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Found 1,233 Skills
Multi-model deep review of the Ralph bd graph and plan via three parallel opencode processes (claude opus, gemini, gpt). Use for high-stakes runs where cross-model consensus reduces single-model bias.
Build hierarchical memory systems for AI agents using GAM (General Agentic Memory) with text, video, and long-horizon trajectory support
Build and deploy autonomous AI agents with CowAgent - planning, memory, knowledge base, skills, and multi-channel support
Builds a content-addressed semantic-unit index from source code for structural context. Use when a pinned or live codebase is available and structural cross-reference data would improve research quality. Don't use for findings analysis, patching, or reporting.
Market intelligence, competitive analysis, technical evaluations, and technology decisions. Use when researching companies, analyzing competitors, evaluating frameworks, or making tech stack decisions.
This skill should be used when the user asks to "humanize text", "make this sound more human", "detect AI writing", "fix AI-sounding content", "copy edit for naturalness", "rewrite to sound less robotic", "check if this sounds AI-generated", or needs guidance on making written content feel authentically human while preserving its original tone.
Use when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing
Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.
Token optimization best practices for MCP server and tool interactions. Minimizes token consumption while maintaining effectiveness. USE WHEN: user mentions "token usage", "optimize tokens", "reduce API calls", "MCP efficiency", asks about "how to use less tokens", "MCP best practices", "limit output size", "efficient queries" DO NOT USE FOR: Code optimization - use `performance` instead, Text compression - this is about API usage patterns, Cost optimization (infrastructure) - use cloud/DevOps skills
Generative Engine Optimization (GEO) — make content rank in AI search answers from ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Audits existing content, rewrites for AI citation, and produces per-engine strategy. Use when asked to "optimize for AI search", "rank in ChatGPT", "GEO audit", "improve AI citations", "rank in Perplexity", "AI Overview optimization", "AI Overview ranking", "LLM SEO", "answer engine optimization", "AEO", "get cited by AI", "GEO", "generative engine optimization", "show up in ChatGPT", "appear in AI answers", "be cited by Perplexity", "SGE optimization", "Search Generative Experience", or "make my content show up in AI answers". Distinct from regular SEO — this targets generative engines, not traditional Google rankings.
Generate a periodic knowledge digest — a human-readable newsletter-style summary of what was learned, updated, and connected in your wiki over a specified period (day/week/month). Use when the user says "what did I learn this week", "give me a digest", "weekly summary", "knowledge report", "what's new in my wiki", "/wiki-digest [period]", "summarize my recent learning", or wants a readable overview of recent wiki activity. Distinct from wiki-status (which reports ingestion delta of sources) — wiki-digest summarizes *knowledge*, not sources.
Two-tier memory system that makes Claude a true workplace collaborator. Decodes shorthand, acronyms, nicknames, and internal language so Claude understands requests like a colleague would. CLAUDE.md for working memory, memory/ directory for the full knowledge base.