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SkillsMP has collected 5,417 skills from majiayu000/claude-skill-registry. Open a skill to review its source and details.
majiayu000/claude-skill-registryShowing 40 of 5,417 collected skills.
Invokes Google Gemini models for structured outputs, multi-modal tasks, and Google-specific features. Use when users request Gemini, structured JSON output, Google API integration, or cost-effective parallel processing.
Iteration 5 of Multiplicity Cascade - Spawns meta-patterns from patterns
Iteration 6 of Multiplicity Cascade - Documentation that modifies itself when read
Iteration 7 of Multiplicity Cascade - Self-creating chaos engines
Iteration 8 of Multiplicity Cascade - Collides unrelated domains for emergence
Search the web using Kagi. Use for web searches with Quick Answer AI summaries.
Source text: Undetermined
Use when evaluating LLM-generated structured output against expected results using keyword matching and F1 metrics.
Kimi K2.5 setup and usage patterns. Most capable subagent with 256K context and built-in vision. Use for complex reasoning and batch image analysis.
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
Run LangChain Open Deep Research agent for iterative web research and comprehensive reports. Requires LLM API keys and search API (e.g., OPENAI_API_KEY, TAVILY_API_KEY).
Build LLM applications with LangChain. Create chains, agents, memory systems, and tool integrations. Use for conversational AI, document QA, and complex LLM orchestration.
LangChain is a framework for building applications powered by LLMs. It helps manage the complexity of prompt chaining, memory, retrieval, agents, and tool use, making it faster to build AI application
LangChain framework utilities for chains, agents, and RAG
Build production-ready LLM applications with chains, agents, memory, tools, and RAG pipelines using the LangChain framework
Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.
A powerful Python-based visual framework for building and deploying AI-powered agents and workflows with Model Context Protocol (MCP) integration, drag-and-drop interface, and enterprise-grade deployment options
Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Use when debugging AI pipelines, investigating errors, analyzing latency, managing prompt versions, or setting up Langfuse. Triggers on "langfuse", "traces", "debug AI",…
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
Integrate Langfuse observability with AWS Strands Agents for comprehensive tracing, monitoring, and debugging of AI agent applications. Use when building Strands agents that need production observability, when debugging agent behavior, when tracking…
Design and build AI agents with LangGraph. Use when building ReAct agents, multi-agent systems, workflow orchestration, human-in-the-loop patterns, or state machine workflows.
LangGraph conditional routing patterns. Use when implementing dynamic routing based on state, creating branching workflows, or building retry loops with conditional edges.
LangGraph workflow patterns for agent orchestration
LangGraph state management patterns. Use when designing workflow state schemas, using TypedDict vs Pydantic, implementing accumulating state with Annotated operators, or managing shared state across nodes.
LangGraph supervisor-worker pattern. Use when building central coordinator agents that route to specialized workers, implementing round-robin or priority-based agent dispatch.
LangSmith trace validation for RAG observability - every query must be traced
Retrieval-augmented Lean4 proof generation. Queries 94k+ exemplars from DeepSeek-Prover V1+V2, uses hybrid search (BM25 + semantic + graph), generates via Claude, compiles in Docker, retries on failure.
ai-integration for learning technology evaluation and implementation.
When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.
Unified LLM API with LiteLLM. Call 100+ LLM providers with one interface. Use for multi-provider AI, cost optimization, fallbacks, and LLM gateway deployment.
Build LLM applications with LlamaIndex. Create indexes, query engines, and data connectors. Use for RAG applications, document search, and knowledge base systems.
LlamaIndex Wolfram Alpha tool for computational knowledge queries, math solving, scientific calculations, and agent integration. Triggers: wolfram alpha, computational query, math solver, scientific calculation, WolframAlphaToolSpec.
Implement large language model (LLM) chat completions using the z-ai-web-dev-sdk. Use this skill when the user needs to build conversational AI applications, chatbots, AI assistants, or any text generation features. Supports multi-turn conversations, system…
Use when building LLM applications: prompt engineering, structured output, agents, RAG integration, memory management, or production deployment. Framework-agnostic patterns using raw SDK calls.
Implement multi-layer LLM caching with exact match, semantic similarity, and provider-side prompt caching. Reduce API costs by 30–70%, cut latency, and improve throughput using Redis, GPTCache, and provider caching APIs.
Write effective LLM prompts, commands, and agent instructions. Goal-oriented over step-prescriptive. Role + Objective + Latitude pattern. Use when writing prompts, designing agents, building Claude Code commands, or reviewing LLM instructions. Keywords:…
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
Patterns for building LLM applications - prompt engineering, RAG pipelines, cost optimization, multi-model routing, and evaluation. Auto-triggers when working with AI/LLM code.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.