allow an @claude mention to trigger it to attempt to fix an issue or implement a feature on a branch. - Without automation, simply use Claude on your side: copy the issue description to Claude and ask for analysis or direct fix suggestions. 1. **Refactoring and Improving Code:** As the project grows, you may want to refactor for clarity or performance. Claude is very helpful with large-scale refactors: - For instance, splitting a large module into smaller ones, or renaming a function globally. You can instruct: *“Refactor: split report\_generator.py into two files: one for formatting text and one for the ASCII chart logic.”* Claude will create new files and move code accordingly, updating imports. It’s like having an intelligent IDE refactor, except it uses understanding rather than static AST only. - Ensure tests still pass after refactor – have Claude run them. - You can also have Claude modernize code if new language features appear (like, "Use pattern matching (PEP 634) instead of if/ elif chain" if we were on Python 3.10+ and it suits, though probably not needed for our case). 1. **Documentation Maintenance:** As code evolves, docstrings and docs might become outdated. Use Claude to update them: *“Update the README to reflect the new CLI option we added.”* It will edit the markdown accordingly. *“Scan the code for any TODO comments and either resolve them or document them in an issue tracker.”![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.239.png)* Claude can find # TODO: comments and help address them or compile them into a list. 1. **Team Collaboration with Claude:** If multiple developers are working, each can use Claude CLI locally. There is also mention of **Claude Code team features** which allow sharing memory (the project CLAUDE.md is one example – all team members benefit from it). For consistent style, the team might put guidelines in CLAUDE.md, so Claude will follow them for everyone. 1. **Known Limitations Over Time:** We will talk more in next section, but note some potential long-term pitfalls: - **Concept Drift:** The context in CLAUDE.md might get stale or overly large. Periodically prune or update it so Claude isn’t reading outdated design decisions. - **Model Updates:** As Anthropic releases new models (Claude 5?), you might want to switch. Evaluate if new model has changes in how it follows instructions or has new capabilities (like built-in knowledge of newer libraries). - **Tool Changes:** If your environment changes (like switching from GitHub to GitLab), you’d need to adjust how you use Claude. Thankfully Claude Code is adaptable (the CLI can point to different git providers or just run generic git commands). 7. **Continuous Learning:** Encourage a practice where whenever maintenance tasks are done, you incorporate the outcomes back into memory: - If a tricky bug fix was needed, add a note in CLAUDE.md about that scenario so future changes avoid regression. Or even write a test for it (we should always add tests for bugs found). - If an external decision (like "We will deprecate feature X next year") is made, document it so Claude doesn’t keep focusing on a soon-to-be-removed part. 7. **Use of Todo List for Maintenance:** The previously mentioned /todo tasks feature can be used to keep![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.240.png) track of maintenance chores. For instance, if not addressing something now, create a ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.241.png)MAINTENANCE.md or a checklist that Claude can update when things are done. 7. **Relying on CI/CD:** Ensure your CI (tests, linters) stays up-to-date and runs regularly (on schedule or on each commit). This automated safety net catches issues from maintenance tasks. Possibly even add a scheduled CI run weekly that just runs tests and maybe uses Claude to scan for potential issues (like check if any dependencies have security advisories). While not built-in, one could script a step: e.g., call an API for known CVEs in dependencies, or even use Claude via API to parse ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.242.png) pip audit results (a pip feature to find vulnerable packages). If any found, raise an issue. 7. **Retirement/Archival:** If at end-of-life, you might update README that project is no longer maintained. That’s beyond what AI does; just a note that maintenance also includes deciding when to stop. Summing up, Claude Code can be a continuous assistant throughout the project’s lifecycle – not just initial code writing, but updating, refactoring, and keeping docs/tests in sync. By integrating it into regular development and maintenance workflows, a solo developer (or team) can reduce grunt work and focus more on decision-making and creative tasks. Next, let's explicitly address the known limitations and failure modes of Claude Code, as no tool is perfect, and how to mitigate them when using an AI agent. 