krzysztofdudek

krzysztofdudek / ResearcherSkill

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One file. Your AI coding agent becomes a scientist. Autonomous experimentation skill for Claude Code, Codex, or any other agent.

ai-agent ai-coding autonomous-agent autoresearch claude-code
25
0
100% credibility
Found Mar 23, 2026 at 25 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A single markdown file that instructs AI coding agents to autonomously run experiments on codebases, testing hypotheses to optimize metrics like speed or efficiency.

How It Works

1
🔍 Discover Researcher Skill

You hear about a simple way to turn your AI coding helper into a smart scientist that tests and improves your project automatically.

2
📥 Grab the single file

Download the one easy instruction file from the website to get started right away.

3
🗂️ Drop it into your AI tool

Place the file into your favorite AI coding assistant like Claude, and it instantly knows how to research and experiment.

4
💡 Tell it what to improve

Chat with your AI about your goal, like making your app faster or tests quicker, and it sets up a safe workspace to try ideas.

5
⏳ Let it run experiments

While you relax or sleep, it designs tests, tries changes, measures results, and keeps the best ones, logging everything clearly.

6
📊 Review the results

Check the summary table of experiments to see what worked, with improved code ready in branches you can pick from.

🎉 Enjoy faster, better project

Your code is optimized with proven improvements, like cutting wait times in half, all discovered automatically overnight.

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AI-Generated Review

What is ResearcherSkill?

ResearcherSkill is a single Markdown file you drop into AI coding agents like Claude Code or Codex, turning them into autonomous scientists that run experiments on your codebase. It interviews you on optimization goals—like API latency or bundle size—then spins up git branches to test hypotheses, measure results, commit keepers, discard failures, and log everything in a .lab directory. Developers get overnight runs of 30+ experiments without babysitting, generalizing ideas like autoresearch to any metric-driven tuning.

Why is it gaining traction?

Its one-file simplicity means github one-click setup—no pyinstaller hassles or one repository multiple projects juggling—just drop the .one file extension into your agent and go. The hook is true autonomy: non-linear git branching, convergence detection, and resume from .lab history, letting agents explore at one pace while you sleep. Stands out for qualitative metrics and thought experiments beyond ML, with all knowledge persisting outside git.

Who should use this?

Backend engineers optimizing DB configs, thread pools, or p99 latency on live APIs. Frontend devs shrinking bundle sizes via tree-shaking or dependency swaps. AI prompt engineers measuring accuracy and token costs, or anyone with measurable code goals tired of manual A/B testing.

Verdict

Worth starring and testing on a side project—25 stars and 1.0% credibility score signal early days with thin docs, but the one-file agent delivers real autonomous value out of the gate. Fork it, run experiments, and contribute if it sticks.

(178 words)

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