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Found 1,301 Skills
Provides patterns to build Retrieval-Augmented Generation (RAG) systems for AI applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
PyTorch implementation of TurboQuant for LLM KV cache compression using two-stage vector quantization (random rotation + Lloyd-Max + QJL residual correction).
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM).
Test if user signup is open and identify potential abuse vectors in the registration process.
Configure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.
SQL Server 2025 and SqlPackage 170.2.70 (October 2025) - Vector databases, AI integration, and latest features
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when "building RAG, vector search, embeddings, semantic search, document retrieval, context retrieval, knowledge base, LLM with documents, chunking strategy, pinecone, weaviate, chromadb, pgvector, rag, embeddings, vector-database, retrieval, semantic-search, llm, ai, langchain, llamaindex" mentioned.
Use when user needs Active Directory security analysis, privileged group design review, authentication policy assessment, or delegation and attack surface evaluation across enterprise domains.
Migrate phase directories to globally sequential numbering, fixing duplicate numeric prefixes across milestones. Triggers include "migrate phases", "fix phase numbers", "renumber phases", "phase collision", "fix phase collisions", "fix duplicate phases", "phase numbering migration".
Implement OpenTelemetry (OTEL) observability - Collector configuration, Kubernetes deployment, traces/metrics/logs pipelines, instrumentation, and troubleshooting. Use when working with OTEL Collector, telemetry pipelines, observability infrastructure, or Kubernetes monitoring.
This skill should be used when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph", "track entities", or mentions memory architecture, temporal knowledge graphs, vector stores, entity memory, or cross-session persistence.