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autoresearch

Claude Autoresearch Skill — Autonomous goal-directed iteration for Claude Code. Inspired by Karpathy's autoresearch. Modify → Verify → Keep/Discard → Repeat forever.

Install / Use

npx skills add uditgoenka/autoresearch

Installs into whichever agent you are using.

About this skill
🤖

CLAUDE.md

Claude Code project instructions

Quality Score

96/100

Supported Platforms

Claude Code

Tags

Our assessment of autoresearch

autoresearch scores 96/100 on our quality scale, 22nd of 441 Education & Research skills we index (top 5%).

Its CLAUDE.md is 36 KB long, well organised into 41 sections with 33 code examples: a thorough specification that gives an agent plenty to work with.

With 6,809 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
16/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 6 days ago, so autoresearch is actively maintained.
  • Our last check on 2026-10-17 found the source still online.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

autoresearch compared with similar skills

All 4 of these similar skills score higher than autoresearch; compare them before choosing.

SkillScoreStarsUpdatedFormat
autoresearch (this skill)by uditgoenka966.8k6d agoCLAUDE.md
claude-memby thedotmack100113.8ktodayCLAUDE.md
taste-skillby Leonxlnx100111.0k1d agoCLAUDE.md
Agent-Reachby Panniantong100109.5k1d agoCLAUDE.md
Understand-Anythingby Egonex-AI10091.0ktodayCLAUDE.md

Frequently asked questions

How do I install autoresearch?
Run npx skills add uditgoenka/autoresearch. The install tabs above show the steps for each supported agent.
Which AI agents does autoresearch work with?
It is written for Claude Code, as a CLAUDE.md file. Other agents that read the same format can often use it too.
Is autoresearch safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is autoresearch still maintained?
The repository was last updated 6 days ago, so autoresearch is actively maintained.
<div align="center">

Autoresearch

Turn Claude Code, OpenCode, or OpenAI Codex into a relentless improvement engine.

Based on Karpathy's autoresearch — constraint + mechanical metric + autonomous iteration = compounding gains.

Claude Code Skill OpenCode Codex Version License: MIT

Based on Follow @iuditg Support

<br>

"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, and OpenAI Codex for the core skill, bundled runtime, installation, and verification surface. Hook guardrails are Claude Code-only.

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.

<br>

How It Works · Commands · Quick Start · Guides · FAQ

</div>
     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.


How It Works

LOOP (N iterations or until done):
  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 until N iterations complete or goal is met.

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

| # | Rule | |---|------| | 1 | Bounded by default — every command has a default iteration count; unlimited is opt-in via Iterations: unlimited | | 2 | Read before write — understand full context before modifying | | 3 | One change per iteration — atomic changes; if it breaks, you know why | | 4 | Mechanical verification only — no subjective "looks good"; use metrics | | 5 | Automatic rollback — failed changes revert instantly | | 6 | Simplicity wins — equal results + less code = keep | | 7 | Git is memory — experiments committed with experiment: prefix; agent reads git log + git diff before each iteration | | 8 | When stuck, think harder — re-read, combine near-misses, try radical changes |


Hooks & Safety

Hooks are defense-in-depth guardrails, not a security sandbox. Claude Code ships the hook surface; OpenCode and Codex ship the core skill/runtime/install surface without hook parity.

What's Protected

| Hook | What it does | Event | |------|-------------|-------| | scout-block | Blocks node_modules/, .git/, pycache/, etc. from filling your context | PreToolUse | | privacy-block | Blocks .env, SSH keys, credentials from being read in sessions | PreToolUse | | dangerous-cmd-block | Blocks force-push, rm -rf, git reset --hard | PreToolUse | | iteration-context | Injects recent TSV iteration data after context compaction | UserPromptSubmit | | subagent-context | Gives subagents awareness of active loop state | SubagentStart | | dev-rules-reminder | Re-injects plan path and code standards after compaction | UserPromptSubmit | | simplify-gate | Warns at 400 LOC, blocks at 800 LOC before shipping | UserPromptSubmit | | session-init | Sets up project context at session start | SessionStart | | stop-notify | Terminal notification + optional webhook on session end | SessionEnd |

Configuration

All hooks are on by default. Disable individually only for troubleshooting:

# Disable a specific hook
export AR_DISABLE_SCOUT_BLOCK=1
export AR_DISABLE_PRIVACY_BLOCK=1
export AR_DISABLE_DANGEROUS_CMD_BLOCK=1
# ... etc for each hook name

Optional webhook for session completion notifications:

export AR_NOTIFY_WEBHOOK=https://hooks.slack.com/services/...

