LLM & Agents

Frontend testing rules with Vitest, React Testing Library, and MSW. Includes coverage requirements, test design principles, and quality criteria. Use when writing frontend tests or reviewing test quality.

Guide for creating Claude Code slash commands. Use when the user wants to create a new slash command, update an existing slash command, or asks about slash command syntax, frontmatter options, or best practices.

Metacognition skill for AI agents. Use when starting work, feeling stuck, output feels off, or before complex tasks. Teaches how to think about thinking.

Testing pyramid and test writing standards for UT/IT/ST/E2E. Use when: writing tests, discussing test coverage, test strategy, or test naming. Keywords: test, unit, integration, e2e, coverage, mock, 測試, 單元, 整合, 端對端.

Guide requirement writing, user story creation, and feature specification. Use when: writing requirements, user stories, issues, feature planning. Keywords: requirement, user story, issue, feature, specification, 需求, 功能規劃, 規格.

Systematic code review checklist and pre-commit quality gates for PRs. Use when: reviewing pull requests, checking code quality, before committing code. Keywords: review, PR, pull request, checklist, quality, commit, 審查, 檢查, 簽入.

Guide OAK constitution maintenance with amendment workflows, validation frameworks, semantic versioning, and agent instruction synchronization.

Multi-agent coordination with Agent Mail MCP. Use when registering agents, reserving files, sending messages, coordinating with other agents, or when the user mentions "agent mail", "coordination", "file reservation", or "multi-agent".

Run an evidence-seeking calibration roundtable to realign the plan with the North Star. Use when pausing between phases, when agents disagree, when reviewing work, when the user mentions "calibrate" or "realign", or when making decisions that affect the plan.

New agent startup checklist for Agent Mail and Beads. Use when starting a new agent session, when the user says "prime" or "startup", or when beginning work on a multi-agent project.

自动同步项目中的 Agents.md、claude.md 和 gemini.md 文件，保持内容一致性。支持自动监听和手动触发。

Resolve disagreements between agents or approaches using test-based adjudication. Use when agents disagree, when multiple valid approaches exist, when the user asks "which approach", or when making architectural decisions with tradeoffs.

Skill for creating and editing Youtube thumbnails that are optimized for click-through rate. This skill should not be used directly, instead use the Thumbnail Designer subagent who can also invoke this skill. Use when the user asks to create a thumbnail from scratch or edit an existing thumbnail.

AWS Bedrock AgentCore comprehensive expert for deploying and managing all AgentCore services. Use when working with Gateway, Runtime, Memory, Identity, or any AgentCore component. Covers MCP target deployment, credential management, schema optimization, runtime configuration, memory management, and identity services.

Generate a project-specific AGENTS.md from a user goal, then confirm before overwriting.

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

This skill should be used when creating, optimizing, or implementing advanced prompt patterns including few-shot learning, chain-of-thought reasoning, prompt optimization workflows, template systems, and system prompt design. It provides comprehensive frameworks for building production-ready prompts with measurable performance improvements.

Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.

Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.

Tool and function calling patterns with LangChain4j. Define tools, handle function calls, and integrate with LLM agents. Use when building agentic applications that interact with tools.