arena
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
Skill by slappcard
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/slappcard-arena
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About
Use cases: productivity
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What's inside
- Skill 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 /skill:arena, 'arena this', 'throw it in the arena', or when one attempt at a non-trivial artifact would lock in the wrong shape.
- License
- MIT
- Instructions
- 44 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Arena Write a checklist in your reply with one entry per phase (Frame, Fan out, Cross-judge, Pick, Graft, Verify) before launching anything. ## Phase A: Frame Every candidate gets the same prompt, so the prompt is the contract. 1. State the artifact each candidate is producing. 2. Derive the rubric, 3-6 concrete gradeable criteria for what success looks like on *this* task. It is the picker's tool in Phase D. Candidates only see the task. 3. Pick the runners. Use the `arena runners` line in the pstack model configuration in `~/.pi/agent/AGENTS.md`. If the rule or that line is missing, default to one each on `anthropic/claude-opus-5-5:xhigh` and `openrouter/x-ai/grok-4.7:xhigh`. An `auto` or `inherit-parent` entry in this line or the cross-judge line means the parent model, so omit `model` for it. If pi rejects a configured model, run that seat on its family's default and say so. Families go by provider prefix: `anthropic/*`, `openrouter/x-ai/*`, `openrouter/openai/*`. With no family match, use `anthropic/claude-opus-5-5:xhigh`. If it rejects a default, use the closest valid model of the same family. 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 thinking level for the job, per the Subagents section of the **poteto-mode** skill. 4. Give each candidate its own output 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 Spawn all N subagents in one `subagents` call (`tasks: [...]`) so they run in parallel, 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. ## Phase C: Cross-judge After all Phase B candidates complete, choose one model from the `arena cross-judge pool` line in the pstack model configuration in `~/.pi/agent/AGENTS.md`. If the rule or that line is missing, choose from `anthropic/claude-opus-5-5:xhigh` and `openrouter/x-ai/grok-4.7:xhigh`. Prefer a different model family from the parent's. Spawn one `pstack-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. ## Phase D: Pick a base 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. ## Phase E: Graft 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. ## Phase F: Verify 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. ## Outputs One synthesized artifact, with a short synthesis note alongside naming the base and why, the cross-judge's verdict, each graft with its source candidate, what was rejected and why, any convergence or dropouts, and the verification result.
From slappcard/pi-pstack-skills, MIT.
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no-comments
slappcard
Spawn Comment Sicko, fix accepted findings, and offer encodings for claimed constraints.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 18, 2026 (10 days ago)
- Last release
- Oct 18, 2026 (10 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/slappcard/pi-pstack-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Guides users through pstack setup, /skill:poteto-mode, and picking the skill, playbook, or principle for a task.
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Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which…
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Cite this page
arena skill. Agent Mart. https://agentmart.dev/skills/slappcard-arena (last updated 2026-10-24)