Customize blocked directories with a .ckignore file (gitignore syntax) at your project root.

See guide/hooks.md for full reference.

Release preparation and contributor verification live in scripts/release.md.


Commands

| Command | What it does | Default Iterations | |---------|--------------|--------------------| | /autoresearch | Classic: Core iterate loop: modify → verify → keep/discard · Orchestrator: free-form goal → auto-select pipeline → loop until predicate met | 25 / goal-bounded | | /autoresearch:plan | Convert goal into validated config | one-shot | | /autoresearch:debug | Hunt bugs via hypothesis iteration | 15 | | /autoresearch:fix | Crush errors one-by-one to zero | 20 | | /autoresearch:security | STRIDE + OWASP audit with red-team | 15 | | /autoresearch:ship | Ship through 8 phases | linear | | /autoresearch:scenario | Generate edge cases across 12 dimensions | 20 | | /autoresearch:predict | 5 expert personas debate | one-shot | | /autoresearch:learn | Scout → generate docs → validate → fix | 10 | | /autoresearch:reason | Adversarial debate with blind judges | 8 | | /autoresearch:probe | 8 personas interrogate requirements | 15 | | /autoresearch:improve | Research ICP, discover improvements, generate PRDs | 15 | | /autoresearch:evals | Analyze iteration results: trends, plateaus | one-shot | | /autoresearch:regression | Stability gate: baseline vs candidate, verdict STABLE/UNSTABLE | one-shot |

Universal flags: Iterations: N, Iterations: unlimited, --evals, --evals-interval N, --chain <targets>, --<subcommand> shorthand.

All commands use interactive setup when invoked without arguments. Just type the command — the agent asks for what it needs with smart defaults based on your codebase.

OpenCode users: Commands use underscore naming (/autoresearch_debug, /autoresearch_fix, etc.). All 14 commands available.

Codex users: Invoke via $autoresearch mention syntax. Subcommands are keywords: $autoresearch debug, $autoresearch plan, etc.

Quick Decision Guide

| I want to... | Use | |--------------|-----| | Give a plain-language goal, let it self-orchestrate | /autoresearch <goal> (bare, no Metric/Verify) | | Improve test coverage / reduce bundle size / any metric | /autoresearch | | Run bounded iterations | Add Iterations: N to any command | | Don't know what metric to use | /autoresearch:plan | | Run a security audit | /autoresearch:security | | Ship a PR / deployment / release | /autoresearch:ship | | Optimize without breaking existing tests | Add Guard: npm test | | Hunt all bugs in a codebase | /autoresearch:debug | | Fix all errors (tests, types, lint) | /autoresearch:fix | | Debug then auto-fix | /autoresearch:debug --fix | | Check if something is ready to ship | /autoresearch:ship --checklist-only | | Explore edge cases for a feature | /autoresearch:scenario | | Generate test scenarios | /autoresearch:scenario --format test-scenarios | | Get expert opinions before starting | /autoresearch:predict | | Analyze from multiple angles then debug | /autoresearch:predict --chain debug | | Generate docs for a new codebase | /autoresearch:learn --mode init | | Update existing docs after changes | /autoresearch:learn --mode update | | Debate an architecture decision | /autoresearch:reason --domain software | | Surface hidden constraints before starting | /autoresearch:probe | | Pre-flight a fuzzy goal then loop | /autoresearch:probe --chain plan,autoresearch | | Discover what to build next for your ICP | /autoresearch:improve | | Research competitors and generate PRDs | /autoresearch:improve --depth deep | | Probe requirements then research improvements | /autoresearch:probe --improve | | Analyze trends and plateaus across past runs | /autoresearch:evals | | Check if a run has stalled | /autoresearch:evals --file *-results.tsv | | Veri

Truncated for display — read the full file on GitHub.

Related Skills

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GitHub Stars6.8k
CategoryEducation
Updated6d ago
Forks492

Languages

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Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions