Arena

---
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."
disable-model-invocation: true
---

# 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.

## 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. Call `orchestrator_capabilities`. Paste that tool result into this quoted heredoc. If the catalog result is large, save it to a temporary file with the host's file tool and pass that path to `--catalog`.

```bash
python3 <pstack-runtime>/scripts/roles.py show --cwd "$PWD" --catalog - --parent "<inheritedProviderInstanceId>/<inheritedModel>" --role "arena runners" <<'JSON'
<the orchestrator_capabilities JSON>
JSON
```

The quoted heredoc sends the JSON unchanged. The command does not write the catalog into the repository, and parallel children do not share a file. It resolves the `arena runners` role per [the runtime's Roles section](../pstack-runtime/SKILL.md#roles). One candidate per seat. The seat count is the panel size. An `inherit` seat means the parent model, so omit `target` for it. A seat that falls back follows the runtime's fallback, and the synthesis note says which seat fell back and whether the models actually differed. Spawn more when the arena covers multiple design directions. Same model 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, per [the runtime's Isolation section](../pstack-runtime/SKILL.md#isolation), otherwise `/tmp/arena-<slug>/candidate-<n>/`), per the **separate-before-serializing-shared-state** principle skill.

## Phase B: Fan out

Spawn all N candidates in one message, one `delegate_task` call per seat, with `mode: "async"`, `role: "design"` (or `implementation` when the artifact is code), the seat's `target` (omitted for `inherit`), and a stable `clientRequestId` such as `arena-<slug>-<n>`. Each brief carries the task, the path to the shared grounding, its own output path, and instructions to produce both the artifact and a short rationale. A candidate that writes code for a poteto-mode playbook also follows step 4 of [the runtime's Delegation section](../pstack-runtime/SKILL.md#delegation): the poteto-agent persona first, the playbook's `Playbook: playbooks/<name>.md` line, and `roles.py check-brief` before `delegate_task`. Retain every returned `taskId`.

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

If a candidate fails to produce output, proceed with N-1 and note the dropout in the synthesis record.

## Phase C: Cross-judge

After all Phase B candidates complete (end the turn and let the completion notifications wake you, or check `task_status`), choose one seat from the `arena cross-judge pool` role. Resolve it with the same quoted heredoc and `--role "arena cross-judge pool"`. Prefer a seat whose model family differs from the parent's model (`inheritedModel` in `orchestrator_capabilities`). Compare models, not providers, because one provider can serve another's models. If every seat shares the parent's family, say the judge is not cross-family. Spawn one judge child on that seat with `delegate_task`, `mode: "async"`, `role: "review"`, and a read-only brief that says "do not edit files, commit, or push." It sees the rubric and the 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 spawn 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. 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. 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, the seats that fell back, whether the models actually differed, and the verification result. Remove candidate worktrees once the graft is done.