meta-cognitive-reasoning
Meta-cognitive reasoning specialist for evidence-based analysis, hypothesis testing, and cognitive failure prevention. Use when conducting reviews, making assessments, debugging complex issues, or any task requ...
Discover testing security knowledge base skills, compare source repositories and read the instructions for each workflow.
Meta-cognitive reasoning specialist for evidence-based analysis, hypothesis testing, and cognitive failure prevention. Use when conducting reviews, making assessments, debugging complex issues, or any task requ...
Automatically activated when user asks "what's the best way to...", "how should I...", "what are best practices for...", or requests recommendations, industry standards, or proven approaches for implementing fe...
Expert at analyzing the quality of Claude's responses and outputs. Use when evaluating response completeness, accuracy, clarity, or effectiveness. Auto-invokes during self-reflection or when quality assessment...
Interactive, multiple-choice interview for requirements discovery and spec clarification; turns an underspecified idea (or draft spec) into an execution-ready spec with decisions, assumptions, acceptance criter...
Transform AI-assisted drafts into authentic, human-sounding content. This skill provides patterns to detect and eliminate AI tells, frameworks for natural writing, and techniques for creating prose that reads a...
Use PROACTIVELY to document new features, significant code changes, or when creating technical specifications for a feature not included in the original project technical specification. Specializes in creating...
Recover credibility after mistakes: incident comms, restitution, roadmap resets, and monitoring sentiment. Use after exploits, missteps, or comms errors.
Drops random fun facts, jokes, stories, or wild ideas tailored to tech wizards like you
Automatically activated when user asks how something works, wants to understand unfamiliar code, needs to explore a new codebase, or asks questions like "where is X implemented?", "how does Y work?", or "explai...
Automated 2-AI verification result documentation and export workflow. Triggers when user requests AI verification report export, documentation of findings, or saving cross-validation results. Use after completi...
知识整理和文档维护
Fetch LLM-optimized documentation for Vercel, the frontend cloud platform.
Fetch LLM-optimized documentation for Clerk, the complete user management platform.
Web scraping, content extraction, and autonomous research agent for deep web investigation.
Deep technical research using Firecrawl Agent for autonomous web investigation, competitive analysis, and implementation pattern discovery.
Structured workflow for co-authoring documentation, PRDs, technical specs, and decision docs. Use when writing substantial documentation, creating proposals, drafting specifications, or any structured content t...
Meta-skill for creating and improving agent skills. Use when designing new skills, refining existing skills, or understanding skill architecture patterns. Provides progressive disclosure, context efficiency pri...
Fetch LLM-optimized documentation for Supabase, the open-source Firebase alternative.
知識管理:第二大腦、筆記系統、資訊整理、數位工具流
Deep research and information gathering specialist for thorough investigation, pattern analysis, and knowledge synthesis
Build searchable knowledge bases from document collections (PDFs, Word, text files). Use for creating technical libraries, standards repositories, research databases, or any large document collection requiring...
Setup vector embeddings and semantic search for document collections. Use for AI-powered similarity search, finding related documents, and preparing knowledge bases for RAG systems.
Score task complexity using keyword matching and heuristics
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.