Overview
This repository provides production-grade engineering skills designed to empower AI coding agents. It translates the workflows, quality gates, and best practices of senior engineers into actionable skills that AI agents can consistently apply across all phases of software development.
Key Features
- Structured Workflows: Skills are organized into distinct phases of the development lifecycle, from defining requirements to shipping code, with clear slash commands (
/spec,/plan,/build,/test,/review,/ship) for each. - Automatic Skill Activation: Skills intelligently activate based on the task at hand, such as
api-and-interface-designfor API work orfrontend-ui-engineeringfor UI development. - Comprehensive Skill Set: Includes 25 specialized skills covering areas like spec-driven development, test-driven development, code review, security hardening, performance optimization, and CI/CD automation.
- Agent Personas: Pre-configured specialist personas (e.g.,
code-reviewer,test-engineer,security-auditor) provide targeted AI perspectives for enhanced quality and specific domain expertise. - Actionable Checklists: Quick-reference checklists for definitions of done, testing patterns, security, performance, accessibility, and observability ensure consistent adherence to best practices.
- Robust Agent Integration: Designed for seamless integration with over 70 AI coding agents and IDEs, including Claude Code, Cursor, Copilot, and more, via CLI or direct plugin installation.
- "Build Auto" for Efficiency: Streamline the development process with
build auto, which generates a plan and executes tasks autonomously after a single user approval, while maintaining rigorous verification at each step. - Emphasis on Verification: Every skill mandates evidence-based verification, rejecting subjective assessments in favor of concrete proof like passing tests, build outputs, or runtime data.
- Anti-Rationalization: Explicitly addresses and counters common excuses agents might use to bypass quality steps, reinforcing disciplined engineering practices.
Typical Use Cases
- Enhancing AI Code Generation: Equip AI coding assistants with sophisticated engineering practices for more reliable, maintainable, and production-ready code.
- Standardizing Development Workflows: Ensure consistent application of quality gates, testing methodologies, and best practices across development teams, regardless of AI tool used.
- Accelerating Development Cycles: Automate complex engineering tasks and reduce manual oversight, allowing developers to focus on innovation.
- Improving Code Quality and Security: Implement rigorous review, testing, and security checks automatically, catching issues early in the development process.
- Onboarding New Developers: Provide a clear, executable framework for junior developers or AI agents to follow established engineering standards.
- Complex Project Management: Break down large initiatives into manageable, verifiable tasks executed with precision by AI agents.