17. **Known Limitations and Failure Modes** Claude Code is a powerful AI coding agent, but it is not infallible. Understanding its **limitations and common failure modes** is crucial to using it effectively and safely. Here we enumerate known issues (as of 2025) and scenarios where Claude Code might falter: - **Hallucinated or Incorrect Code:** While Claude is trained on vast code data, it can sometimes produce code that looks plausible but is wrong or even references nonexistent functions or APIs (a form of AI “hallucination”). For example, it might call a method that doesn’t exist in a library, or assume a certain JSON field in an API that isn’t actually there. This typically happens if the spec is ambiguous or the model’s knowledge is outdated. Mitigation: Always test and verify the code (as we did with unit tests). If something doesn’t work, prompt Claude with the error so it can correct itself. Cross-check critical pieces against documentation (Claude’s web search tool can help). - **Overconfidence and Lack of Uncertainty Output:** Claude will almost always give you an answer or code, even if it’s not entirely sure. It doesn’t currently say “I don’t know” in code contexts – it tries something. The burden is on the developer to detect when an answer might be off. Mitigation: Use extensive logging and verification. If tackling a domain outside of common training data (obscure libraries, highly specific business logic), consider breaking the problem down for Claude or providing it documentation to avoid guesswork. - **Context Limit and Memory:** Claude Code has a large context window (tens of thousands of tokens in extended mode), but on a long session it might eventually lose track of earlier details or run out of space to stuff in all relevant code. This could lead to it forgetting an earlier instruction or re-reading only part of a file. Mitigation: Use the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.243.png) /clear command between distinct tasks to avoid irrelevant context bleed. Keep the CLAUDE.md concise and up-to-date to assist memory. If a conversation becomes too lengthy, consider summarizing progress or starting a fresh session with key info (Claude’s ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.244.png) --resume or providing the critical files anew). - **Tool Invocation Failures:** Sometimes Claude might attempt to use a tool that isn’t available or mis- use one. For instance, if ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.245.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.246.png) ripgrep isn’t installed but Claude tries to call rg , or if internet access is down but it tries a web search. Mitigation: If a tool is optional, ensure to either install it or configure Claude’s allowed-tools to not use it if not present. If a WebSearch fails (Claude will often show it tried and got no result), you may need to manually supply that info or retry with different query. Always monitor the CLI output for tool actions; if one fails silently, you might need to guide Claude: e.g., “That search didn’t find relevant info, maybe the API has no such field.” - **Permission Fatigue or Mistakes:** The permission system is great, but users might get into the habit of blindly approving actions. This can be dangerous (e.g., if a malicious prompt injection in code made Claude run a harmful command). Mitigation: Stay vigilant on each approval. Use the permanent allow list wisely (only for safe commands). Do not use ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.247.png) --dangerously-skip- permissions![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.248.png) (the “YOLO mode”) in a non-sandbox environment. If you do use it for bulk tasks, follow Anthropic’s advice to run in a container with no internet and no important files accessible. - **External Integration Gaps:** Claude might not know how to use a specific tool or API it hasn’t seen. For example, if your project needs to call a brand-new API or use a niche command-line tool, Claude might guess incorrectly how to use it. Mitigation: Provide the documentation to Claude (via copy- paste or instruct it to fetch the docs URL if available). Essentially, treat it as a junior dev who needs onboarding on that specific tech. - **Non-deterministic Outputs:** Claude’s responses might vary slightly from run to run (though it’s more consistent than some models). This can be an issue if you rely on consistent formatting (like generating code scaffolds multiple times may yield differences in naming or comment style). Mitigation: Pin important outputs (don’t regenerate the same thing repeatedly expecting exact match, unless you provide the previous output as part of the prompt). Also, use the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.249.png) --seed option if available to fix randomness (the docs mention new API tools like prompt caching which might help). - **Performance on Very Large Codebases:** Claude Code can navigate large projects by on-demand file reading, but if a repository has thousands of files, searching through them (like ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.250.png) grep across huge code) could be slow or time-consuming. It might also bump into OS or memory limits reading extremely large