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Found 331 Skills
Implement Corrective RAG (CRAG) with retrieval validation, fallback strategies, and self-correction. Use this skill when RAG outputs need quality guarantees and automatic error correction. Activate when: CRAG, corrective RAG, retrieval validation, fallback search, self-correcting RAG, grounded generation.
Use OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches. Ideal for RAG and vector retrieval pipelines in Claude Code/Codex.
Generate text embeddings and rerank documents via Together AI. Embedding models include BGE, GTE, E5, UAE families. Reranking via MixedBread reranker. Use when users need text embeddings, vector search, semantic similarity, document reranking, RAG pipeline components, or retrieval-augmented generation.
Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.
Build stateful chatbots with OpenAI Assistants API v2 - Code Interpreter, File Search (10k files), Function Calling. Prevents 10 documented errors including vector store upload bugs, temperature parameter conflicts, memory leaks. Deprecated (sunset August 2026); use openai-responses for new projects. Use when: maintaining legacy chatbots, implementing RAG with vector stores, or troubleshooting thread errors, vector store delays, uploadAndPoll issues.
LLM prompt testing, evaluation, and CI/CD quality gates using Promptfoo. Invoke when: - Setting up prompt evaluation or regression testing - Integrating LLM testing into CI/CD pipelines - Configuring security testing (red teaming, jailbreaks) - Comparing prompt or model performance - Building evaluation suites for RAG, factuality, or safety Keywords: promptfoo, llm evaluation, prompt testing, red team, CI/CD, regression testing
Build AI agents with persistent threads, tool calling, and streaming on Convex. Use when implementing chat interfaces, AI assistants, multi-agent workflows, RAG systems, or any LLM-powered features with message history.
Comprehensive skill for Microsoft GraphRAG - modular graph-based RAG system for reasoning over private datasets
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Coaches end-to-end ML system design interviews covering inference pipelines, recommendation systems, RAG, feature stores, and monitoring. Use for L6+ design rounds, ML architecture whiteboarding, system design practice, serving tradeoff analysis. Activate on "ML system design", "ML interview", "recommendation system design", "RAG architecture", "feature store design", "model serving". NOT for coding interviews, behavioral questions, ML theory quizzes, or paper implementations.
Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.
Implement AI Coaching best practices on AnalyticDB for PostgreSQL (ADBPG): Leverage Supabase projects (training data management) + ADBPG instances with vector optimization to build RAG-driven coaching systems that guide users through domain-specific workflows, decision-making, or skill development. Use when: User wants to create Supabase projects (spb-xxx), ADBPG instances (gp-xxx), vector knowledge bases, or RAG-driven coaching systems on ADBPG. Triggers: "Supabase", "ADBPG", "vector database", "knowledge base", "RAG", "AI coaching", "coaching system", "spb-xxx", "gp-xxx"