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Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
Passed 9 of 16 test cases with the skill, and 5 of 16 without it.
and 10 more in the full report
8 fixed5 passed either way7 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.6-flash on 10/4/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are setting up candidate subagents for a C# distributed key-value cache arena task. There is no `~/.cursor/rules/pstack-models.mdc` file present in the project. The developer suggests using four instances of standard gpt-4o for all candidates. Which runner models should be assigned by default?
| Checked | Without skill | With skill |
|---|---|---|
| assigns candidate runner models using the default set containing claude-fable-5-1-thinking-max | Missed | Passed |
| spawns candidates across multiple distinct model families | Missed | Passed |
The instructions your model reads when the skill loads.
Open a todolist with one entry per phase (Frame, Fan out, Cross-judge, Pick, Graft, Verify) before launching anything.
Every candidate gets the same prompt, so the prompt is the contract.
arena runners line in ~/.cursor/rules/pstack-models.mdc. If the rule or that line is missing, default to one each on claude-opus-5-5-xhigh and grok-4.7-xhigh-fast. An auto or inherit-parent entry in this line or the cross-judge line means the parent model, so omit model for it. If the Task tool rejects a configured entry, run that seat on its family's default and say so. Families go by prefix: claude-*, and grok-* with cursor-grok-* and composer-*. A refused composer-* entry runs on grok-4.7-xhigh-fast. With no family match, use claude-opus-5-5-xhigh. If it rejects a default, use the closest valid slug of the same family from its error message. Spawn more runners when the arena covers multiple design directions. Use the same model N times when the work is generation-bound rather than judgment-sensitive. Pick each subagent's reasoning level and fast or standard speed for the job, per the Subagents section of the poteto-mode skill./tmp/arena-<slug>/candidate-<n>/), per the separate-before-serializing-shared-state principle skill.Spawn all N subagents in one message with run_in_background: true, each with the task, the path to the shared grounding, its own output path, and instructions to produce the artifact and a short rationale naming the alternatives it considered and what it rejected.
If a candidate produces no output, proceed with N-1 and note the dropout.
After all Phase B candidates complete, choose one model from the arena cross-judge pool line in ~/.cursor/rules/pstack-models.mdc. If the rule or that line is missing, choose from claude-opus-5-5-xhigh and grok-4.7-xhigh-fast. Prefer a different model family from the parent's. Spawn one readonly judge subagent on that model. It sees the rubric and the candidates by path label, scores each criterion, and recommends a base with rationale. It runs in parallel with your reading in Phase D. Don't spawn it while candidates are still writing.
Read every candidate end to end before picking. Score each against the rubric criterion by criterion, not on holistic feel, and compare with 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 a future maintainer can extend most easily without breaking invariants. When two feel tied, prefer the cleaner boundary or smaller API, per the Laziness Protocol.
Walk each losing candidate once more for what is worth porting into the base, 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, so the result stays coherent under one mental model. Don't paste mechanically.
If the candidates converge on one shape, that is strong agreement. Note it and ship the consensus shape. No graft is needed. If they wildly diverge, Phase A was under-specified. Reframe and re-run rather than averaging the divergence.
Verify the synthesized artifact like any other output, per the prove-it-works principle skill. If verification finds a problem the arena missed, either Phase A was wrong (re-frame and re-run) or a candidate caught it and you missed the graft (go back to Phase E). Don't paper over it.
test cases passed, vs 5 / 16 without
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