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Claude Market/OpenClaw Skills/uditgoenka/Autoresearch
Autoresearch logo

Autoresearch

uditgoenka/autoresearch
Run it on Hostinger, 20% off →Your friend gets 20% off too, using this linkFree API →|Coding & Engineering|View on GitHub|Create your own skill →

Installation

git clone https://github.com/uditgoenka/autoresearch.git

Summary

An autonomous iteration loop, inspired by Karpathy's autoresearch concept, that repeatedly modifies, verifies, and keeps or discards changes against a metric until a goal is met, with dedicated subcommands for debugging, fixing, security audits, shipping, and more.

SKILL.md

Autoresearch — Autonomous Goal-directed Iteration

Safety Invariants (all subcommands)

  • Never push, publish, or deploy without explicit user approval.
  • Bounded by default. Override with Iterations: unlimited.
  • All results logged to autoresearch/{subcommand}-{YYMMDD}-{HHMM}/ directory.
  • Chain handoff via handoff.json. Evals reads *-results.tsv.

Dispatch (bare /autoresearch)

Parse the invocation in this order:

| Condition | Mode | |---|---| | Metric: or Verify: present | Classic — existing metric loop, unchanged | | Free-form natural-language goal, no metric/verify | Orchestrator — see Orchestrator section | | Nothing | Setup wizard — interactive config builder | | --classic flag | Force Classic regardless of goal text | | --auto flag | Force Orchestrator regardless of goal text |

Print a banner on every invocation: [autoresearch] mode: classic | orchestrator | wizard.

Subcommands

| Command | Does | Default Iterations | |---|---|---| | /autoresearch | Iterate against a metric: modify → verify → keep/discard | 25 | | /autoresearch:plan | Convert a goal into validated Scope, Metric, Verify config | N/A | | /autoresearch:debug | Hunt bugs: hypothesize → test → falsify → repeat | 15 | | /autoresearch:fix | Crush errors one-by-one until zero remain | 20 | | /autoresearch:security | STRIDE + OWASP audit with red-team personas | 15 | | /autoresearch:ship | Ship through 8 phases: checklist → dry-run → deploy → verify | N/A | | /autoresearch:scenario | Generate edge cases across 12 dimensions | 20 | | /autoresearch:predict | 5 expert personas debate before implementation | N/A | | /autoresearch:learn | Scout codebase → generate docs or wiki → validate → fix loop | 10 | | /autoresearch:reason | Adversarial debate with blind judges until convergence | 8 | | /autoresearch:probe | 8 personas interrogate requirements until saturation | 15 | | /autoresearch:improve | Research ICP challenges, discover improvements, generate PRDs | 15 | | /autoresearch:evals | Analyze iteration results: trends, plateaus, regressions | N/A | | /autoresearch:regression | Regression stability gate: baseline vs candidate, verdict STABLE/UNSTABLE | N/A |

Universal Flags

| Flag | Applies To | Purpose | |---|---|---| | Iterations: N | All looping | Set iteration count | | Iterations: unlimited | All looping | Opt-in unbounded | | --evals | All looping | Mid-loop checkpoints + final summary | | --evals-interval N | All looping | Override checkpoint frequency | | --chain <targets> | All | Sequential handoff after completion | | --<subcommand> | All | Shorthand for --chain <subcommand> | | --dry-run | Orchestrator | Print derived config + planned pipeline; no execution | | --max-cycles N | Orchestrator | Hard ceiling on orchestration cycles (default 50) | | --classic | Bare /autoresearch | Force Classic metric-loop mode | | --auto | Bare /autoresearch | Force Orchestrator mode |

Orchestrator

Activated when a plain-language goal is given without Metric:/Verify:. Classifies the goal into a Goal archetype — see references/orchestrator-routing.md for the archetype table and router decision table.

Two modes based on archetype:

  • Orchestration loop — predicate-bearing archetypes (ship-ready, optimize-metric, fix-broken, harden, build-feature, explore). Goal has a mechanical Success predicate; the loop runs until that predicate is met.
  • Single-pass dispatch — subjective/terminal archetypes (document, what-to-build, decide-design). Routes once to the fitting subcommand (learn / improve / reason), lets it self-terminate, then reports. No loop, no Plateau, no ship gate.

Orchestration Loop Steps

Backed by scripts/orchestrate.sh (deterministic seam — all routing logic lives there). Subcommands exposed: classify, next-hop, units, plateau, screen-cmd, verdict, validate-state, screen-state-predicate.

