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Found 1,211 Skills
Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate
Build AI-powered Ruby applications with RubyLLM. Full lifecycle - chat, tools, streaming, Rails integration, embeddings, and production deployment. Covers all providers (OpenAI, Anthropic, Gemini, etc.) with one unified API.
Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage. Summarizes patterns from DSV3, Qwen3, Qwen3-Next, and VLM bring-up work.
Build voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots, interactive voice characters, or any real-time voice conversation experience.
Working memory management, context prioritization, and knowledge retention patterns for AI agents. Use when you need to maintain relevant context and avoid information loss during long tasks.
INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).
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.
Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
Use when "LangChain", "LLM chains", "ReAct agents", "tool calling", or asking about "RAG pipelines", "conversation memory", "document QA", "agent tools", "LangSmith"
Set up and optimize context management for any project. Use this skill when the user says "set up context management", "optimize my CLAUDE.md", "context setup", "configure compact instructions", "set up rules", or when starting a new project and wanting best practices for long sessions, memory, compaction, and subagent delegation. Also trigger when the user mentions problems with context loss, compaction losing info, or sessions getting slow.
Use when entering orchestrator mode to manage agents via Paseo CLI
Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines