majiayu000/claude-skill-registry - 26페이지

prompt-review

.github/prompts/*.prompt.md をレビューして改善提案を出す。frontmatterの妥当性、入力変数(${input:...})の設計、出力フォーマット固定、instructions/skillsへの参照（重複排除）、tools最小化、例の有無、命名規則、壊れやすい指示の検出を行い、レポートをdocs/prompt-review-report.mdに出力する。

원문 언어: 일본어

prompt-section-design

Design composable prompt sections for building agentic prompts. Use when creating reusable prompt components, designing LEGO-block prompt sections, or structuring prompts for the stakeholder trifecta.

원문 언어: 영어

prompt-sensei

Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.

원문 언어: 영어

prompt-template-designer

Design reusable prompt templates that encode domain-specific patterns for recurring AI tasks. Use when you've executed similar prompts 2+ times and need to capture the pattern as reusable intelligence. NOT for one-off prompts or generic "ask AI a question"…

원문 언어: 영어

prompt-testing

A/B testing and performance metrics for prompts

원문 언어: 영어

prompt-versioning-management

プロンプトのライフサイクル管理を専門とするスキル。バージョン管理、デプロイ戦略、ロールバック、変更追跡により、本番環境で安全かつ効率的なプロンプト運用を実現します。 Anchors: • The Pragmatic Programmer (Andrew Hunt, David Thomas) / 適用: 手順設計と実践的改善 / 目的: 体系的なバージョン管理 • Continuous Delivery (Jez Humble) / 適用: デプロイパイプラインとロールバック戦略 / 目的:…

원문 언어: 일본어

promptcraft

Use when user asks to "create a skill", "make a command", "generate a prompt", "write a slash command", "build a Claude extension", or needs help crafting optimized skills and commands with proper frontmatter.

원문 언어: 영어

promptfoo

Promptfoo evaluation framework for testing and comparing LLM outputs. Use when writing eval configs, creating test cases, debugging eval runs, or working with assertions.

원문 언어: 영어

prompting-leitfaden

Prompting-Leitfaden für juristische KI-Nutzung in Kanzleien: Anwendungsfall Anwalt oder Mitarbeitende wollen KI effektiver nutzen und benoetigen praxiserprobte Prompt-Methoden. Mandantenkommunikation mit KI, Anwaltsgeheimnis beim Prompten. Prüfraster…

원문 언어: 독일어

provenance-audit

AI generation provenance and audit trail tracking. Records decision factors, data lineage, reasoning chains, confidence scoring, and cost tracking for AI-generated content.

원문 언어: 영어

proxy-mode-reference

Reference guide for using PROXY_MODE with external AI models. Use when running multi-model reviews, understanding which agents support PROXY_MODE, or debugging external model integration issues.

원문 언어: 영어

pydantic-ai-agent-builder

Expert guidance for building AI agents with Pydantic AI framework. Use when creating multi-agent systems, AI orchestration workflows, or structured LLM applications with type safety and validation.

원문 언어: 영어

pydantic-ai-agents

Build and debug Pydantic AI agents using best practices for dependencies, dynamic system prompts, tools, and structured output validation. Use when the user wants to: (1) Create a new Pydantic AI agent, (2) Debug or fix an existing agent, (3) Add features…

원문 언어: 영어

pydantic-ai

Build AI agents with PydanticAI. Type-safe agent framework with structured outputs, tools, and dependency injection. Use for production AI agents, type-safe LLM applications, and Python AI development.

원문 언어: 영어

pydantic-ai

"Build production AI agents with Pydantic AI: type-safe tools, structured output, embeddings, MCP, 30+ model providers, evals, graphs, and observability."

원문 언어: 영어

pydantic-evals

Test and evaluate AI agents and LLM outputs using code-first evaluation framework with strong typing. Use when the user wants to: (1) Create evaluation datasets with test cases for AI agents, (2) Define evaluators (deterministic, LLM-as-Judge, custom, or…

원문 언어: 영어

pydanticai-docs

Use this skill for requests related to Pydantic AI framework - building agents, tools, dependencies, structured outputs, and model integrations.

