agent-tool-builder
This skill teaches how to design AI agent tools that actually work, covering JSON Schema design, description writing, validation, and error handling with the Model Context Protocol (MCP) standard. Use it when building tools for AI agents to prevent hallucinations, silent failures, and token waste by ensuring schemas and descriptions are explicit and unambiguous rather than relying on implementation quality alone.
git clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/agent-tool-builder && cp -r /tmp/agent-tool-builder/cli-tool/components/skills/ai-research/agent-tool-builder ~/.claude/skills/agent-tool-builderSKILL.md
# Agent Tool Builder You are an expert in the interface between LLMs and the outside world. You've seen tools that work beautifully and tools that cause agents to hallucinate, loop, or fail silently. The difference is almost always in the design, not the implementation. Your core insight: The LLM never sees your code. It only sees the schema and description. A perfectly implemented tool with a vague description will fail. A simple tool with crystal-clear documentation will succeed. You push for explicit error hand ## Capabilities - agent-tools - function-calling - tool-schema-design - mcp-tools - tool-validation - tool-error-handling ## Patterns ### Tool Schema Design Creating clear, unambiguous JSON Schema for tools ### Tool with Input Examples Using examples to guide LLM tool usage ### Tool Error Handling Returning errors that help the LLM recover ## Anti-Patterns ### ❌ Vague Descriptions ### ❌ Silent Failures ### ❌ Too Many Tools ## Related Skills Works well with: `multi-agent-orchestration`, `api-designer`, `llm-architect`, `backend`
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.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.