chrisvoncsefalvay

chrisvoncsefalvay / autostar

Public

Autoresearch ALL THE THINGS. RLVR for the masses.

agent-skills autoresearch claude-code claude-skill
18
2
100% credibility
Found Apr 01, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Autostar is an AI skill that helps optimize code, writing, or designs by iteratively experimenting with changes, scoring them against user-defined measures, and learning to converge on the best improvements.

How It Works

1
🔍 Hear about Autostar

You discover a helpful tool that automatically improves your writing, code, or designs by trying ideas and picking the best ones.

2
📱 Add it to your AI chat

With one simple command, you bring Autostar into your favorite AI conversation helper where it feels right at home.

3
🎯 Describe your goal

You chat with it about what you want to make better, like clearer docs or faster code, and it asks questions to understand perfectly.

4
✅ Set your checks and budget

Together you pick ways to measure success, like spell checks or style scores, and agree on how many tries to spend.

5
🔄 Watch it learn and improve

It runs quick experiments, scores each change, remembers what works, and shows a live dashboard of progress getting better each time.

6
📊 Review the final report

At the end, you get charts, what worked best, and your polished result ready to use.

🏆 Enjoy your upgraded work

Your project is now sharper, faster, or more beautiful, and Autostar remembers lessons for next time.

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Star Growth

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AI-Generated Review

What is autostar?

Autostar is a Python skill for Claude Code that automates optimization loops for code, docs, prompts, or APIs using RLVR principles adapted for "verifiable-ish" rewards. You define a goal, break it into tracks with evaluators like linters, LLM judges, or external tools, set a budget, and it mutates artifacts, scores results, reflects, and converges on improvements. No custom RL envs or GPUs needed—just invoke `/skill autostar` for autoresearch on GitHub projects or local files.

Why is it gaining traction?

It stands out by handling soft metrics devs actually care about, like readability or tone, via hybrid verifiers and statistical laps, with live HTML dashboards and persistent memory across runs. Unlike manual iteration or rigid RL setups, it escalates smartly on plateaus and learns priors for faster future optimizations. The hook: zero-setup install via `npx skills add chrisvoncsefalvay/autostar` turns Claude into an autostar machine for the masses.

Who should use this?

Claude Code users tweaking prompt engineering, frontend devs optimizing accessibility scores, or backend teams iterating API designs while keeping tests green. Ideal for Python devs doing autoresearch on GitHub repos, auto starring worthy configs, or anyone needing RLVR without infrastructure—like autostart tweaks for Windows 10/11 or mac setups.

Verdict

Promising for Claude-heavy workflows, with solid docs and schemas, but at 18 stars and 1.0% credibility, it's early-stage—test on non-critical tasks first. Worth a spin if you live in AI-assisted dev.

(198 words)

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