files. Mitigation: Scope its search by giving specific directory hints (“grep only in src/ folder for X”). If dealing with giant files, consider splitting them or only feeding Claude relevant slices. - **Cost Considerations:** Since Claude operates via an API, token usage costs money (unless using a free local setup in the future). Extended sessions with many tool uses can accumulate token usage (though extended thinking uses the “thinking” budget outside the prompt/response, which might have a cost as well). Mitigation: Monitor usage (Claude Code has usage monitoring features for teams to see how many tokens are used per session). Use extended thinking only when beneficial; don’t always say “think harder” if not needed. And perhaps limit the length of sessions by clearing context when done to avoid carrying huge conversation history. - **Security and Privacy:** Claude Code sends code and prompts to Anthropic’s servers (or via Bedrock/ Vertex). Proprietary code or sensitive data should be handled carefully. Anthropic presumably has confidentiality measures, but it’s a third-party service. Mitigation: For highly sensitive code, you might opt for self-hosted models (though Claude is not open source) or ensure your company has an NDA/BAA with the provider. Alternatively, scrub sensitive data before showing it to Claude (like use placeholder values in logs). - **Ethical and Policy Constraints:** Claude has guardrails (it might refuse to do certain tasks if they violate usage guidelines, like writing extremely harmful code or revealing sensitive info). Usually not an issue in normal coding, but it might refuse if asked to do something it considers unethical (e.g., exploit development). If your work is in a grey area (like security tools), you might hit this. Mitigation: You can usually rephrase to clarify legitimate intent, but if it’s truly disallowed, you must respect that and not try to trick the model. This is a limitation in that Claude won’t help with anything against its policy. - **Lack of True Understanding:** Ultimately, Claude doesn’t truly understand the purpose or user needs beyond patterns. It can’t replace a human in making high-level design decisions or interpreting requirements ambiguously (it will make an assumption). Mitigation: Use Claude as an assistant, not an autonomous decision-maker. Always review its design suggestions against your own reasoning. Use prompt engineering (like asking pros/cons) to ensure it surfaces considerations, but you make the final call. By being aware of these limitations, you can prevent many failure modes. The next section will cover strategies to mitigate these issues when they occur, ensuring the development process remains on track even when Claude hits a stumbling block. 18. **Mitigation Strategies and Best Practices** In light of the limitations discussed, here are **mitigation strategies and best practices** to ensure smooth use of Claude Code: - **Cross-Verification and Testing:** As reinforced throughout, always test the outputs of Claude Code. Write unit tests for critical logic and run them whenever changes are made. Use multiple forms of verification (tests, manual code review, static analysis). This “belt and suspenders” approach catches hallucinations or errors. For example, after Claude writes a complex algorithm, create a small test scenario (possibly using known-good external output) and see if it matches. - **Incremental Development:** Don’t ask Claude to generate a huge chunk of system in one go. Break tasks into smaller pieces (as we did by generating one module at a time). This reduces the chance for large-scale mistakes and makes it easier to pinpoint where something went wrong. It also keeps the conversation focused, so context is manageable. - **Prompt Clarity and Constraints:** When asking for code, be as specific as possible about what you expect, including any constraints (memory limits, performance goals). If you notice Claude tends to go off track in a certain way (perhaps using a deprecated method), explicitly tell it not to. E.g., “Use Python’s standard library only, do not use any external HTTP client besides requests.”![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.251.png) - **Use of Memory Files (CLAUDE.md):** Keep CLAUDE.md updated with project knowledge and decisions, as this guides Claude in each session. Include known pitfalls: e.g., “We are using version 2 of API, not version 1 – responses differ.” Claude will then factor that in, mitigating mistakes from outdated info. Also include coding style guidelines to avoid back-and-forth on stylistic fixes. - **Manual Tool Use if Needed:** If Claude struggles with a certain tool (maybe it repeatedly fails to configure something in CI), don’t hesitate to do that step manually or use a simpler approach. You can then explain the result to Claude. For instance, if YAML spacing tricked it, fix the spacing in an editor, then show the final to Claude so it knows for future changes. - **Chaining and Decomposition:** Use the model’s ability to plan. Ask Claude for a plan (with “think” prompts) and verify the plan