> browse/registry

> ai-engineer

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

> llm-ops

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

> recallmax

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

> ai-engineering-toolkit

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

> redis-development

Redis performance optimization and best practices. Use this skill when working with Redis data structures, Redis Query Engine (RQE), vector search with RedisVL, semantic caching with LangCache, or optimizing Redis performance.

> turso

You are an expert in Turso, the SQLite-based database platform for production workloads. You help developers use libSQL (Turso's SQLite fork) as a primary database with features like embedded replicas (SQLite file synced from cloud), multi-region replication, vector search, branching, and edge deployment — providing sub-millisecond reads with the simplicity of SQLite and the durability of a cloud database.

> convex

Assists with building real-time reactive backends using Convex. Use when creating databases with automatic client sync, reactive queries, file storage, scheduled functions, or full-text and vector search. Trigger words: convex, reactive backend, real-time database, useQuery, useMutation, convex functions, convex schema.

> mongodb

Assists with designing document schemas, building aggregation pipelines, managing indexes, and operating MongoDB clusters. Use when working with flexible schemas, nested documents, horizontal scaling, Atlas Search, or vector search for AI applications. Trigger words: mongodb, mongo, document database, aggregation, atlas, nosql.

> ollama

Run LLMs locally with Ollama. Use when a user asks to run AI models locally, self-host a language model, use LLaMA or Mistral on their machine, run offline AI, build a local chatbot, avoid sending data to cloud AI providers, generate text without API costs, fine-tune or customize local models, or set up a private AI inference server. Covers model management, API usage, Modelfile customization, GPU acceleration, and integration with LangChain and other frameworks.

> crewai

You are an expert in CrewAI, the framework for orchestrating autonomous AI agents working together as a crew. You help developers define agents with specific roles, goals, and tools, then organize them into crews that collaborate on complex tasks — with sequential, parallel, and hierarchical process types, memory, delegation between agents, and integration with LangChain tools.

> pgvector

Store and search vector embeddings in PostgreSQL with pgvector — no separate vector database needed. Use when someone asks to "vector search in Postgres", "store embeddings", "pgvector", "similarity search", "RAG with Postgres", "semantic search in existing database", or "add AI search to my app without a separate vector DB". Covers vector columns, indexing (IVFFlat, HNSW), similarity search, and integration with ORMs.

> traceloop

You are an expert in Traceloop and its OpenLLMetry SDK, the open-source observability framework that extends OpenTelemetry for LLM applications. You help developers instrument AI pipelines with automatic tracing for OpenAI, Anthropic, Cohere, LangChain, LlamaIndex, vector databases, and frameworks — exporting to any OpenTelemetry-compatible backend (Grafana Tempo, Jaeger, Datadog, Honeycomb, Traceloop Cloud).

> langtrace

You are an expert in Langtrace, the open-source observability platform for LLM applications built on OpenTelemetry. You help developers trace LLM calls, RAG pipelines, agent tool use, and chain executions with automatic instrumentation for OpenAI, Anthropic, LangChain, LlamaIndex, and 20+ providers — providing cost tracking, latency analysis, token usage, and quality evaluation in a self-hostable dashboard.

> lancedb

Embedded vector database with LanceDB — serverless, zero-config vector search for AI applications. Use when someone asks to "vector search without a server", "embedded vector database", "LanceDB", "local vector search", "serverless vector DB", "vector search in a file", or "lightweight RAG storage". Covers table creation, vector search, full-text search, hybrid search, and multimodal embeddings.

> langchain

Build LLM-powered applications with LangChain. Use when a user asks to create AI chains, build RAG pipelines, implement agents with tools, set up document loaders, create vector stores, build conversational AI, implement prompt templates, chain LLM calls, add memory to chatbots, or orchestrate language model workflows. Covers LangChain v0.3+ with LCEL (LangChain Expression Language), structured output, tool calling, retrieval, and production deployment patterns.

> llamaindex

Assists with building RAG pipelines, knowledge assistants, and data-augmented LLM applications using LlamaIndex. Use when ingesting documents, configuring retrieval strategies, building query engines, or creating multi-step agents. Trigger words: llamaindex, rag, retrieval augmented generation, vector index, query engine, document loader, knowledge base.

> orama

Expert guidance for Orama, the fast full-text and vector search engine that runs everywhere — browser, server, and edge. Helps developers implement search with typo tolerance, facets, filters, and hybrid (keyword + vector) search without external infrastructure.

> qdrant

You are an expert in Qdrant, the high-performance vector search engine written in Rust. You help developers build semantic search, RAG retrieval, recommendation systems, and anomaly detection with billion-scale vector collections, advanced filtering, multi-vector support, and hybrid search — providing sub-millisecond query latency with rich payload filtering that other vector DBs can't match.

> llm-application-dev-langchain-agent

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

> web-search-plus

Unified search skill with Intelligent Auto-Routing. Uses multi-signal analysis to automatically select between Serper (Google), Tavily (Research), Exa (Neural), Perplexity (AI Answers), You.com (RAG/Real-time), and SearXNG (Privacy/Self-hosted) with confidence scoring.