Claude Skills by majiayu000

Design tools that agents can use effectively. Use when creating new tools for agents, debugging tool-related failures, or optimizing existing tool sets.

Design tools that agents can use effectively. Use when creating new tools for agents, debugging tool-related failures, or optimizing existing tool sets.

Design tools that agents can use effectively. Use when creating new tools for agents, debugging tool-related failures, or optimizing existing tool sets.

Build tools that agents can use effectively, including architectural reduction patterns

This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces.

This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces.

This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces.

Build tools that agents can use effectively, including architectural reduction patterns. Use when creating new tools for agent systems, debugging tool-related failures or misuse, or optimizing existing tool sets for better agent performance.

Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.

This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces.

Guide for discovering and using MCP tools via the discover_tools meta-tool. Use this skill when asked to perform tasks that require finding the right tools, when you see only discover_tools in your available tools, when asked "what tools are available", "find tools for X", or when planning multi-step workflows across Slack, WhatsApp, Google, or Slides.

Guide for discovering and using MCP tools via the discover_tools meta-tool. Use this skill when asked to perform tasks that require finding the right tools, when you see only discover_tools in your available tools, when asked "what tools are available", "find tools for X", or when planning multi-step workflows across JIRA, Slack, WhatsApp, or Google Docs.

Semantic tool search with embeddings for scalable tool discovery. Enables on-demand tool loading to reduce context usage by 90%+ for large tool libraries.

CLI-first tool selection policy for Claude Code. Use when choosing between CLI tools and MCP servers, designing new agents, or reviewing agent tool configurations. Do NOT use for executing commands or running tools -- this is for tool selection decisions during agent/config design only.

Design systematic decision frameworks for selecting appropriate AI tools (Claude Code vs Gemini CLI) based on context requirements, codebase size, reasoning depth needs, and task complexity. Use this skill when starting projects with unclear tool requirements, optimizing context-constrained workflows, or designing multi-phase strategies that leverage multiple tools' strengths. This skill helps match tool capabilities to task characteristics, preventing wasted context and ensuring optimal reso...

Design systematic decision frameworks for selecting appropriate AI tools (Claude Code vs Gemini CLI) based on context requirements, codebase size, reasoning depth needs, and task complexity. Use this skill when starting projects with unclear tool requirements, optimizing context-constrained workflows, or designing multi-phase strategies that leverage multiple tools' strengths. This skill helps match tool capabilities to task characteristics, preventing wasted context and ensuring optimal reso...

Use when selecting between MCP tools based on task complexity and requirements - provides a structured selection workflow and decision rationale.

Use when selecting between MCP tools based on task complexity and requirements - provides a structured selection workflow and decision rationale.

Tool use patterns for Claude including schema design, tool_choice modes, result handling, parallel execution, error recovery, and extended thinking integration.

Patterns for Claude tool use including tool definition schemas, multi-tool orchestration, parallel tool calls, error handling, and result formatting. Use when the user is defining tools for Claude, building agentic workflows with tool calling, handling tool errors, or implementing multi-step tool pipelines.

Auto-generate custom agents and skills for remaining capability gaps after assembly. Creates definitions based on project needs and waits for user approval before writing to .claude/.

Troubleshooting protocol with MiniMax subagent delegation - NEVER stop early, use all resources before asking user

Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot

Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot

Invoke Claude Code CLI from Python orchestrators and shell scripts. Use when asked to "spawn claude as subprocess", "automate claude cli", "run claude headless", "configure --allowedTools", "set up claude hooks", or "parallel claude invocation". Covers permissions, directory access (--add-dir), hooks, sandbox mode, and async patterns.

Use when programmatically creating or managing agent trajectories in TypeScript - provides TrajectoryClient for persistent storage and TrajectoryBuilder for in-memory construction

Validation agent that validates plan tech choices against current best practices

Ultimate autonomous execution engine. Plan → Execute → Test → Never Stop Until Done. Use when the user wants hands-off completion with no questions, automatic recovery from any failure, and proven results.