before execution. This often surfaces potential issues early. If the plan seems flawed, correct it in English then let Claude follow the corrected plan. - **Peer Review (AI and Human):** Consider using the multi-Claude workflow as a quality check: have one Claude instance generate code, and another instance review it critically. The second may catch subtle issues (like missing edge cases or code style issues). And of course, human review is key – treat AI-generated code as if it were written by a junior developer: review it thoroughly, especially if it’s security-sensitive or complex. - **Locking Down Dependencies:** Use your dependency management (like requirements.txt with pinned versions or a lockfile) to ensure reproducibility. This prevents an environment drift that could confuse Claude (if for example a major version bump happens and the code no longer works, tests will catch it and you can address with Claude then). Also, by pinning versions, when you intentionally upgrade, you involve Claude explicitly rather than it happening behind the scenes. - **Fail-Safe Mechanisms:** Implement safeguards in the code for critical operations. For instance, if Claude generates code that calls an external API, have timeouts and fallback logic. That way if the AI logic wasn’t perfect (like it didn’t consider network failures), your app still handles it gracefully. Claude can actually help add these after the fact if you prompt “Add timeout and retry to API calls”. - **Limiting Autonomy for Unfamiliar Tasks:** If you or Claude are venturing into unknown territory (maybe integrating a complex third-party system), it might not be wise to just say “Claude, do it all.” Instead, guide it more closely: get documentation, implement step by step with frequent test points. Essentially, reduce the agent’s autonomy when risk is higher. On the other hand, for well-bounded repetitive tasks (like fix all lint errors), you can grant more autonomy (maybe use ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.252.png) yes! to allow all safe edits, or YOLO mode in a disposable branch), because the risk is low and the efficiency gain is high. - **Regular Clean-Up:** Over a long session or project timeline, there might be accumulated junk (old branches, outdated notes in CLAUDE.md). Periodically, use Claude to clean these up: e.g., “Remove any functions marked deprecated that are no longer used” or “Our CLAUDE.md still references ModuleX which we removed; update the memory file.” This avoids confusing future Claude sessions. - **Community and Official Resources:** Stay updated with official Claude Code documentation and community forums (there might be a developer discord or subreddit as referenced). Many failure modes may have been encountered by others; perhaps Anthropi updates the tool with fixes (make sure to run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.253.png) claude update once in a while). If the AI is giving you a really hard time on something, it could be a known issue – checking release notes or discussions might provide a workaround or at least confirm it’s not just you. - **Fallback to Human Effort:** Ultimately, if Claude is stuck in a loop or not getting something right after several attempts, don’t waste too much time. Take over manually for that bit, then later analyze why the model struggled – this could be due to ambiguous prompt or a model knowledge gap. You can feed the correct solution back into Claude so it learns in context, but sometimes it’s more efficient to do it yourself and move on. Use Claude where it adds value, and know when to step in. By following these best practices, you greatly reduce the impact of Claude Code’s limitations. In our guide’s journey, we saw how testing and incremental prompting helped catch mistakes early, how specificity in prompts guided the model correctly, and how manual oversight was always present to approve changes. This human-AI collaboration model leads to the best outcomes. Finally, we will conclude with a checklist summarizing the key steps for taking a project to production using Claude Code, and a glossary of terms used in this guide. 19. **Extending Claude Code with Auxiliary Tools** Claude Code’s utility can be enhanced by incorporating **auxiliary tools and extensions** that complement its built-in capabilities. These tools can improve productivity or handle tasks that Claude alone might be less efficient at. Here are several ways to extend Claude Code: - **Model Context Protocol (MCP) Servers:** As mentioned earlier, MCP allows Claude to connect to external specialized tools or services via a standardized interface. For example, you could connect a *Puppeteer MCP server* to let Claude automate a headless browser (perhaps for generating a screenshot of our weather report, if it had a graphical form), or an *iOS simulator MCP* to test mobile code. To use MCP: - Run or have access to an MCP server (could be a local process or a remote endpoint providing a capability).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.254.png) - Use claude mcp add command to register it (with appropriate name and command/URL).