majiayu000/claude-skill-registry - 21ページ

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.

原文の言語: 英語

meta-pattern-spawner

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

原文の言語: 英語

self-modifying-docs

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

原文の言語: 英語

autopoietic-chaos

Iteration 7 of Multiplicity Cascade - Self-creating chaos engines

原文の言語: 英語

cross-domain-collider

Iteration 8 of Multiplicity Cascade - Collides unrelated domains for emergence

原文の言語: 英語

kagi-search

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

原文の言語: 判定不能

keyword-based-llm-eval

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

原文の言語: 英語

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.

原文の言語: 英語

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.

原文の言語: 英語

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).

原文の言語: 英語

langchain

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

原文の言語: 英語

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

原文の言語: 英語

langchain-tools

LangChain framework utilities for chains, agents, and RAG

原文の言語: 英語

langchain

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

原文の言語: 英語

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.

原文の言語: 英語

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

原文の言語: 英語

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",…

原文の言語: 英語

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.

原文の言語: 英語

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…

原文の言語: 英語

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.

原文の言語: 英語

langgraph-routing

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

原文の言語: 英語

langgraph

LangGraph workflow patterns for agent orchestration

原文の言語: 英語

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.

原文の言語: 英語

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.

原文の言語: 英語

langsmith-testing

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

原文の言語: 英語

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.

原文の言語: 英語

learning-ai-integration

ai-integration for learning technology evaluation and implementation.

原文の言語: 英語

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.

原文の言語: 英語

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.

原文の言語: 英語

llamaindex

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

原文の言語: 英語

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.

原文の言語: 英語

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…

原文の言語: 英語

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.

原文の言語: 英語

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.

原文の言語: 英語

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:…

原文の言語: 英語

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.

原文の言語: 英語

llm-and-ai-cost-optimization

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

原文の言語: 英語

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.

原文の言語: 英語

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.

原文の言語: 英語

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.

原文の言語: 英語