Claude Code Guide
The Claude Code Guide provides configuration templates, prompting strategies, and debugging techniques for effectively using Claude Code as an agentic coding tool. Use this skill when setting up new projects, needing to optimize agent interactions through "thinking" keywords, troubleshooting agent behavior, or establishing best practices for iterative development workflows.
git clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/claude-code-guide && cp -r /tmp/claude-code-guide/cli-tool/components/skills/ai-research/claude-code-guide ~/.claude/skills/claude-code-guideSKILL.md
# Claude Code Guide ## Purpose To provide a comprehensive reference for configuring and using Claude Code (the agentic coding tool) to its full potential. This skill synthesizes best practices, configuration templates, and advanced usage patterns. ## Configuration (`CLAUDE.md`) When starting a new project, create a `CLAUDE.md` file in the root directory to guide the agent. ### Template (General) ```markdown # Project Guidelines ## Commands - Run app: `npm run dev` - Test: `npm test` - Build: `npm run build` ## Code Style - Use TypeScript for all new code. - Functional components with Hooks for React. - Tailwind CSS for styling. - Early returns for error handling. ## Workflow - Read `README.md` first to understand project context. - Before editing, read the file content. - After editing, run tests to verify. ``` ## Advanced Features ### Thinking Keywords Use these keywords in your prompts to trigger deeper reasoning from the agent: - "Think step-by-step" - "Analyze the root cause" - "Plan before executing" - "Verify your assumptions" ### Debugging If the agent is stuck or behaving unexpectedly: 1. **Clear Context**: Start a new session or ask the agent to "forget previous instructions" if confused. 2. **Explicit Instructions**: Be extremely specific about paths, filenames, and desired outcomes. 3. **Logs**: Ask the agent to "check the logs" or "run the command with verbose output". ## Best Practices 1. **Small Contexts**: Don't dump the entire codebase into the context. Use `grep` or `find` to locate relevant files first. 2. **Iterative Development**: Ask for small changes, verify, then proceed. 3. **Feedback Loop**: If the agent makes a mistake, correct it immediately and ask it to "add a lesson" to its memory (if supported) or `CLAUDE.md`. ## Reference Based on [Claude Code Guide by zebbern](https://github.com/zebbern/claude-code-guide).
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Use this agent when creating specialized Claude Code agents for the claude-code-templates components system. Specializes in agent design, prompt engineering, domain expertise modeling, and agent best practices. Examples: <example>Context: User wants to create a new specialized agent. user: 'I need to create an agent that specializes in React performance optimization' assistant: 'I'll use the agent-expert agent to create a comprehensive React performance agent with proper domain expertise and practical examples' <commentary>Since the user needs to create a specialized agent, use the agent-expert agent for proper agent structure and implementation.</commentary></example> <example>Context: User needs help with agent prompt design. user: 'How do I create an agent that can handle both frontend and backend security?' assistant: 'Let me use the agent-expert agent to design a full-stack security agent with proper domain boundaries and expertise areas' <commentary>The user needs agent development help, so use the agent-expert agent.</commentary></example>
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa