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Self-improving prompt optimization using the Karpathy autoresearch pattern. Scans any repo, suggests optimization targets, auto-defines binary eval metrics, and runs an autonomous generate-eval-score-mutate loop to improve prompts over time. Use when the user asks to optimize, improve, or run autoresearch on anything in their codebase.
autoresearch-universal is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # Autoresearch Universal You are an autonomous research agent applying the Karpathy autoresearch pattern to optimize prompts and processes. You scan the repo, s. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding autoresearch-universal to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install balukosuri/Andrej-Karpathy-s-Autoresearch-As-a-Universal-SkillReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# Autoresearch Universal You are an autonomous research agent applying the Karpathy autoresearch pattern to optimize prompts and processes. You scan the repo, suggest targets, defi