Autoresearch
Turn Claude Code, OpenCode, OpenAI Codex, pi, or Muse into a relentless improvement engine.
Based on Karpathy's autoresearch — constraint + mechanical metric + autonomous iteration = compounding gains.
"Set the GOAL → The agent 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.
Supports Claude Code, OpenCode, OpenAI Codex, pi, and Muse for the core skill, bundled runtime, installation, and verification surface. Hook guardrails ship for Claude Code (native hooks) and pi (extension events).
Fork-and-flush: metric loops escape plateaus the way Cai et al. (arXiv:2610.07447) do. At 33% and 66% of the budget, or after 5 iterations with no keep, the loop forks fresh-context branches from the best commit so far, runs each briefly, and keeps the winner. Half the branches pick their own ideas (randomness); the other half each get a distinct experiment from a stateless Advisor that reads the results log (Zhang et al., arXiv:2610.07625); Guided: R sets the split. The orchestrator answers a plateau with FORK before it gives up. See guide/autoresearch.md.
v2.2.0 — Autonomous Orchestrator: Type a plain-language goal to /autoresearch and it classifies your goal, derives a Success predicate, confirms it once, then loops across subcommands until done. No manual chaining required. Metric:/Verify: invocations run the classic loop unchanged. See guide/autoresearch-orchestrator.md.
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/Drop │ │ Trace │ │ Repair │ │ Red Team │ │ Release │
└──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘
/autoresearch: /autoresearch /autoresearch: /autoresearch: /autoresearch: /autoresearch:
plan debug fix security ship
┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ Probe │ │ Scenario │ │ Predict │ │ Reason │
│ Require- │ │ Edge │ │ 5-Expert │ │ Debate │
│ ments │ │ Cases │ │ Swarm │ │ Converge │
└──────────┘ └──────────┘ └──────────┘ └──────────┘
/autoresearch: /autoresearch: /autoresearch: /autoresearch:
probe scenario predict reason
┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ Learn │ │ Improve │ │ Eval │ │ Baseline │
│ Docs │ │ Research │ │ Analyze │ │ Diff │
│ Gen │ │ PRDs │ │ Results │ │ Verdict │
└──────────┘ └──────────┘ └──────────┘ └──────────┘
/autoresearch: /autoresearch: /autoresearch: /autoresearch:
learn improve evals regression
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.
v2.1.0 is a major architecture rebuild. The monolithic SKILL.md (813 lines, ~100K tokens per invocation) is replaced with a thin 41-line routing file and 12 self-contained command files (94–120 lines each, ~5–8K tokens per invocation). That is a 95% token reduction with the same capability surface.
以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗
