jmilinovich

jmilinovich / goal-md

Public

A goal-specification file for autonomous coding agents. Generalizes Karpathy's autoresearch to domains with constructed metrics.

23
2
100% credibility
Found Mar 16, 2026 at 23 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

GOAL.md provides a template file and pattern for directing AI coding agents to autonomously optimize software projects using a custom fitness score, improvement loop, and guidelines.

How It Works

1
💡 Discover the idea

You hear about a clever trick to make AI helpers improve your project automatically by giving them a clear number to chase.

2
📺 Watch the quick video

Enjoy a short animated story explaining how to turn fuzzy goals like 'better docs' into something AI can measure and boost.

3
📋 Grab the ready guide

Copy the simple template file into your project folder to set up the instructions for your AI.

4
🎯 Define your success number

Pick what 'better' means for your project, like a score for test reliability or doc quality, so the AI knows exactly what to aim for.

5
🤖 Hand it to your AI

Tell your AI assistant to read the guide and start working on making that number go up.

6
😴 Step away and relax

Let the AI loop through checks, fixes, and improvements on its own while you rest.

🌅 Wake up to progress

Open your project to find higher scores, useful changes, and a noticeably better result from the overnight work.

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AI-Generated Review

What is goal-md?

Goal-md is a goal-specification file you drop into any repo to turn autonomous coding agents into targeted improvers. It generalizes Karpathy's autoresearch beyond natural metrics like training loss, letting you define constructed scores for fuzzy domains like docs quality or test trustworthiness via simple shell scripts. Paste one prompt into Claude or Cursor—"read goal-md and write me a GOAL.md"—and the agent generates a custom file with fitness function, loop, actions, modes, and constraints.

Why is it gaining traction?

It stands out by providing ready templates, real examples across docs, APIs, perf, and browser tests, plus a self-scoring script that outputs progress like "47/100 → 83." Developers hook on the "give it a number, go to sleep" loop—agents iterate autonomously overnight with atomic commits, no babysitting. Builds trust via dual scores (target + instrument quality) to avoid gaming metrics.

Who should use this?

Backend devs optimizing API test coverage without pytest-cov equivalents. Frontend teams measuring React docs quality or Playwright reliability. Anyone looping AI agents on iterative repo tasks like perf tuning via wrk/k6, where vibes won't cut it.

Verdict

Promising pattern for agent-driven coding in metric-less domains, but at 23 stars and 1.0% credibility, it's raw—docs shine with video/examples, yet needs more battle-tested cases. Try on a side project if you're deep into autonomous agents; skip for production until reviews pile up.

(187 words)

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