autoresearch-agent
SolidAutonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.
Install
Quality Score: 96/100
Skill Content
Details
- Author
- alirezarezvani
- Repository
- alirezarezvani/claude-skills
- Created
- 7 months ago
- Last Updated
- 3 days ago
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
autoresearch
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.
autoresearch
Karpathy's autoresearch: autonomous ratcheting optimization loops for any artifact. A human writes program.md, the agent runs experiments with git-backed keep/revert. Trigger on "optimize this", "make this better", "iterate on", "autoresearch", "loop on this", "A/B test", "find the best version", Karpathy's loop, experiment loops, hill climbing, the ratchet pattern, or program.md workflows. Works across code, prompts, content, models, and configs.
autoresearch
Autonomous experiment loop inspired by Karpathy's autoresearch. Iteratively modifies code, runs evaluation, measures a metric, and keeps or discards changes using git. Use when optimizing code against a measurable target (test pass rate, performance, bundle size, model quality, etc).
autoresearch
Karpathy-pattern autoresearch — autonomous hill-climbing over a measurable metric, deep multi-agent research, or research-then-optimize. Three modes: Optimize (keep/discard ratchet), Research (STORM multi-perspective), Improve.
autoresearch
Check and run autonomous experiments. Query experiment status, view results dashboards, and execute iterations. TRIGGER when: user asks about experiment status, autoresearch progress, "how's the experiment going", "run another iteration", or invokes "/autoresearch". DO NOT TRIGGER when: user is working on autoresearch agent code itself.