creating-skills
Generates high-quality, predictable, and efficient `.agent/skills/` directories based on user requirements. Use when the user wants to create a new skill or capability.
发现 测试与安全 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。
Generates high-quality, predictable, and efficient `.agent/skills/` directories based on user requirements. Use when the user wants to create a new skill or capability.
Generates high-quality, predictable, and efficient .agent/skills/ directories based on user requirements. Use when the user asks to create a new skill or "habilidad".
This skill should be used when users want to restore, reload, or copy their Claude Code configuration files (commands, skills, agents) from a backup directory back to ~/.claude/. Triggered by phrases like "rest...
Use this agent to audit and replace button text with compassionate, non-demanding language. User: "Buttons say 'Submit' and 'Delete' - feels demanding" Agent: Use button-language-audit to soften language U...
Adapts orchestration based on workspace context AND observed user prompt behavior over time.
Expert in Model Context Protocol (MCP) server development. Use when building MCP servers, creating tools for Claude, implementing resources, debugging MCP connections, or integrating databases with Claude Code.
Skill focada em transformar solicitações simples em prompts de alta engenharia, utilizando técnicas avançadas (CoT, Persona, Constraints) para maximizar resultados de LLMs.
Set up a cross-platform Ralph Wiggum loop for continuous autonomous Claude execution. Use when the user wants to run Claude in a loop, implement the Ralph technique, or needs continuous autonomous operation. Th...
启动或推进多 Agent 并行开发流程。用于协调多个 Claude Code 实例并行开发项目。
Call claude to perform a task.
when generating a skill, always follow this template.
Full E2E verification with REAL APIs (OpenAI + Gemini). Use when stub tests pass but you need to verify real LLM behavior. Requires hybrid mode (I:ope/E:gem/V:ope). NEVER uses AGENT_MODE=stub.
Expert guidance for building autonomous AI agents using Anthropic's Claude Agent SDK (Python). Covers: agent loop patterns (GTVR: gather context, take action, verify, repeat), ClaudeSDKClient and query() APIs,...
Answer questions from indexed vault content using RAGtriever search, plus setup, configuration, and troubleshooting
Outils avancés pour le 13ème Docteur (Context7, Conductor, Ralph).
Conduct structured interviews with the user. Use when multiple decisions need user input: ask ONE question at a time, wait for response, record answer, then proceed to next question.
Shared project memory management for /context-save and /context-load slash-style commands; use when the user wants to write or read case-specific memory (spec, decisions, next steps, logs) under ~/ai-memory so...
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or...
OpenAI API documentation. Use for GPT models, Chat Completions, Embeddings, Assistants API, Vision, Audio, Fine-tuning, and integrating OpenAI into applications.
Use when users want to save, backup, copy, export, or version control their Claude Code configuration files (commands, skills, agents) from ~/.claude/ to another directory. Triggered by phrases like "save my cl...
Optimize agent instruction files by extracting sections into separate files and referencing them. Reduces context size while preserving information.
Expert assistance for OpenAI Agents SDK Python library including installation, configuration, multi-agent systems, debugging, and best practices. Proactively fetches the latest documentation to ensure accuracy....
Structured clarification before decisions. Use when user is in PLANNING mode, explicitly asks to plan or discuss, or when agent faces choices requiring user input. Ensures agent asks questions instead of making...
Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。