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.255.png) - Once added, Claude can invoke it using the name. For instance, after adding a server my-browser for screenshots, Claude might call my-browser.screenshot("http://example.com") internally. - Be cautious: only add MCP servers you trust (a malicious MCP could compromise your environment). Also, note that using internet-connected MCPs may expose you to prompt injection via those channels, so keep them sandboxed. - **Custom Slash Commands:** You can extend Claude with project-specific commands. Claude Code allows adding custom slash commands by placing markdown files with content or instructions in ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.256.png).claude/commands/ . For example, if you frequently ask Claude to perform a certain multi-step operation (like "update weather API data and regenerate sample output"), you could encode that in a custom command:![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.257.png) - Create a file .claude/commands/update-sample.md with something like: /update-sample:![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.258.png) 1. Run get\_weather\_data for a default city and save output to sample.json 1. Regenerate documentation snippet in README with the new data - Then in the CLI, simply typing /update-sample would cue Claude to execute those steps. This is like scripting the AI with natural language.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.259.png) - Custom commands can be shared with teammates via the repo. They amplify productivity by automating complex sequences. - **Git Hooks with Claude:** One can integrate Claude Code into git hooks for automation. For instance, a ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.260.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.261.png) pre-commit hook could run claude -p "lint and format the code" automatically, saving the developer time. Or a commit-msg hook could use Claude to analyze the diff and suggest an improved commit message (though commit messages are often better from the author’s intent). This is an experimental area, but it showcases how Claude can be part of the dev toolchain beyond the editor. - **Integration with Editors/IDEs:** While our focus is CLI, note that Claude Code has official plugins for VS Code and JetBrains as of GA. Those essentially bring Claude’s suggestions into the editor environment. Using them could speed up code writing by inline suggestions and allow quick switches between CLI and editor. It’s not exactly an “auxiliary tool” to Claude Code, more of an alternative frontend, but in practice you might use both (e.g., CLI for big tasks and IDE plugin for on- the-fly suggestions while typing code). - **Batch or Parallel Operations:** You can use shell scripting or Python scripts to call Claude Code in batch mode for large-scale tasks: - Example: you have 100 log files to analyze with Claude. Instead of doing it manually, write a small shell loop: ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.262.png) for f in logs/\*.log; do cat "$f" | claude -p "summarize the errors in this log"; done . Each iteration calls Claude in one-shot mode to process a log. This shows how Claude can be a component in pipelines (like using it as a Unix-style filter). - If using the SDK (once Python SDK is available), you could script parallel calls or orchestrate more complex logic around Claude’s outputs. - **Combining with Other AI Models:** While outside Claude Code itself, one might use Claude in conjunction with other models. For example, you might use OpenAI’s models for something and Claude for something else, comparing outputs or using whichever is best for a subtask. An example workflow: if you have a codebase documentation task, you could try both and merge results. - However, keep in mind context and formatting differences. It might be more straightforward to stick to one at a time per project, to avoid confusion. But for those with resources, this can mitigate a single model’s blind spots (like ensemble thinking). - **Automating Project Management:** Claude can help generate release notes by comparing git diffs, or even draft pull request descriptions by summarizing commits. These aren’t exactly coding tasks, but they integrate with development. For instance, one could make a GH Action that on a merged PR, calls Claude to generate a summary of changes to add to CHANGELOG.md. - **Utilize Platform Proxies:** If working within a corporate environment that proxies AI access (like using Anthropic via AWS Bedrock or a self-hosted proxy as mentioned[ 5 ](https://docs.anthropic.com/en/docs/claude-code/bedrock-vertex-proxies#:~:text=)), ensure to leverage enterprise features such as prompt logging or monitoring provided by those platforms. They might give analytics on Claude’s usage or performance. This can be an auxiliary way to gauge how well the AI is doing (like tracking how often you have to correct it manually).