HoangNguyen0403/agent-skills-standard

What it solves

AI coding agents often struggle with team-specific coding standards. Developers typically face a trade-off between providing too many rules (which leads to token waste and AI confusion) or too few rules (which results in generic code). Manually managing .cursorrules or similar prompt files across a team leads to version drift and scaling issues.

How it works

Agent Skills Standard converts engineering rules into modular, version-controlled "skills" that are loaded on demand. It uses a hierarchical lookup system to minimize token usage:

1. Router Table (AGENTS.md): A compact entry point that directs the AI to the correct category index.
2. Category Index (_INDEX.md): A trigger table that matches the current file or keywords in the prompt to specific skills.
3. Skill (SKILL.md): The actual engineering rules, loaded only when relevant.

The project provides a CLI for distribution (syncing skills to disk in formats native to tools like Cursor, Claude Code, and GitHub Copilot) and an optional MCP server for runtime enforcement, ensuring agents actually call the necessary skills before editing code.

Who it’s for

Engineering teams who use AI coding agents (such as Cursor, Claude Code, Copilot, Gemini, Windsurf, and others) and want to enforce consistent, portable SDLC standards across their codebase without wasting tokens.

Highlights

• Multi-Agent Support: Generates native assets for a wide range of AI tools including Cursor, Claude, Copilot, Gemini, Windsurf, Trae, Kiro, and Roo.
• Token Efficiency: Uses a hierarchical loading system to reduce token consumption by up to 85% compared to traditional prompt engineering.
• Extensive Library: Includes 280 ready-to-use coding standards across 20+ frameworks (e.g., Flutter, React, NestJS, Go, Python).
• SDLC Workflow Spine: Provides a native lifecycle for agents to follow, covering stages from brainstorming and planning to security testing and verification.
• Runtime Enforcement: An MCP server that allows agents to load skills via tool calls, closing the gap where agents might forget to read the index files.