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Auto-improve any agent prompt using Karpathy's autoresearch method with iterative test-measure-improve loops
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Basado en la clasificación ocupacional SOC
Use when implementing features, fixing bugs, refactoring code, optimizing performance, resolving deprecations, or any multi-step development task requiring planning, implementation, testing, and review. Orchestrates specialized agent teammates through research, architecture, coding, QA, code review, adversarial review, and documentation phases. Triggers on: "implement", "build", "fix bug", "refactor", "add feature", "develop this", "help me build", "add functionality", "optimize performance", "resolve deprecation", "systematic development". Do NOT trigger on: simple questions ("what does this code do?"), file searches ("where is the auth function?"), one-off commands ("run the tests"), code explanations, quick edits, or non-development tasks.
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| name | autoresearch |
| description | Auto-improve any agent prompt using Karpathy's autoresearch method with iterative test-measure-improve loops |
| author | Jennings Liu |
| version | 1.0.0 |
| license | MIT |
Based on Andrej Karpathy's autoresearch method. Instead of manually improving agent prompts, run an iterative loop: try a small change, score the result, keep improvements, revert regressions. Systematically increase agent output quality.
Triggers on: "autoresearch", "auto-improve", "optimize agent", "tune prompt", "improve skill quality"
Run agent on test input. Score output against checklist (yes/no). Record baseline score (average of 3 runs). Analyze weakest checklist items. Make ONE small change to agent prompt. Re-run agent on same test input (3 runs). Score again. If improved → KEEP change. If dropped → REVERT. Repeat until 95%+ score 3 times in a row OR max rounds reached (default: 10). User specifies agent. Read from `agents/.md`. 3-6 yes/no questions. Each checks one specific aspect. Good: "Does the code review identify at least one production-risk bug?" Bad: "Rate the quality 1-10" (subjective). Run agent on test input 3 times. Score each. Average = baseline. For each round: analyze failing items → hypothesize one change → apply (save backup) → test 3 runs → score → KEEP if improved, REVERT if same/worse → log round. Produce summary (baseline/final score, rounds, changes kept/reverted) and changelog per round (score change, what changed, why, effect). Add specific gotcha, add worked example, add banned-patterns list, add rule targeting weakest item, restructure output template Rewriting entire prompt, adding vague instructions, adding 5 rules at once, removing sections without testing Using only one test input optimizes for that case. Use 2-3 diverse inputs. More than 6 items may cause gaming individual checks at expense of quality. Short-term score drops may enable bigger gains. Try "creative rounds" for structural changes. Results stored in `${CLAUDE_PLUGIN_DATA}/autoresearch/`: `[agent-name]-results.json` and `[agent-name]-changelog.md`.