Short alias for vc-phase-loop. Use when the user invokes "vcf:" or "use vcf:" and wants maximum hands-off completion.

Create and configure VoltAgent AI agents with tools, memory, hooks, and sub-agents. Use when building AI agents, adding agent tools, configuring VoltAgent memory, creating multi-agent workflows, or debugging VoltAgent integrations.

Create and manage sandboxed WASM agents for isolated code execution

Also called "wr". Optional research agent for broad multi-source research without polluting context. Use when user requests it or when research needs depth across many sites.

当用户要求"微信监听"、"消息提取"、"Agent 开发"、"wxauto"、"Accessibility API"、"UI 自动化"、"输入框控制"、"Platform Agent"，或者提到"微信自动化"、"消息监控"、"WeChat monitoring"时使用此技能。用于开发 WeReply 的 Platform Agent（Windows wxauto 或 macOS Accessibility API）、实现微信消息监听、消息提取、输入框控制和 Agent 错误处理。

Start an autonomous implementation loop from a spec or PRD. Enters plan mode for user approval, enforces command gates (test/lint/typecheck/build), validates dependencies, commits incrementally, and maintains documentation and changelog. Production-ready quality gates.

Execute beads autonomously within a track. Handles bead-to-bead context persistence via Agent Mail, uses preferred tools from AGENTS.md, and reports progress to orchestrator.

Execute beads autonomously within a track. Handles bead-to-bead context persistence via Agent Mail, uses preferred tools from AGENTS.md, and reports progress to orchestrator.

Defines behavior protocol for spawned worker agents. Injected into worker prompts. Covers startup, progress reporting, exit conditions, and handover preparation.

Defines behavior protocol for spawned worker agents. Injected into worker prompts. Covers startup, progress reporting, exit conditions, and handover preparation.

Use when building AI agents with Microsoft Agent Framework (Semantic Kernel + AutoGen unified); when implementing memory or context providers; when threads won't deserialize; when workflow checkpointing fails; when migrating from Semantic Kernel or AutoGen; when seeing ChatAgent or AgentThread errors

Train and deploy generative UI agents using behavioral programming constraints, A2A protocol for agent interoperability, and pattern registry for composition. Use when working with agent training, trajectory generation, reward computation, A2A integration, or deploying models.

Complete fixer agent workflow including report discovery, validation strategy (HIGH/MEDIUM/FALSE_POSITIVE confidence), fix application, and fix report generation. Use when implementing or updating fixer agents.

Step-by-step validation workflow for checker agents - initialize report, discover content, validate progressively, finalize with summary. Use when implementing or updating checker agents.

Systematic methodology for constructing compact (<150 lines), expressive, Claude Code-integrated subagent prompts using lambda contracts and symbolic logic. Use when creating new specialized subagents for Claude Code with agent composition, MCP tool integration, or skill references. Validated with phase-planner-executor (V_instance=0.895).

Start babysitting in non-interactive mode — no user interaction or breakpoints, fully autonomous execution.

zhimeng's Agent 智能助手操作技能。 触发场景：用户提到"问知识库"、"发日报"、"检查Agent"、"重建索引"、"知识问答"等。

Create your llmops-fine-tuner skill from Unsloth documentation before learning fine-tuning theory

Create your ChatKit Server skill in one prompt, then learn to improve it throughout the chapter

Create your Claude Agent SDK skill in one prompt, then learn to improve it throughout the chapter

Create your RAG skill in one prompt, then learn to improve it throughout the chapter

[23] UNDERSTAND. Consult external AI models when internal sources are exhausted. Build quality prompts using Prompt150 formula (Context + Query + Method + Style). Use when Loop150 exhausts internal sources, need real-world precedents, confidence <75%, or require reasoning from specialized AI models.