autoresearch
Autonomous iteration loop: modify, verify, keep/discard against any metric
Adversarial debate with blind judges until convergence
> /plugin marketplace add uditgoenka/autoresearch > /plugin install autoresearch@autoresearch
How it fires
How this command gets triggered: by you, by Claude, or both.
/reasonContext preview
What this command does when you run it.
Adversarial debate with blind judges until convergence
name: autoresearch:reason description: "Adversarial debate with blind judges until convergence" argument-hint: "[Task: <question>] [Domain: <type>] [--mode convergent|creative|debate] [--judges N] [Iterations: N] [--evals]"
EXECUTE IMMEDIATELY.
Extract from $ARGUMENTS:
Remaining text not matching flags = task description.
AskUserQuestion (single batch): Q1 (Task): "What should be reasoned about?" — open text Q2 (Domain): "What domain?" — software architecture, product strategy, business decision, security, research, content Q3 (Mode): "Refinement mode?" — convergent (stop when winner repeats), creative (never auto-stop), debate (no synthesis) Q4 (Judges): "How many blind judges?" — 3 (default), 5 (thorough), 7 (deep) If all provided → skip.
1. Load `references/reason-judge-protocol.md` for judge and convergence specs 2. Parse domain → select domain-specific judge criteria 3. Create output directory: `autoresearch/reason-{YYMMDD}-{HHMM}/` 4. TSV header: `round\ttimestamp\tcandidate_label\tjudge_verdict\tconvergence_count\tdescription` 5. Initialize: incumbent = null, convergence_count = 0
If incumbent changed 5+ times in last 8 rounds → recommend early stop (not converging).
Append to TSV: round, timestamp, winning candidate label, judge verdict, convergence_count, description
If --evals: check if current_round % interval == 0 → run checkpoint.
If bounded: current_round >= max_iterations → exit loop.
Print: total rounds, convergence status, final winner summary, judge agreement rate.
If --evals present:
After completion, write handoff.json: version "2.1.0", source "reason", timestamp, status (COMPLETE|CONVERGED|USER_INTERRUPT|BOUNDED|ERROR), results_tsv path, findings = [{id, type: "recommendation", summary: winner description}], config{task, domain, mode}. Invoke next target in --chain order. Propagate --evals flag.
Turn Claude Code, OpenCode, or OpenAI Codex into a relentless improvement engine. Based on Karpathy's autoresearch — constraint + mechanical metric + autonomous iteration = compounding gains.
Repo: uditgoenka/autoresearch
Autonomous iteration loop: modify, verify, keep/discard against any metric
Analyze iteration results: trends, plateaus, regressions, recommendations
Scout codebase and auto-generate docs — or a navigable wiki knowledge base — with validation-fix loop