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Claude Autoresearch — autonomous goal-directed iteration for Claude Code. Inspired by Karpathy's autoresearch: constraint + mechanical metric + autonomous iteration = compounding gains.
npx claudepluginhub uditgoenka/autoresearchAutonomous improvement engine for Claude Code. Runs an unbounded modify-verify-keep/discard loop against any mechanical metric. 10 subcommands: plan, debug, fix, security, ship, scenario, predict, learn, and reason.
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Production-ready workflow orchestration with 80 focused plugins, 185 specialized agents, and 153 skills - optimized for granular installation and minimal token usage
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Turn Claude Code into a relentless improvement engine.
Based on Karpathy's autoresearch — constraint + mechanical metric + autonomous iteration = compounding gains.
"Set the GOAL → Claude runs the LOOP → You wake up to results"
You don't need AGI. You need a goal, a metric, and a loop that never quits.
How It Works · Commands · Quick Start · Guides · FAQ
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
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.
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.
Before looping, Claude performs a one-time setup: