autoresearch

      PLAN              LOOP             DEBUG              FIX            SECURE            SHIP
 ┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐
 │   Goal   │     │  Modify  │     │   Find   │     │   Fix    │     │  STRIDE  │     │  Stage   │
 │  Metric  │────▶│  Verify  │────▶│   Bugs   │────▶│  Errors  │────▶│  OWASP   │────▶│  Deploy  │
 │  Scope   │     │  Keep/   │     │  Trace   │     │  Repair  │     │  Red     │     │ Release  │
 └──────────┘     │  Discard │     └──────────┘     └──────────┘     │  Team    │     └──────────┘
/autoresearch:    └──────────┘    /autoresearch:    /autoresearch:   └──────────┘    /autoresearch:
  plan            /autoresearch     debug              fix          /autoresearch:      ship
                                                                     security

                  ┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐
                  │ Scenario │     │ Predict  │     │  Learn   │     │  Reason  │
                  │   Edge   │     │ 5-Expert │     │   Docs   │     │  Debate  │
                  │   Cases  │     │  Swarm   │     │   Gen    │     │ Converge │
                  └──────────┘     └──────────┘     └──────────┘     └──────────┘
                 /autoresearch:   /autoresearch:   /autoresearch:   /autoresearch:
                   scenario         predict           learn           reason

Why This Exists

Karpathy's autoresearch demonstrated that a 630-line Python script could autonomously improve ML models overnight — 100 experiments per night — by following simple principles: one metric, constrained scope, fast verification, automatic rollback, git as memory.

Claude Autoresearch generalizes these principles to ANY domain. Not just ML — code, content, marketing, sales, HR, DevOps, or anything with a number you can measure.

How It Works

LOOP (FOREVER or N times):
  1. Review current state + git history + results log
  2. Pick the next change (based on what worked, what failed, what's untried)
  3. Make ONE focused change
  4. Git commit (before verification)
  5. Run mechanical verification (tests, benchmarks, scores)
  6. If improved → keep. If worse → git revert. If crashed → fix or skip.
  7. Log the result
  8. Repeat. Never stop until you interrupt (or N iterations complete).

Every improvement stacks. Every failure auto-reverts. Progress is logged in TSV format.

The Setup Phase

Before looping, Claude performs a one-time setup:

1. Read context — reads all in-scope files
2. Define goal — extracts or asks for a mechanical metric
3. Define scope — which files can be modified vs read-only
4. Establish baseline — runs verification on current state (iteration #0)
5. Confirm and go — shows setup, then begins the loop

8 Critical Rules