Catalog
All AI skills
28 skills. Every one is readable here; downloads and live use need a subscription.
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.
azure-cosmos-db-py
Build production-grade Azure Cosmos DB NoSQL services following clean code, security best practices, and TDD principles.
vector-database-engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
vector-index-tuning
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
awt-e2e-testing
AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g
azure-cosmos-java
Azure Cosmos DB SDK for Java. NoSQL database operations with global distribution, multi-model support, and reactive patterns.
azure-cosmos-py
Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data.
azure-cosmos-rust
Azure Cosmos DB SDK for Rust (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data.
azure-cosmos-ts
Azure Cosmos DB JavaScript/TypeScript SDK (@azure/cosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management.
azure-data-tables-java
Build table storage applications using the Azure Tables SDK for Java. Works with both Azure Table Storage and Cosmos DB Table API.
azure-data-tables-py
Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.
azure-resource-manager-cosmosdb-dotnet
Azure Resource Manager SDK for Cosmos DB in .NET.
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
arrowspace
Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search.
azure-search-documents-py
Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.
azure-search-documents-ts
Build search applications with vector, hybrid, and semantic search capabilities.
context-manager
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.
draw
Vector graphics and diagram creation, format conversion (ODG/SVG/PDF) with LibreOffice Draw.
embedding-strategies
Guide to selecting and optimizing embedding models for vector search applications.
hybrid-search-implementation
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
rag-engineer
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.
rag-implementation
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
vexor
Vector-powered CLI for semantic file search with a Claude/Codex skill
weaviate
Search, query, inspect, create, and import data into Weaviate vector database collections using official scripts and references.
weaviate-cookbooks
Build Weaviate AI apps from official cookbook blueprints for RAG, agentic RAG, data exploration, multimodal PDF search, async clients, and frontends.