LLM 與 Agent

Terragrunt wrapper for Terraform with DRY configurations. Enforces plan-before-apply workflow. Use when working with terragrunt.hcl files.

Gate 6 - Verify tests exist and cover critical paths. Issues result in WARNINGS (encourages tests, doesn't block).

This skill should be used when creating or updating implementation documentation, task breakdowns, verification steps, or phase planning documents. It provides standards and templates for consistent, professional documentation throughout the MTG Agent project.

LLMs, prompt engineering, RAG systems, and AI application development

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

Agent-to-Agent (A2A) executor implementation patterns for task handling, execution management, and agent coordination. Use when building A2A executors, implementing task handlers, creating agent execution flows, or when user mentions A2A protocol, task execution, agent executors, task handlers, or agent coordination.

Manages Claude Skills lifecycle - creating, updating, maintaining, and versioning project-specific and cross-IDE knowledge bases. Invoke when user wants to create new skills, update existing ones, or sync skill content across tools.

expo-secure-store patterns for sensitive data. Use when storing tokens and credentials.

Enhance and refine prompts for AI coding agents using Chain-of-Thought reasoning. Use when user asks to improve, optimize, rewrite, or enhance a prompt. Transforms vague requests into structured, high-context instructions for Codex, Claude Code, or Gemini CLI.

Parse Claude command frontmatter and expose body/meta for Codex CLI.

LangGraph development for stateful multi-agent applications, cyclic workflows, conditional routing, human-in-the-loop patterns, and persistent state management. Use for complex AI orchestration, agent coordination, and production-grade agentic systems.

Configuring and optimizing 16-bit Low-Rank Adaptation (LoRA) and Rank-Stabilized LoRA (rsLoRA) for efficient LLM fine-tuning using triggers like lora, qlora, rslora, rank selection, lora_alpha, lora_dropout, and target_modules.

AI assistant framework for building unique, authentic portfolio websites from scratch. Guides agents through research, design, and implementation phases.

Conducts structured interviews to produce Change Contracts - definitions of done expressed in terms of behavior, constraints, operability, and acceptance criteria. Use when externalizing intent for work to be delegated to AI agents, when defining requirements without implementation details, or when asked to create a change contract.

Use when designing database schema, writing migrations, or making data storage decisions - enforces UUIDs, account_id multi-tenancy, state-as-records, no foreign keys, and proper index patterns

Execute agentic coding tools directly via bash commands with output capture. Use this when the user requests to run AI agents (Gemini, Claude Code, etc.) and get their results directly.

Transcribe audio via OpenAI Audio Transcriptions API (Whisper).

Use when spawning Codex agents via Otto to delegate implementation work.

Convert MCP servers into standalone Bash-invokable scripts. Use when user wants to make an MCP server usable as bash commands, convert MCP to CLI, or wrap MCP tools for agent use.

Write minimal, strategic tests focused on core user flows and critical paths during feature development, deferring comprehensive edge case testing. Use this skill when writing unit tests, integration tests, or end-to-end tests, creating test files (*.test.ts, *.spec.js, __tests__/*), testing critical user workflows, focusing on behavior rather than implementation details, using descriptive test names, mocking external dependencies (databases, APIs, file systems), ensuring tests run quickly, testing business-critical functionality, or determining which tests to write during development versus dedicated testing phases. Apply this skill when completing features at logical milestones, writing tests for primary user flows, or balancing feature development speed with adequate test coverage.