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Found 223 Skills
MUST READ before creating or enhancing any ADK agent project. Use when the user wants to build a new agent (e.g. "build me a search agent") or enhance an existing project (e.g. "add CI/CD to my project", "add RAG").
25 advanced POWERFUL-tier engineering skills covering agent design, RAG architecture, MCP servers, CI/CD pipelines, database design, observability, security auditing, release management, and platform operations. Works with Claude Code, Codex CLI, and OpenClaw.
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.
LangChain LLM application framework with chains, agents, RAG, and memory for building AI-powered applications
Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.
Manage Alibaba Cloud RDS Supabase (RDS AI Service 2025-05-07) via OpenAPI. It is used for creating, starting/stopping/restarting instances, resetting passwords, querying endpoints, authentication and storage information, configuring authentication, RAG, SSL and IP whitelist, as well as listing instance details or conversations.
Use this skill when crafting LLM prompts, implementing chain-of-thought reasoning, designing few-shot examples, building RAG pipelines, or optimizing prompt performance. Triggers on prompt design, system prompts, few-shot learning, chain-of-thought, prompt chaining, RAG, retrieval-augmented generation, prompt templates, structured output, and any task requiring effective LLM interaction patterns.
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.