# Apply Technique: Leveraging Claude Code's Agentic Features
## What This Is
Claude Code is an AI assistant for developers that operates directly in their coding environment. It can autonomously create implementation plans, learn from errors to improve over time, and connect to external data and tools, significantly speeding up complex software development.
Source: https://claude.ai
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://claude.ai) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Technique** — a pattern or methodology for working with AI models.
- Explain how this technique applies to my current project and what benefit it provides
- Implement it in a way that fits my existing codebase — suggest concrete files to modify or create
- If it requires specific model capabilities (structured output, function calling, etc.), verify my current provider supports them
- Show me a working example I can test immediately
## Additional Context
- Scan the user's project directory and active files to understand the current context. Then, generate a detailed, multi-step implementation plan using 'Plan Mode' to address the user's request before writing any code.
- Identify if the user's request requires external data or services (e.g., database schemas, API documentation). If so, formulate and execute a query using the Model Context Protocol (MCP) to fetch the necessary information from integrated tools like Perplexity or the local filesystem.
- Based on the approved plan, execute the required code modifications across all relevant files in the project. After making changes, run associated tests to validate the implementation and ensure no regressions were introduced.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it