  1. Classify — scripts/orchestrate.sh classify "<goal>" → archetype label + mode.
  2. Derive predicate — reuse plan logic to produce a concrete Success predicate: exact shell command + expected output. For optimize-metric, run the full plan/wizard derivation internally.
  3. Confirm — ONE AskUserQuestion showing: archetype, mode, concrete predicate (command + expected output), terminal choice (stop-at-verified vs proceed-to-ship). Misclassifications are caught here, not mid-run.
  4. Round-0 dry-run — prove the predicate command runs and returns a value; safety-screen every derived command via screen-cmd; print projected cycle budget. Stop here if --dry-run.
  5. Loop until predicate satisfied:

a. Assess state via cheap signals (last handoff.json, regression verdict, error count) + affected-test verify. b. scripts/orchestrate.sh next-hop orchestrator-state.json → next subcommand. c. Run subcommand (its own bounded inner loop). d. Record per-hop outcome ∈ {progressed, no-op, failed, blocked}. e. Fold hop's handoff.json into orchestrator-state.json. f. scripts/orchestrate.sh units → recompute Units remaining.

  1. Stop conditions (checked after each hop):
  • Predicate met → ship gate (only if ship is in the pipeline) else CONVERGED.
  • scripts/orchestrate.sh plateau orchestrator-state.json → true → stop + report PLATEAU.
  • Cycles > ceiling (default 50, override --max-cycles N) → stop + report CEILING.
  • Hop outcome blocked/failed with no alternative route → checkpoint + stop + report BLOCKED.

Orchestrator State

orchestrator-state.json — orchestrator-owned, additive. Tracks: goal, archetype, predicate, terminal-choice, units_remaining history, cycle count, per-hop pipeline log with outcomes, current incumbent. Each hop's handoff.json is unchanged (single-hop bridge); the orchestrator reads it and folds it in. Two clearly-owned state objects, no overlap.

Orchestrator Safety Invariants

  • Never auto-approve ship/deploy/push. The orchestrator never passes --auto to ship; deploy always requires explicit user approval.
  • Data-migration behind anchored DB-URL allowlist. Reuses regression's allowlist — host must be localhost/127.0.0.1/container hostname, or database name carries _test/_ci suffix. Bare substring match does not qualify. Anything else refused.
  • screen-cmd on every derived command — run before the loop starts AND on every command read from a persisted state file on resume. Persisted commands are never trusted; resume re-screens the pinned predicate via screen-state-predicate and refuses on refuse.
  • No un-screened commands mid-loop. The autonomous loop cannot introduce new shell commands that bypass screen-cmd.
  • Predicate pinned, not re-derived. Round-0 writes the derived Success predicate verbatim into orchestrator-state.json; every cycle and every resume reuses that exact string so "done" is reproducible across runs.
  • Validate the ledger before routing. validate-state gates orchestrator-state.json (required fields + coarse types); a malformed ledger is not trusted to route from.
  • Independent verify before convergence. High-impact changes accepted on the working signal set pending_verify; next-hop routes to a verify hop (held-out / adversarial check) before DONE or ship. The verify hop never auto-approves ship.
  • Unknown-units cycles excluded from Plateau counter. A cycle where units returns unknown (e.g. runner crash) is not counted as zero-progress; repeated unknown routes to BLOCKED.

Score

0–100
82/ 100

Grade

B

Popularity22/30

5,759 GitHub stars on the source repo — strong adoption. Install counts are not tracked for this skill.

Completeness30/30

Complete listing: SKILL.md body, description, install command, and metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness15/15

Updated within the last 90 days.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Autoresearch skill score badge previewScore badge

Markdown

[![Autoresearch skill](https://www.claudemarket.ai/skills/uditgoenka/autoresearch/badges/score.svg)](https://www.claudemarket.ai/skills/uditgoenka/autoresearch)

HTML

<a href="https://www.claudemarket.ai/skills/uditgoenka/autoresearch"><img src="https://www.claudemarket.ai/skills/uditgoenka/autoresearch/badges/score.svg" alt="Autoresearch skill"/></a>

Autoresearch FAQ

How do I install the Autoresearch skill?

Run “git clone https://github.com/uditgoenka/autoresearch.git” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Autoresearch skill do?

An autonomous iteration loop, inspired by Karpathy's autoresearch concept, that repeatedly modifies, verifies, and keeps or discards changes against a metric until a goal is met, with dedicated subcommands for debugging, fixing, security audits, shipping, and more. The SKILL.md section on this page shows the exact instructions the skill gives your agent.

Is the Autoresearch skill free?

Yes. Autoresearch is a free, open-source skill published under the MIT license by uditgoenka. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Autoresearch work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Autoresearch works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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