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Found 226 Skills
This skill generates a comprehensive set of Frequently Asked Questions (FAQs) from the course description, course content, learning graphs, concept lists, MicroSims, and glossary terms to help students understand common questions and prepare content for chatbot integration. Use this skill after course description, learning graph, glossary, and at least 30% of chapter content exist.
Retrieves implementation knowledge, code examples, and documentation references. Use to inform technical decision-making when the user requires specific library usage, framework patterns, or syntax details. Trigger on requests to 'search docs', 'find code examples', or 'check implementation details'.
Interact with the Denser Retriever API to build and query knowledge bases. Use this skill whenever the user wants to create a knowledge base, upload documents (files or URLs), search/query a knowledge base, list or delete knowledge bases or documents, check document processing status, or check account usage/balance. Also trigger when the user mentions 'denser retriever', 'knowledge base', 'document search', 'semantic search', 'RAG pipeline', or wants to index and search their files.
Comprehensive skill for Graphiti and Zep - temporal knowledge graph framework for AI agents with dynamic context engineering
Comprehensive skill for building, deploying, and managing multi-agent AI systems with Agno framework
AI Intelligent Email Assistant that analyzes email content to generate summaries, determines whether a reply is needed, and creates professional reply drafts based on context.
Prompt caching for Claude API to reduce latency by up to 85% and costs by up to 90%. Activate for cache_control, ephemeral caching, cache breakpoints, and performance optimization.
Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase search protocol). V2 includes L4/L5 retrieval constraints.
Look up Next.js documentation for a topic. Use before implementing any Next.js feature to get accurate, up-to-date framework knowledge.
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.