TheGreenCedar

TheGreenCedar / codex-autoresearch

Public

A codex plugin for running optimization loops inside a codebase. It is useful when you have a measurable target and many possible changes to try: test runtime, build speed, bundle size, model loss, Lighthouse scores, memory use, query latency, or any other metric you can print from a script.

autoresearcher codex
18
0
100% credibility
Found Apr 25, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
HTML
AI Summary

Codex Autoresearch is a plugin for running AI-driven, evidence-based optimization loops on code projects with a live dashboard for tracking metrics and decisions.

How It Works

1
🔍 Discover the helper

You hear about a smart tool that helps improve your code by trying small changes and measuring if they work better.

2
📦 Add it to your project

You easily add the tool to your coding workspace so it can understand your files.

3
💭 Tell it your goal

You simply describe what you want to improve, like making tests run faster or fixing bugs, and it creates a safe plan with measurements.

4
🔄 Let it experiment

The tool tries tiny improvements one by one, runs tests to measure results, and only keeps the ones that truly help.

5
📊 Watch progress live

A colorful dashboard opens showing charts of improvements, what worked, what didn't, and the next smart step.

6
✅ Review and save wins

You check the evidence of the best changes and package them neatly for your final review.

🎉 Better code achieved

Your project is faster, more reliable, or improved exactly as you wanted, backed by clear proof from the measurements.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 18 to 18 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is codex-autoresearch?

Codex Autoresearch is a Codex plugin that automates optimization loops in your codebase by running small, measured experiments against benchmarks like test runtime, bundle size, or query latency. Tell Codex a goal and a script that prints "METRIC name=value", and it proposes changes, tests them, logs keeps or discards with structured ASI notes (hypothesis, evidence, next hints), and resumes across sessions via durable state. Built in TypeScript with a Vite-powered React dashboard for live metric trends, ledgers, and handoffs, it installs via `codex marketplace add TheGreenCedar/codex-autoresearch` and works as a codex github plugin in VSCode or IntelliJ.

Why is it gaining traction?

Unlike manual tweaks or unmeasured AI suggestions from codex github copilot, it enforces evidence-based decisions with a live dashboard showing trends, confidence, and safe next actions—preventing vibe-based chaos in long runs. CLI tools like `autoresearch.mjs serve`, `next`, and `log --status keep` make it a seamless codex github integration for autoresearch loops, while quality-gap tracking turns broad research into iterable checklists. Devs dig the Codex AI research skills boost for reproducible gains without context loss.

Who should use this?

Backend engineers chasing p99 latency drops in GraphQL services, frontend teams tuning Lighthouse scores or bundle sizes, or anyone with a printable metric (memory, build speed) wanting codex plugin claude code reviews via experiments. Ideal for codex github issues triage or perf regressions where "make it faster" needs proof, not guesses.

Verdict

Try it if you're deep in Codex workflows—early traction (18 stars) and solid docs make codex autoresearch github viable for targeted loops, despite 1.0% credibility signaling immaturity. Pair with doctor checks for production; it's raw but fills a real gap in measured AI coding.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.