Generate comprehensive handoff documentation optimized for AI agent takeover by analyzing project structure, design docs, and codebase

Agent parameter passing, memory files, and data handoffs between agents

Self-improvement loop for multi-agent workflows. Diagnose failures, improve tool descriptions, and learn from success/failure patterns.

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Initialize or improve AGENTS.md files that define how coding agents operate in a repo. Use when asked to set up or replace an agent init command (Codex, Claude), standardize multi-agent behavior, or audit an existing AGENTS.md for clarity, commands, boundaries, and repo-specific context. For Claude Code, also create CLAUDE.md as a symlink to AGENTS.md.

Agent Inventor Skill

Agent invocation syntax and boundary rules

Senior Java architect specializing in enterprise-grade applications, Spring ecosystem, and cloud-native development. Masters modern Java features, reactive programming, and microservices patterns with focus on scalability and maintainability.

Expert JavaScript developer specializing in modern ES2023+ features, asynchronous programming, and full-stack development. Masters both browser APIs and Node.js ecosystem with emphasis on performance and clean code patterns.

Expert Laravel specialist mastering Laravel 10+ with modern PHP practices. Specializes in elegant syntax, Eloquent ORM, queue systems, and enterprise features with focus on building scalable web applications and APIs.

Launches specialized Claude agents for targeted tasks. Analyzes requirements, selects appropriate agent, and executes with optimized configuration.

Expert legacy system modernizer specializing in incremental migration strategies and risk-free modernization. Masters refactoring patterns, technology updates, and business continuity with focus on transforming legacy systems into modern, maintainable architectures without disrupting operations.

Expert legal advisor specializing in technology law, compliance, and risk mitigation. Masters contract drafting, intellectual property, data privacy, and regulatory compliance with focus on protecting business interests while enabling innovation and growth.

Expert MCP developer specializing in Model Context Protocol server and client development. Masters protocol specification, SDK implementation, and building production-ready integrations between AI systems and external tools/data sources.

通过交互式提问生成高质量的 GitHub Copilot agents.md 文件。

Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

Expert MLOps engineer specializing in ML infrastructure, platform engineering, and operational excellence for machine learning systems. Masters CI/CD for ML, model versioning, and scalable ML platforms with focus on reliability and automation.

Expert network engineer specializing in cloud and hybrid network architectures, security, and performance optimization. Masters network design, troubleshooting, and automation with focus on reliability, scalability, and zero-trust principles.

Platform/Language agnostic API delivery and correctness auditor. Use when project contains API endpoints to verify contract alignment, endpoint behavior, and test coverage.

Create .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.

A senior code-review agent that produces critical, thorough, constructive, and evidence-based reviews. Works as a sub-agent or through direct invocation.

Interactive code review for agent iterations. Captures comments, tracks resolution status, and integrates with git diffs.

Create/update .agent/constitution.md. Use when commands/boundaries/constraints must be confirmed before baseline or code changes. Draft v0 from repo evidence, then interview user.

Analyze the codebase to create a concise, LLM-optimized structured overview in .agent/map.md.

Create a plan and issues for implementation of a production-ready Python project with proper structure, tooling, and best practices.

Create focused, specific technical documentation for codebase sections. Analyzes code, identifies topics, presents options before writing. Supports code blocks with line numbers.

Deep, excruciating code review. Use anytime to analyze code for correctness, edge cases, security, performance, and design issues. Not tied to baseline—this is pure code analysis.

Systematic debugging approaches for isolating and fixing software defects. Use when something isn't working and the cause is unclear.

Docker image reviews, optimization, and step-building guidance. Analyzes Dockerfiles for best practices, security issues, and anti-patterns.

Dogfooding discovery agent — establish human-approved project baseline from public docs without code inspection

Analyze issues to identify the next work item and update focus.md. Enforces issue-first workflow and confidence-based batch limits.

Generate narrative summaries from git history for onboarding, retrospectives, changelogs, and exploration. LLM-enhanced when available, works without LLM too.

Comprehensive project hygiene: archive issues, validate schema, clean clutter, align docs, check git, update ignores.

Extract, plan, or propose implementation details at configurable depth levels (low/normal/extensive). Outputs to reference files for team discussion and handoff.

Implement only after a validated/approved plan. Use for coding: small diffs, frequent tests, no refactors, stop on ambiguity.

You are a **senior engineering analyst** tasked with identifying **concrete, justified improvements** to the codebase that are **aligned with the project’s stated goals and current reality**.

---name: agent-ops-lint-instructionsdescription: "Validate and lint AgentOps instruction files (skills, prompts, agents). Checks formatting, structure, and consistency."category: extended []invoked_by

Migrate a project into another, ensuring functionality and validating complete content transfer. Use for monorepo consolidation, template upgrades, or codebase mergers.

MkDocs documentation site management: initializing, updating, building, and deploying

Optimize agent instruction files by extracting sections into separate files and referencing them. Reduces context size while preserving information.

Transform implementation plans into concise stakeholder-friendly summaries with file change overviews, component listings, and optional flow diagrams.

Identify and map different sections of a software project (API, frontend, database, CLI, domain). Use for context scoping and architecture documentation.

Handle failures and errors during workflow. Use when build breaks, tests fail unexpectedly, or agent gets stuck. Semi-automatic recovery with user confirmation for destructive actions.

Generate markdown reports from issues. Filter by type, priority, epic, date range. Supports summary, detailed, progress, completion, velocity, and backlog analysis views.

Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.

Create clean git branches from feature work, excluding agent-ops files. Use for PR preparation.

Manage semantic versioning, changelog generation, and release notes. Auto-generates entries from completed issues or git diff.

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.