Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Operate Codex as a high-autonomy repository executor for implementation, debugging, and long-running research loops. Use when work requires strict git discipline (init, pull, commit, optional push), repeated continuation until the objective is fully complete, and strong runtime validation such as smoke runs (at least 100 steps), checkpoint integrity checks, and basic inference verification before stopping.
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
Optimizes raw idea descriptions into structured prompts ready for the brainstorming workflow. TRIGGER when: user says "optimize for brainstorm", "prepare idea for brainstorm", "enhance this idea", "make this ready for brainstorming", "imposta per brainstorm", or wants to improve a feature idea before using /specs.brainstorm. DO NOT TRIGGER for code optimization, refactoring, or general prompt engineering tasks.
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.
Provides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.
Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.
Provides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.
Meta-skill for auditing and rewriting SKILL.md `description` fields per the agentskills.io optimizing-descriptions framework, layered with mizchi's two-track trigger policy (Meta = explicit-invoke-only, Project = pushy auto-trigger). Invoke ONLY when the user explicitly asks to "optimize a skill description," "audit descriptions," or "rewrite descriptions per agentskills." Do NOT auto-invoke after every SKILL.md edit; description tuning is a deliberate batch, not a per-edit reflex.
面向 GPT-5.4 等强模型和熟练用户的轻量 AI Agent Harness / checkpoint-driven coding skill。默认用户已经把任务切到基本可执行的最小混沌单元；模型自行分解、探索与推进，人类通过最终目标、最小 spec、复述、checkpoint、证据验证与回写来低干扰控盘。