celesteanders

celesteanders / harness

Public

Harness engineering best practices

47
7
89% credibility
Found Apr 10, 2026 at 47 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project is a structured guide that helps AI agents plan, build, test, and review software changes in a reliable, step-by-step way.

How It Works

1
🔍 Find the Helper

You discover a smart guide that helps AI reliably build and improve software in your project.

2
⚙️ Set Your Rules

You simply tell it how to check if your project is working well, like your usual quality tests.

3
💡 Share Your Idea

You describe a bug to fix or a new feature you want, and it starts organizing the work.

4
🧠 AI Creates a Plan

The AI thoughtfully breaks your request into clear, bite-sized steps with specific goals to meet.

5
🔨 Build and Test

It carefully writes the code, creates tests first, and checks everything step by step.

6
🔍 Smart Review

A fresh, picky checker examines the work to ensure it fully meets the goals and has no shortcuts.

✅ Project Improved

Your software now has the new fix or feature, safely added and ready to use with confidence.

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Star Growth

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AI-Generated Review

What is harness?

Harness is a Python tool that automates AI-driven coding workflows using Claude from Anthropic, drawing from harness engineering research by Anthropic and OpenAI on generator-evaluator patterns. It takes user feedback—like bugs or features—triages it into JSON plans with tasks and acceptance criteria, then executes them via test-driven development, verifies with your project's checks, and gates progress with a separate skeptical evaluator agent. You get interactive mode in Claude Code or headless CLI runners for batch jobs, persisting state across sessions for reliable AI software development.

Why is it gaining traction?

It stands out with a fresh-context evaluator that catches what self-grading agents miss, plus built-in retries, git commits per task, and TDD enforcement—making AI outputs production-ready without babysitting. Devs dig the simple CLI for looping tasks (`--loop`), skipping evals for speed (`--skip-eval`), or targeting specifics (`--task 2`), plus seamless GitHub repo integration for plans and progress. Inspired by harness engineering YouTube talks and papers like "leveraging Codex in an agent-first world," it hooks those exploring harness engineering AI agents.

Who should use this?

Backend and fullstack devs using Claude for ticket triage, planning, and implementation on GitHub repos. AI engineering jobs teams building harness engineering Anthropic or OpenAI flows. Solo devs handling repetitive fixes or features in Python/TS projects with custom verify commands.

Verdict

Solid for early adopters in harness engineering GitHub workflows—docs distill best practices well, but 47 stars and 0.9% credibility score signal experiment territory, not battle-tested. Try it on a side repo if you're into AI agents; skip for mission-critical unless you tweak configs. (187 words)

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