interview-writer
AI 采访式内容创作系统。不是人写,也不是 AI 自动写,而是 AI 分析后采访人再按人的风格写。通过问答不断沉淀用户画像(观点、写作风格、思考逻辑),持续迭代演进。支持博客、社交媒体、观点文章等场景。
AI 采访式内容创作系统。不是人写,也不是 AI 自动写,而是 AI 分析后采访人再按人的风格写。通过问答不断沉淀用户画像(观点、写作风格、思考逻辑),持续迭代演进。支持博客、社交媒体、观点文章等场景。
Use when creating a new skill, updating an existing skill, or learning skill best practices. Load for extending Claude's capabilities with specialized workflows, tool integrations, or domain expertise. Covers skill anatomy, progressive disclosure (98% token savings), and the critical description-as-trigger pattern.
技能创建指南。当用户需要创建新技能(Skill)或更新现有技能时使用此技能。帮助扩展 Claude 在特定领域的专业能力。
Use when creating or developing anything, before writing code or implementation plans - refines rough ideas into fully-formed designs through structured Socratic questioning, alternative exploration, and incremental validation. Optimized for git worktree workflows and Claude CLI agent patterns.
AgentHero AI - Hierarchical multi-agent orchestration system with PM coordination, file-based state management, and interactive menu interface. Use when managing complex multi-agent workflows, coordinating parallel sub-agents, or organizing large project tasks with multiple specialists. All created agents use aghero- prefix.
Model Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
Security guidelines for LLM applications based on OWASP Top 10 for LLM 2025. Use when building LLM apps, reviewing AI security, implementing RAG systems, or asking about LLM vulnerabilities like "prompt injection" or "check LLM security".
MCP(Model Context Protocol)服务器开发指南。当用户需要开发 MCP 服务器、创建 LLM 工具集成、或构建 AI 代理与外部服务交互的接口时使用。
Use OpenAI's Deep Research API (o3 / o4 models) to automate multi-step, citation-backed research workflows.
Install, manage, and run ComfyUI instances. Use when setting up ComfyUI, launching servers, installing/updating/debugging custom nodes, downloading models from CivitAI/HuggingFace, managing workspaces, running API workflows, or troubleshooting node conflicts with bisect.
List/inspect/watch local OpenAI Codex sessions (CLI + VS Code) using the CodexMonitor Homebrew formula.
Suite of tools for creating elaborate, multi-component chat-embedded HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
Copilot agent that assists with machine learning model development, training, evaluation, deployment, and MLOps Trigger terms: machine learning, ML, AI, model training, MLOps, model deployment, feature engineering, model evaluation, neural network, deep learning Use when: User requests involve ai ml engineer tasks.
Use when creating or updating AGENTS.md files. Provides the agents.md format specification and best practices for writing effective guidance for AI coding assistants.
Prompt engineering framework for Claude Code. Transforms vague requests into precise TCRO-structured prompts. Use when: crafting prompts for code generation, improving prompts not getting results, structuring multi-step workflows, debugging pattern drift. Triggers: optimize prompt, improve prompt, create prompt, prompt engineering, TCRO, prompt template, better prompt.
Optimize system prompts for Claude Code agents using proven prompt engineering patterns. Use when users request prompt improvement, optimization, or refinement for agent workflows, tool instructions, or system behaviors.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.