/reason

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]"
disable-model-invocation: true

EXECUTE IMMEDIATELY.

Parse Arguments

Extract from $ARGUMENTS:

• `Task:` — question, proposal, design, argument, or claim to refine
• `Domain:` or `--domain` — software, product, business, security, research, content
• `Mode:` or `--mode` — convergent (default), creative, debate
• `--judges N` or `Judges:` — blind judge count (3 default, 5 thorough, 7 deep)
• `--convergence N` or `Convergence:` — stop when incumbent wins N consecutive rounds (default 3)
• `Iterations:` or `--iterations` — default 8. "unlimited" for unbounded.
• `--judge-personas` — custom judge persona overrides
• `--no-synthesis` — skip synthesis, pure debate only
• `--temperature` — generation temperature hint
• `--evals`, `--evals-interval N`, `--chain`, `--<subcommand>`

Remaining text not matching flags = task description.

Setup (if Task or Domain missing)

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.

Setup Phase

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

Round Loop

Phase 1: Generate-A

• If round 1: Author-A generates first candidate from task description
• If round N>1: incumbent is Author-A's candidate
• Cold-start: Author-A sees ONLY task description + domain context

Phase 2: Critic

• Critic receives candidate-A (cold-start, no shared session)
• MUST find at least 3 specific weaknesses
• MUST suggest what a superior candidate would do differently
• Role is purely adversarial — never compliment

Phase 3: Generate-B

• Author-B receives: task + candidate-A + critique (cold-start)
• Produces candidate-B addressing critique while preserving A's strengths

Phase 4: Synthesize (unless --no-synthesis or debate mode)

• Synthesizer receives: task + A + B (cold-start)
• Produces hybrid candidate-AB merging best of both

Phase 5: Blind Judge Panel

• Each judge receives 3 candidates with RANDOMIZED labels (Label-X, Label-Y, Label-Z)
• Judges evaluate independently on domain-specific criteria
• Each produces ranking + one-paragraph justification
• Verdict: majority vote. Tie → synthesized candidate wins.

Phase 6: Convergence Check

• If winner == incumbent → convergence_count++
• If winner != incumbent → convergence_count = 1, winner becomes incumbent
• **Convergent mode**: convergence_count >= N → CONVERGED, stop
• **Creative mode**: never auto-stop
• **Debate mode**: same as convergent, no synthesis

Phase 7: Oscillation Guard

If incumbent changed 5+ times in last 8 rounds → recommend early stop (not converging).

Phase 8: Log

Append to TSV: round, timestamp, winning candidate label, judge verdict, convergence_count, description

Eval Checkpoint

If --evals: check if current_round % interval == 0 → run checkpoint.

Bounded Check

If bounded: current_round >= max_iterations → exit loop.

Output

• `reason-results.tsv` — per-round results
• `lineage.md` — full history of candidates + critiques + judge reasoning
• `summary.md` — final winner, convergence trajectory, key insights

Summary

Print: total rounds, convergence status, final winner summary, judge agreement rate.

Eval Checkpoint (--evals flag)

If --evals present:

• Compute interval: floor(max_iterations / 3), min 1. Fixed 10 if unbounded.
• Print: `--- Eval Checkpoint (rounds {X}-{Y}) ---\nIncumbent: {label} | Convergence: {count}/{target} | Oscillations: {n}\n{recommendation}\n---`
• If oscillation detected 3+ checkpoints → recommend early stop.
• At loop end → full evals summary to evals-summary.md.

Chain Handoff

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 via the `autoresearch` skill's Subcommand routing. Propagate --evals flag.