autostar
Iterative experiment loops that measurably improve any artifact
All Skills in This Repository (2)
Pure Emerald Level Indicatorsautostar
Automate structured experimentation to optimize measurable artifacts across code, prompts, docs, and configurations.
autostar-web
Run structured optimization experiments across multiple tracks in memory-constrained web runtimes.
Frequently Asked Questions
FAQPage SchemaHow to install autostar?โผ
Run `npx skills add chrisvoncsefalvay/autostar --all -g -y` in your terminal to install the skill globally, then invoke it with /skill autostar in Claude Code.
What does autostar actually do?โผ
It turns any measurable goal into a structured optimisation loop: it mutates your artifact, scores each attempt across multiple quality tracks, keeps improvements, and reverts failures until your budget runs out or the goal is met.
What kinds of artifacts can autostar improve?โผ
Anything you can measure, including code quality, documentation, prompts, writing style, API design, and accessibility, using verifiers like pytest, pyright, eslint, lighthouse, or rubric-based judges.
Does autostar work outside Claude Code?โผ
Yes. It ships runtime adapters for Claude Code, Codex, Gemini CLI, and Pi with full support, plus a reduced-support package for Claude.ai custom Skills.
Do I need machine learning expertise to use autostar?โผ
No. You describe what good looks like in plain language, approve the plan and budget, and the loop handles experiments, scoring, and learning automatically.
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