Boost efficiency with task automation and organizers.
Provides AI consultation workflows, prompt templates, and best practices for multi-model consultation. Use when user asks for "second opinion", "ask codex", "ask gemini", "AI code review", "external AI consultation", or needs prompt templates for code review, security audit, or architecture analysis.
整合智能体输出结果为结构化最终输出，支持多种格式。适用于整合分析报告、生成结构化报告
Beeper Desktop CLI for chats, messages, search, and reminders.
Working with Claude Code features, debugging hooks, MCP integration, snippet verification, headless automation, and Agent SDK. Use this skill when the user asks about Claude Code features, hooks, memory, statusline, debugging, MCP servers, headless use patterns, CI/CD automation, Python/TypeScript Agent SDK, or building custom agents.
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.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Adapted for Warren's system with snippet integration.
Use when designing or implementing cross-service communication, data synchronization, or service boundary patterns.
Synthesize knowledge from multiple sources into Zettelkasten notes for Logseq. Use when creating wiki pages, integrating academic research, or building interconnected knowledge with [[links]] and
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.
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.
Creating and optimizing Claude Code Skills including activation patterns, content structure, and development workflows. Use when creating new skills, converting memory files to skills, debugging skill activation, or understanding skill architecture and best practices.
Replace with a clear description of what this skill does and when Claude should use it. Be specific about the use cases, tools, or workflows this skill addresses.
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.
Analyzes conversations to identify learnings, patterns, and techniques for capture in skills/snippets. Proposes updates or new skills, shows preview before changes. Use when user says "reflect", "what did we learn", or after complex tasks.
CRITICAL learning capture. Extracts HIGH/MED/LOW confidence patterns from conversations to prevent repeating mistakes and preserve what works. Use PROACTIVELY after user corrections ("no", "wrong"), after praise ("perfect", "exactly"), when discovering edge cases, or when skills are heavily used. Without reflection, valuable learnings are LOST forever. Acts as continuous improvement engine for all skills. Invoke EARLY and OFTEN - every correction is a learning opportunity.
Master skill for TypeScript backend development. Decision framework for APIs (tRPC/REST), authentication (Auth.js/Passport), database (Prisma), validation (Zod), logging (Pino), testing (Vitest), and deployment (Docker). Routes to specialized skills for implementation. Use as entry point for any backend task.
Transforms simple user requests into enriched descriptions with traceable findings. Use when interpreting vague user intent, preparing context for agent generation, or synthesizing actionable insights from available project context.
