majiayu000/claude-skill-registry - Página 21

invoking-gemini

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.

Idioma del texto original: inglés

meta-pattern-spawner

Iteration 5 of Multiplicity Cascade - Spawns meta-patterns from patterns

Idioma del texto original: inglés

self-modifying-docs

Iteration 6 of Multiplicity Cascade - Documentation that modifies itself when read

Idioma del texto original: inglés

autopoietic-chaos

Iteration 7 of Multiplicity Cascade - Self-creating chaos engines

Idioma del texto original: inglés

cross-domain-collider

Iteration 8 of Multiplicity Cascade - Collides unrelated domains for emergence

Idioma del texto original: inglés

kagi-search

Search the web using Kagi. Use for web searches with Quick Answer AI summaries.

Idioma del texto original: Sin determinar

keyword-based-llm-eval

Use when evaluating LLM-generated structured output against expected results using keyword matching and F1 metrics.

Idioma del texto original: inglés

kimi-k2-5

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.

Idioma del texto original: inglés

langchain-architecture

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.

Idioma del texto original: inglés

langchain-deep-research

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).

Idioma del texto original: inglés

langchain

Build LLM applications with LangChain. Create chains, agents, memory systems, and tool integrations. Use for conversational AI, document QA, and complex LLM orchestration.

Idioma del texto original: inglés

langchain-patterns

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

Idioma del texto original: inglés

langchain-tools

LangChain framework utilities for chains, agents, and RAG

Idioma del texto original: inglés

langchain

Build production-ready LLM applications with chains, agents, memory, tools, and RAG pipelines using the LangChain framework

Idioma del texto original: inglés

langchain4j-spring-boot-integration

Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.

Idioma del texto original: inglés

langflow

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

Idioma del texto original: inglés

langfuse

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",…

Idioma del texto original: inglés

langfuse-observability

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.

Idioma del texto original: inglés

langfuse-strands

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…

Idioma del texto original: inglés

langgraph-python

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.

Idioma del texto original: inglés

langgraph-routing

LangGraph conditional routing patterns. Use when implementing dynamic routing based on state, creating branching workflows, or building retry loops with conditional edges.

Idioma del texto original: inglés

langgraph

LangGraph workflow patterns for agent orchestration

Idioma del texto original: inglés

langgraph-state

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.

Idioma del texto original: inglés

langgraph-supervisor

LangGraph supervisor-worker pattern. Use when building central coordinator agents that route to specialized workers, implementing round-robin or priority-based agent dispatch.

Idioma del texto original: inglés

langsmith-testing

LangSmith trace validation for RAG observability - every query must be traced

Idioma del texto original: inglés

lean4-prove

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.

Idioma del texto original: inglés

learning-ai-integration

ai-integration for learning technology evaluation and implementation.

Idioma del texto original: inglés

litellm

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.

Idioma del texto original: inglés

litellm

Unified LLM API with LiteLLM. Call 100+ LLM providers with one interface. Use for multi-provider AI, cost optimization, fallbacks, and LLM gateway deployment.

Idioma del texto original: inglés

llamaindex

Build LLM applications with LlamaIndex. Create indexes, query engines, and data connectors. Use for RAG applications, document search, and knowledge base systems.

Idioma del texto original: inglés

llamaindex-wolfram-alpha

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.

Idioma del texto original: inglés

llm

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…

Idioma del texto original: inglés

llm-application-patterns

Use when building LLM applications: prompt engineering, structured output, agents, RAG integration, memory management, or production deployment. Framework-agnostic patterns using raw SDK calls.

Idioma del texto original: inglés

llm-caching

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.

Idioma del texto original: inglés

llm-communication

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:…

Idioma del texto original: inglés

llm-cost-optimization

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.

Idioma del texto original: inglés

llm-and-ai-cost-optimization

Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.

Idioma del texto original: inglés

llm-engineering

Patterns for building LLM applications - prompt engineering, RAG pipelines, cost optimization, multi-model routing, and evaluation. Auto-triggers when working with AI/LLM code.

Idioma del texto original: inglés

llm-evaluation

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.

Idioma del texto original: inglés

llm-evaluation

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.

Idioma del texto original: inglés