I built a Claude Code skill that applies Karpathy’s autoresearch to any task … not just ML Karpathy’s autoresearch showed that constraint + mechanical metric + autonomous iteration = compounding gains. 630 lines of Python, 100 experiments per night, automatic rollback on failure. I generalized this into a Claude Code skill. You define a goal, a metric, and a verification command … then Claude loops forever: make one atomic change → git commit → verify → keep if improved, revert if not → repeat. Never stops until you interrupt. Works for anything measurable: test coverage, bundle size, Lighthouse scores, API response time, SEO scores, ad copy quality, even SQL query optimization. Combines with MCP servers for database-driven or analytics-driven loops. Every improvement stacks. Every failure auto-reverts. Progress logged in CSV. You wake up to results. MIT licensed, open source: github.com/uditgoenka/autoresearch Please do share your feedback or raise a PR, happy to implement newer ideas. submitted by /u/uditgoenka
Originally posted by u/uditgoenka on r/ClaudeCode