Automates continuous single-change experiments to optimize measurable performance metrics.
npx skills add https://github.com/Oruga420/claude-code-skills --skill karpathy-oruga420
Please help me install this Agent Skill. Skill: karpathy Source: https://github.com/Oruga420/claude-code-skills/tree/main/karpathy Command: npx skills add https://github.com/Oruga420/claude-code-skills --skill karpathy-oruga420
Turn ad-hoc guesses and one-off tweaks into a systematic, never-ending experimentation pipeline that reliably finds measurable improvements for any metric-driven project. It removes the manual coordination burden of proposing, implementing, measuring, and deciding on changes so teams can continuously optimize performance, ads, code quality, ML training, or custom domains.
Use the karpathy skill to start an autonomous experimentation loop that measures a baseline, proposes targeted changes, runs them in isolated branches, and reports keep/discard decisions.
High-intent search queries and answers about installing and using this skill.
You can automate continuous experiments by spawning an agent team that proposes changes, applies them in isolated git worktrees, runs reproducible measurements, and evaluates keep versus discard decisions for each experiment to systematically improve your measurable performance metric.
An autonomous experimentation loop for ML training and code quality systematically proposes single-change experiments, applies them to isolated git branches, runs deterministic measurement commands, and logs the results to continuously optimize metrics without manual coordination.
Yes, you need git worktree and branch access for isolated experiments along with a deterministic, reproducible measurement command to establish baselines and reliably evaluate whether each proposed single-change experiment improves the target metric.
Yes, after accumulating sufficient experiment history in your results log, a local GPU-backed learning layer can optionally train a tiny predictor to prioritize high-probability ML hyperparameter proposals for subsequent optimization iterations.
Yes, the autonomous experimentation approach works for ad optimization and custom domains by utilizing domain templates and measurement contracts, allowing you to define specific metrics and commands for any measurable, git-accessible target.
Isolated branch experimentation prevents metric regression by testing proposed changes in separate git worktrees, ensuring only modifications that demonstrably improve the reproducible measurement command results are merged back into the main branch.
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