원문 언어: 영어

qdrant-memory

Use this skill for semantic search, long-term memory storage, and RAG (Retrieval Augmented Generation). Enables vector-based knowledge retrieval and persistent memory across sessions.

원문 언어: 영어

qdrant

Qdrant vector database REST API via curl. Use this skill to store, search, and manage vector embeddings.

원문 언어: 영어

query-decomposition

Query decomposition for multi-concept retrieval. Use when handling complex queries spanning multiple topics, implementing multi-hop retrieval, or improving coverage for compound questions.

원문 언어: 영어

qwen-delegation

Delegates tasks to Qwen CLI via delegation-core for Alibaba's models. Use when delegation-core selects Qwen or large-context batch processing is needed.

원문 언어: 영어

qwen-wsp-enhancement

Enhance WSP protocols using Qwen strategic analysis and 0102 supervision. Use when enhancing WSPs, analyzing protocol gaps, generating WSP recommendations, or coordinating multi-WSP updates.

원문 언어: 영어

rag-and-vector-search

Use when building RAG systems, implementing semantic/hybrid search, selecting vector databases, tuning retrieval quality, or choosing chunking and embedding strategies.

원문 언어: 영어

rag-architect

Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.

원문 언어: 영어

rag-architecture

Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.

원문 언어: 영어

rag-architecture-patterns

RAG (Retrieval-Augmented Generation) architecture patterns cover system design for building efficient, scalable, and accurate RAG systems. This skill covers data ingestion, chunking strategies, embedd

원문 언어: 영어

rag-engineer

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...

원문 언어: 영어

rag-evaluation

Comprehensive guide to evaluating Retrieval-Augmented Generation systems including retrieval metrics, generation quality, faithfulness, and end-to-end evaluation frameworks

원문 언어: 영어

rag-implementation

Comprehensive guide for Retrieval-Augmented Generation (RAG) implementation using LangChain. This skill covers the complete RAG pipeline from document processing and chunking, through embedding genera

원문 언어: 영어

rag-implementation

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization. Use when building RAG systems.

원문 언어: 영어

rag-implementation

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

원문 언어: 영어

rag-implementation

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

원문 언어: 영어

rag-patterns

Retrieval-Augmented Generation patterns and best practices. Implement chunking, embedding, retrieval, reranking, and generation pipelines. Use for knowledge-grounded AI, document QA, and semantic search applications.

원문 언어: 영어

rag-quality

RAG 시스템 품질 평가 및 개선을 위한 스킬입니다. RAGAS 기반 LLM-as-Judge 평가, 사용자 페르소나 시뮬레이션, 합성 데이터 생성, 평가 결과 저장 및 분석 기능을 제공합니다.

rag-retrieval

Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, constructing context from retrieved documents, adding citations, or implementing hybrid search.

원문 언어: 영어

rag-system-builder

Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.

원문 언어: 영어

rag-systems

Build Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, document search, knowledge bases, reducing hallucinations, and grounding LLM responses in factual data.

원문 언어: 영어

ralph-prompt-multi-task

Generate Ralph-compatible prompts for multiple related tasks. Creates phased prompts with sequential milestones, cumulative progress tracking, and phase-based completion promises. Use when creating prompts for CRUD implementations, multi-step features, staged…

원문 언어: 영어

ralph-prompt-project

Generate Ralph-compatible prompts for entire projects from scratch. Creates comprehensive prompts with architecture phase, implementation phases, testing, and documentation. Use when building complete applications, libraries, CLI tools, or any greenfield…

원문 언어: 영어

ralph-prompt-research

Generate Ralph-compatible prompts for research, analysis, and planning tasks. Creates prompts with systematic research phases, synthesis requirements, and deliverable specifications. Use when analyzing codebases, creating migration plans, researching…

원문 언어: 영어