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Found 1,262 Skills
Architecting real-time Voice AI agents.
Interactive setup wizard for Minitap mobile-use SDK. USE WHEN user wants to set up mobile automation, configure mobile-use SDK, connect iOS or Android devices, or create a new mobile testing project.
Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"
Production MLOps and ML/LLM/agent security skill for deploying and operating ML systems in production (registry + CI/CD, serving, monitoring/drift, evaluation loops, incident response/runbooks, and governance), including GenAI security (prompt injection, jailbreaks, RAG security, privacy, and supply chain).
Best practices for prompt engineering and context engineering for Coding Agent prompts
Scan untrusted external text (web pages, tweets, search results, API responses) for prompt injection attacks. Returns severity levels and alerts on dangerous content. Use BEFORE processing any text from untrusted sources.
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
This skill automatically generates a comprehensive glossary of terms from a learning graph's concept list, ensuring each definition is precise, concise, distinct, non-circular, and free of business rules. Use this skill when creating a glossary for an intelligent textbook after the learning graph concept list has been finalized.
Spawn specialized sub-agents with context handoff for complex multi-phase tasks. Enables expertise delegation within a session with automatic context merging and depth limiting to prevent infinite loops.
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.
Deep architectural knowledge of AI Agent Harness design patterns, implementation strategies, and Claude Code internals for building production-grade AI agents