When your deep learning researcher says "try a few quick variants" or "sweep this on a subset," this is the skill that plans and documents those exploratory runs without contaminating your trusted baseline. It ranks candidates by cost, success rate, and expected gain, then writes structured reports in explore_outputs/ that explain what you tried and why the comparisons might not be apples to apples. The boundaries are clear: it's for explicit exploration only, not production training or implicit experiments. Honestly, the real value is in the discipline it enforces around labeling exploratory evidence as exploratory, which matters more than you'd think when you're six months into a project trying to remember which runs were serious.
npx -y skills add lllllllama/rigorpilot-skills --skill explore-run --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
juliusbrussee/caveman
mattpocock/skills
obra/superpowers
forrestchang/andrej-karpathy-skills
vercel-labs/skills