version: "1.0.1" name: arena description: "Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it. Use for /arena, 'arena this', 'throw it in the arena', or when one attempt at a non-trivial artifact would lock in the wrong shape."

Arena

Fan out N parallel attempts at the same task. Read every candidate end to end. Pick the strongest as the base. Graft the best ideas from the others into it. Verify the synthesized result.

Dispatch contract. Read `provider-dispatch.md` before fan-out. Configured values are Routes written as [provider:]model@effort. The parent starts the lanes directly; children never route themselves. On Codex, resolve the remaining Claude tool names via `codex-tools.md`.

Start

Open a todolist with one entry per phase before launching anything.

1. Frame
2. Fan out
3. Cross-judge
4. Pick
5. Graft
6. Verify

Phase A: Frame

The N candidates will receive the same prompt, so the prompt is the contract.

1. State the artifact each candidate is producing.
2. Derive the rubric. State what success looks like for this task, then turn it into 3-6 concrete gradeable criteria. The rubric is the picker's tool in Phase D. Candidates only see the task.
3. Pick the runners. Use the arena runners line in the active model sheet (see `t3-tools.md`). If the sheet or that line is missing, default to inherit-parent: every runner is the parent model, and /setup-engineering-toolkit configures a panel. An auto or inherit-parent entry in this line or the cross-judge line means the parent model. A Route follows `provider-dispatch.md`. If a configured entry does not resolve, record a dropout and continue. Never pick the closest model or silently fall back. Spawn more when the arena covers multiple design directions. Same descriptor N times when the work is generation-bound rather than judgment-sensitive.
4. Assign output paths. Each candidate writes to its own location (a git worktree where possible, otherwise /tmp/arena-<slug>/candidate-<n>/), per the separate-before-serializing-shared-state principle skill.

Phase B: Fan out

Start all N lanes in one fan-out phase through the provider-dispatch contract. Lanes are background subagents with retained task handles. Give every lane the task, the path to the shared grounding, its own output path, and instructions to produce both the artifact and a short rationale.

Each rationale names the alternatives the candidate considered and what it rejected.

A lane counts when its child returned a non-empty result on the assigned model. If a candidate fails, proceed with N-1 and note the exact dropout in the synthesis record. Never replace it with another provider silently.

Phase C: Cross-judge

After all Phase B candidates complete, choose the judge descriptor from the arena cross-judge pool line in the active model sheet. If the sheet or that line is missing, the judge is the parent model. Prefer a provider different from the parent and the likely base candidate when the sheet offers one. Dispatch one read-only judge through the provider contract. It sees the rubric and completed candidates by path label, scores each criterion, and recommends a base with rationale. It runs in parallel with the parent's reading in Phase D, not with the candidates themselves. Don't dispatch the judge while candidates are still writing.

Phase D: Pick a base

Read every candidate end to end before picking.

Score each candidate against the rubric criterion by criterion, not on holistic feel. Compare against the cross-judge. Agreement on the base confirms the pick only when the judge is a different model from the candidate. A judge on the parent model repeating the parent's own candidates is a same-model check, not confirmation. Disagreement means one of you is biased or the rubric was ambiguous. Read both rationales before deciding.

Pick the base on which candidate a future maintainer can extend most easily without breaking invariants. Prefer the cleaner boundary or smaller API when two feel tied, per the Laziness Protocol.

Record the pick and the reason in a short synthesis note alongside the base artifact, including the cross-judge's verdict.

Phase E: Graft

Walk each losing candidate once more and identify what is worth porting into the base. The signal is usually one or two things per candidate, not most of it.

Fold each graft in by hand, per the redesign-from-first-principles principle skill. Don't paste mechanically. The result has to remain coherent under one mental model.

Record what was grafted, from which candidate, and what was rejected and why.

When N candidates converge on the same shape, that is a strong agreement signal when the runners are different models. Same-model convergence is weak evidence. Note the convergence in the record and ship the consensus shape. No graft is needed. When N candidates wildly diverge, Phase A was under-specified. Reframe and re-run rather than averaging the divergence.

Phase F: Verify

The synthesized artifact has to hold up under the same scrutiny as any other output, per the prove-it-works principle skill.

If verification surfaces a problem the arena did not catch, either Phase A was wrong (re-frame and re-run) or one candidate caught it and you missed the graft (go back to Phase E). Don't paper over.

Outputs

One synthesized artifact. One short synthesis note alongside, naming the base, the grafts (with source candidate), the rejections, the dropouts if any, and the verification result.