Browse 138,453 skills across 70 categories. Mirrored from majiayu000/claude-skill-registry.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques includ...
This skill should be used when building production LLM applications in any language. It applies when implementing predic...
Use when building LLM applications: prompt engineering, structured output, agents, RAG integration, memory management, o...
This skill should be used when users want to build LLM-powered applications using LangChain. It provides patterns for in...
Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system...
Use when user needs LLM system architecture, model deployment, optimization strategies, and production serving infrastru...
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and...
Execute programs on a compiled transformer stack machine where every instruction fetch and memory read is a parabolic at...
Implement multi-layer LLM caching with exact match, semantic similarity, and provider-side prompt caching. Reduce API co...
Multi-level caching strategies for LLM applications - semantic caching (Redis), prompt caching (Claude/OpenAI native), c...
External LLM invocation. Triggered ONLY by @council,@probe,@crossref,@gpt,@gemini,@grok,@qwen.
Instrument LLM API calls with proper spans, tokens, and latency
LLM-based zero-shot and few-shot classification for flexible intent detection
Centralized AI-readable documentation repository with 245+ frameworks and tools. Use to find documentation, add new sour...
Write effective LLM prompts, commands, and agent instructions. Goal-oriented over step-prescriptive. Role + Objective +...
Analyze llm-compact-logger test output and configure enhancements. Use when user shares debug-compact.json or debug-repo...
Guidelines for working with LLM context stored in the .llm/ directory.
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and se...
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
Reduce LLM API costs without sacrificing quality. Covers prompt caching (Anthropic), local response caching, prompt comp...
Use when you need to reduce LLM API spend, control token usage, route between models by cost/quality, implement prompt c...
Multi-LLM collaborative brainstorming and planning. Use when user explicitly requests consultation with multiple AI mode...
Orchestrate a configurable, multi-member CLI planning council (Codex, Claude Code, Gemini, OpenCode, or custom) to produ...
Orchestrate multiple LLMs as a council, generating collective intelligence through peer review and chairman synthesis
This skill should be used when Claude needs to "formulate a request for external feedback", "craft a question for ChatGP...
Automate construction data processing using LLM (ChatGPT, Claude, LLaMA). Generate Python/Pandas scripts, extract data f...
Diagnoses LLM output failures including hallucinations, constraint violations, format errors, and reasoning issues. Prov...
Write token-efficient documentation for LLM context. Use when creating CLAUDE.md, README, technical docs, agent instruct...
Fetch LLM-optimized documentation from llms.txt endpoints for up-to-date API references
Optimize documentation for AI coding assistants and LLMs. Improves docs for Claude, Copilot, and other AI tools through...
Extract structured data from construction documents using LLMs. Process RFIs, submittals, contracts, specifications. Con...
Patterns for building LLM applications - prompt engineering, RAG pipelines, cost optimization, multi-model routing, and...
LLM生成システムの検証設計スキル。assay-kitフレームワークを活用し、LLM特有の失敗モード（幻覚、例への過学習、部分的処理）を考慮した総合的なテストケース設計を支援する。使用タイミング:- LLMベースのワークフロー/エージェント...
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmar...
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B test...
LLM testing and evaluation tools including promptfoo, trulens, and evals frameworks
LoRA/QLoRA/PEFT fine-tuning workflows, dataset formatting, adapter merging, and eval loops. Covers Hugging Face TRL/PEFT...
Set up infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and d...
Fine-tune large language models efficiently using LoRA, QLoRA, and PEFT methods. Use for domain adaptation, instruction...
LLM Fine-Tuning expert. Covers LoRA, QLoRA, PEFT, dataset preparation, Hugging Face Trainer/TRL, RLHF, DPO, quantization...
LLM function calling (also known as tool use) enables Large Language Models to interact with external systems by calling...
Implementing function calling (tool use) with LLMs for structured outputs and external integrations.
LLM-powered quality verification using prompt hooks. Validates commit messages, code patterns, and conventions using AI...
LLM gateway and routing configuration using OpenRouter and LiteLLM. Invoke when: - Setting up multi-model access (OpenRo...
LLM content governance and compliance standards. Use when llm governance guidance is required.
Comprehensive guide for LLM safety and guardrails implementation.