agentleFS
andrmazandrmaz/spec-driven-architecture/AGENTS.md

spec-driven-architecture

AGENTS.md1 starsChanged 9 months agoMITView on GitHub

Full-stack app built with React Router v7, Drizzle ORM + PostgreSQL, Tailwind CSS v4, and Vite. Commit format: type(scope): subject — e.g. feat(auth): add login

The file

We split this AGENTS.md into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Commit format: type(scope): subject — e.g.

    line 28

≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
AGENTS.md

Spec-Driven Architecture — Agent Guidelines

Full-stack app built with React Router v7, Drizzle ORM + PostgreSQL, Tailwind CSS v4, and Vite.

Package Manager

Use pnpm.

Project Structure

  • app/ — React Router routes, components, loaders
  • server/ — Express server
  • database/ — Drizzle schema and context

Path Aliases

  • ~/ → app/
  • ~/database/* → database/*

Commands

TaskCommand
Lintpnpm lint / pnpm lint:fix
Formatpnpm format / pnpm format:check
Validate commit messagepnpm commitlint

Commit format: type(scope): subject — e.g. feat(auth): add login

Further Reading

  • Tambo AI framework — CLI usage, generative UI rules

Plan Mode

  • Make the plan extremely concise. Sacrifice grammar for the sake of concision.
  • At the end of each plan, give me a list of unresolved questions to answer, if any.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.