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.263.png) In summary, Claude Code is extensible. By connecting it with specialized tools (via MCP), custom commands, or embedding it in scripts, you can automate even more of your development workflow. These auxiliaries support faster iteration and can tailor Claude to your project’s unique needs. Always weigh the complexity added against the benefit – sometimes a quick manual step is fine, but for repetitive or large- scale tasks, these extensions can be game-changers. Now, let's move to the conclusion with a checklist and glossary. **20. Conclusion and Next Steps** We have traversed the entire journey of using Claude Code as a “Vibe” coding agent, from an empty repository to a fully tested and deployable Python project. This guide demonstrated how to harness Claude Code’s capabilities at each stage of development: - We began with **ideation and planning**, where Claude helped refine requirements and propose a design, effectively serving as a brainstorming partner. - Through **code generation** and **iterative refinement**, we leveraged prompt engineering to build out the project module by module, while continuously validating the output via tests and analysis. Claude’s extended reasoning and tool use allowed it to not just write code but also run it, test it, and fix it in a loop – a significant productivity boost. - We set up robust **CI/CD pipelines**, with Claude automating the creation of workflow configurations and ensuring our project can be consistently built, tested, and delivered in an infrastructure-agnostic manner. - We instrumented the application with **logging and monitoring**, so that once deployed, the system is observable and maintainable. We also discussed how to keep Claude’s contributions aligned over the long term and how to handle updates, ensuring that the AI continues to assist effectively during maintenance. - Finally, we addressed **limitations** of using an AI agent and laid out **best practices and mitigation strategies** to handle them – emphasizing that human oversight, careful prompting, and comprehensive verification are key to a successful human-AI collaboration in coding. Claude Code (Q1/Q2 2025 version) emerges as a powerful ally for solo developers and teams alike. It brings a conversational, agentic approach to programming that can accelerate development cycles. By following the techniques in this guide – structured prompting, iterative development, safety checks, and tool integration – developers can achieve a significant “vibe”: a flow state where the AI handles routine tasks and heavy lifting, while the developer steers the creative and critical decisions. **Next Steps:** As you adopt Claude Code in your workflow, start with small projects or components to get accustomed to its behavior. Experiment with the prompt strategies outlined, and adjust them to your personal style and project needs. Incorporate the bot into your version control practice (perhaps trying out the GitHub PR integration for code reviews). Over time, build a library of your own custom commands or memory notes that make Claude even more effective for your domain. Keep an eye on updates from Anthropic – new model versions or features (like improved extended thinking or additional tools) will further expand what’s possible. In conclusion, while AI coding agents are not a replacement for human developers, they are a powerful force multiplier. They handle the boilerplate, provide instant research and suggestions, and even catch errors, all while you maintain the high-level vision and judgment. By mastering tools like Claude Code, you can focus more on what’s truly important – building value and innovation into your software – and let the AI handle the repetitive and mechanical aspects, truly coding “at the speed of thought.”![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.264.png) Below is a **Deployment Checklist** summarizing the key items to finalize when releasing a project, followed by a **Glossary** of terms for quick reference. **Deployment Checklist** Before releasing your project to production, ensure the following items are completed: - **Requirements Defined:** All project requirements are documented (in CLAUDE.md or a requirements doc) and agreed upon.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.265.png) ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.266.png) - **Code Complete:** All planned features are implemented and the codebase is free of TODO or placeholder sections (or such items are moved to a backlog for future). - **Testing Coverage:** Unit tests and integration tests cover the main functionality and edge cases. Run ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.267.png) pytest (or your test suite) – all tests should pass. - **Static Analysis Clean:** Linting (flake8/pylint) shows no warnings or only acceptable ones, and static type checking (mypy) passes without errors. Security scan (bandit or similar) has been run and any high issues are addressed. - **Performance Check:** The code meets any performance requirements. If needed, profiling has been done and hotspots optimized. (For our CLI, this might be N/A or trivial, but consider API call latencies or large input handling). - **CI Pipeline Green:** The CI workflow (GitHub Actions or others) runs successfully on a fresh clone – meaning all steps (install, test, lint, build) work in a clean environment. - **Dependencies Locked:** Dependencies are pinned to known-good versions (in ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.268.