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majiayu000/claude-skill-registry - Page 26

SkillsMP has collected 5,417 skills from majiayu000/claude-skill-registry. Open a skill to review its source and details.

majiayu000/claude-skill-registry

Showing 40 of 5,417 collected skills.

occupation
Software Developers
description

.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。

Source text: Japanese

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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.

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occupation
Project Management Specialists
description

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"…

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occupation
Software Quality Assurance Analysts & Testers
description

A/B testing and performance metrics for prompts

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occupation
Software Developers
description

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:…

Source text: Japanese

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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

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occupation
Lawyers
description

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…

Source text: German

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occupation
Data Scientists
description

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

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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…

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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

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occupation
Software Quality Assurance Analysts & Testers
description

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…

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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

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occupation
Software Quality Assurance Analysts & Testers
description

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

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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.

Source text: Korean

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occupation
Software Developers
description

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

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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.

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occupation
Software Developers
description

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…

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occupation
Software Developers
description

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…

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occupation
Software Developers
description

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…

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Showing 40 of 5,417 collected skills.