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.269.png)requirements.txt or lockfile). Check for any known vulnerabilities in these versions (using pip audit or similar). - **Configuration Externalized:** All sensitive or environment-specific configuration (API keys, endpoints, etc.) are pulled from environment or config files, not hardcoded. Confirm that in your deployment environment, those configs are set (e.g., ensure the ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.270.png) OPENWEATHER\_KEY env var will be provided). ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.271.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.272.png) - **Packaging Done:** If distributing, the package builds correctly (e.g., python -m build or pip wheel .![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.273.png) completes). If publishing to PyPI or container registry, test that pipeline (maybe via TestPyPI or a private registry). - **Logging and Monitoring:** Logging is set up at appropriate levels (INFO for normal ops, ERROR for exceptions)[ 11 ](https://www.anthropic.com/engineering/claude-code-best-practices#:~:text=e,often). Test run the program and ensure logs appear as expected (check format, timestamps). Set up monitoring alerts if applicable (e.g., if run via cron, ensure failures send an email or alert).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.274.png) - **Documentation Updated:** README and any user documentation reflect the current usage, installation, and examples. Include how to configure and run the application. If the tool is public, include a license file and possibly contribution guidelines. - **Review and Approvals:** Perform a final code review (manual). If working in a team, get any necessary approvals or security reviews. Ensure no sensitive info is accidentally in the repo (like debug logs with private data). - **Tag and Release:** Once satisfied, create a version bump (update version in code or metadata) and tag the release in git. This tag can trigger CD (like publishing to PyPI or deploying containers) if configured. - **Post-Deploy Verification:** After deployment, do a quick smoke test in the production environment. Run the CLI or service to verify it works with live data, and monitor logs closely for the first run. - **Backup Plan:** Have the previous version ready in case a rollback is needed. Keep good documentation of what was deployed and when (release notes). - **Maintenance Plan:** Note any scheduled tasks (like certificate renewals, API key rotations) that might affect the application later. Create calendar reminders or issues for these so they aren’t forgotten. This checklist ensures a smooth transition from development to production, with minimized surprises. **Glossary** - **Anthropic Claude** – A family of AI models created by Anthropic. Claude 4 (Opus and Sonnet) is the 2025 generation, known for coding and reasoning capabilities. - **Claude Code** – The CLI-based coding assistant/agent using Claude models. Lives in the terminal, can use tools, and helps with coding tasks through natural language. Reached general availability in May 2025. - **Claude Opus 4 / Sonnet 4** – Variants of the Claude 4 model. Opus is the larger, more powerful model optimized for long, complex tasks; Sonnet is a faster, lightweight model for prompt responsiveness. Claude Code uses these behind the scenes (and can switch between them for extended thinking vs quick replies). - **Extended Thinking** – A mode where Claude takes more time and uses tools to reason through a problem. Triggered by prompts like “think hard,” it allows up to 64k token reasoning and parallel tool use for better solutions[ 3 ](https://techcrunch.com/2025/05/22/anthropics-new-claude-4-ai-models-can-reason-over-many-steps/#:~:text=Opus%204%20and%20Sonnet%204,as%20%E2%80%9Ctacit%20knowledge%E2%80%9D%20over%20time).![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.275.png) - **Tools / Agents (in Claude Code)** – Built-in capabilities Claude can invoke, such as Bash (shell commands), Edit (file editing), Read (file reading), WebSearch/WebFetch (internet queries). An "Agent" tool spawns a sub-agent (another instance of Claude) for subtasks. - **Permission System** – Claude Code’s safety mechanism requiring user approval for certain actions (write, execute, internet). It prevents unauthorized destructive operations by asking the user to Allow or Deny each sensitive tool use. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.276.png) - **REPL** – "Read-Eval-Print Loop", an interactive session mode. Claude’s REPL is started by claude where you can converse with the agent continuously. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.277.png) - **One-shot Mode** – Running Claude with a single prompt non-interactively (via -p ). It processes input and prints output, then exits. Useful in scripts or CI.![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.278.png) - **CLAUDE.md** – A special memory file for Claude Code, placed in the project to give persistent context (project info, conventions). Claude automatically loads it at session start. - **MCP (Model Context Protocol)** – An open protocol to extend LLMs with external tools or data sources. Claude Code can act as an MCP client to connect to servers that provide extra capabilities (like browsing or custom APIs). - **Custom Commands (/slash commands)** – User-defined commands in Claude’s interface to execute preset actions. Stored under ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.279.png)![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.280.png) .claude/commands/\*.md and invoked with /name in the REPL. - **Continuous Integration (CI)** – Automation pipelines that build and test code on changes. We used GitHub Actions YAML to run tests/linters on each push. - **Continuous Deployment (CD)** – Automating the release of code to production or distribution channels. For instance, publishing to PyPI or deploying a Docker image after CI passes. - **Infrastructure-agnostic** – Not tied to a specific cloud or platform. Our strategies (like using Docker, environment variables, standard tools) can work on any infrastructure (AWS, Azure, on-prem, etc.). - **Observability** – The ability to understand the internal state of a system from its external outputs (logs, metrics, traces). We implemented logging and error reporting to make our CLI observable in production. - **Hallucination (AI)** – When an AI generates content that is incorrect or not grounded in reality (e.g., citing a non-existent function). We guard against this with tests and context verification. - **Prompt Injection** – A malicious technique where input to the AI is crafted to make it ignore prior instructions or do unintended things. For example, if an external site’s content says “ignore previous directives,” an AI might comply. Claude Code has some defenses, but user caution is needed especially with WebFetch or untrusted MCP servers. ![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.281.png) - **YOLO mode** – Slang referring to running Claude Code with all permissions auto-approved ( -- dangerously-skip-permissions![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.282.png) ). “YOLO” (“You Only Live Once”) implies risky, unchecked operation. Only to be used in disposable environments due to risk of destructive actions. - **Bedrock / Vertex AI** – Cloud platforms (AWS Bedrock, Google Vertex AI) that host Anthropic’s models. Claude Code can connect to these instead of Anthropic’s API directly for enterprise use. - **Tokens** – The pieces of text (word segments) the model processes. A 100k token context means Claude can handle extremely large inputs (like multiple files) but with associated cost and processing time. - **IEEE-style Citations** – The referencing format used in this document (numeric with bracketed pointer to sources). We used them to cite documentation and authoritative info for credibility. This concludes the technical guide. By integrating Claude Code thoughtfully into your development process, you can benefit from accelerated coding, thorough assistance in testing and maintenance, and a more fluid development “vibe.” Happy coding with Claude!![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.283.png) 1 [6](https://www.anthropic.com/engineering/claude-code-best-practices#:~:text=d)[ 8 9](https://www.anthropic.com/engineering/claude-code-best-practices#:~:text=,to%20take%20a%20different%20approach)[ 11 ](https://www.anthropic.com/engineering/claude-code-best-practices#:~:text=e,often)Claude Code Best Practices \ Anthropic![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.284.png) 2 Claude Code settings - Anthropic![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.285.png) 3 [4](https://techcrunch.com/2025/05/22/anthropics-new-claude-4-ai-models-can-reason-over-many-steps/#:~:text=Opus%204%20and%20Sonnet%204,as%20%E2%80%9Ctacit%20knowledge%E2%80%9D%20over%20time)[ 7](https://techcrunch.com/2025/05/22/anthropics-new-claude-4-ai-models-can-reason-over-many-steps/#:~:text=Both%20Opus%204%20and%20Sonnet,a%20given%20problem%20before%20answering)[ 10 ](https://techcrunch.com/2025/05/22/anthropics-new-claude-4-ai-models-can-reason-over-many-steps/#:~:text=instant%20responses%20and%20extended%20thinking,a%20given%20problem%20before%20answering)Anthropic's new Claude 4 AI models can reason over many steps | TechCrunch![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.286.png) [5 ](https://docs.anthropic.com/en/docs/claude-code/bedrock-vertex-proxies#:~:text=)Bedrock, Vertex, and proxies - Anthropic![](Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.287.png) 44 [ref1]: Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.041.png [ref2]: Aspose.Words.97d16f1e-013a-4ffc-a735-35906fbd729d.076.png