arena
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
Skill by shrimpwtf
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/shrimpwtf-arena
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: productivity
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/arena
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 /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
- 69 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Arena Follow the [portable runtime contract](../pstack-pi/references/runtime.md) for child briefs, parallel execution, panels, models, and fallbacks. 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 Track one checklist entry per phase before launching anything. Use the host's planning facility when available; otherwise keep a compact checklist in the conversation or project-local decision trail. The arena runs autonomously and the list keeps phases from silently disappearing. 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. Get it right before spawning anything. 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. Concrete: `Adds a --dry-run flag that skips writes`. Vague: `code is correct`. The rubric is the picker's tool in Phase D; candidates only see the task. 3. Pick the runners. Use the `arena runners` panel from the portable pstack configuration when present. Otherwise use a diverse host-supported panel drawn from `designer`, `planner`, `reviewer`, and `inherit-parent`. Spawn more when the arena covers multiple design directions. Repeating one host-supported choice N times is valid 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>/`). N candidates writing to the same path is shared mutable state and fails the the **separate-before-serializing-shared-state** principle skill test. ## Phase B: Fan out Launch all N children concurrently through the host's task facility. Give each a standalone brief with the task, the path to the shared grounding, its own output path, acceptance criteria, verification, forbidden scope, and instructions to produce both the artifact and a short rationale. The rationale is mandatory. Without it, the parent cannot tell whether a candidate's structure is principled or accidental, which makes Phase E grafting unreliable. 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, choose one choice from the `arena cross-judge pool` in the portable pstack configuration when present. Otherwise use the host's `reviewer` role or `inherit-parent`. Prefer a different model family from the parent's when the host exposes model identity. Launch one read-only judge child on that choice. 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. Spawning while candidates are still writing means the judge sees partial or empty outputs and reports them as dropouts. ## Phase D: Pick a base Read every candidate end to end before picking. Skimming N candidates surfaces only the candidate whose surface looks most familiar. 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 surface area 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. The rejection notes are the highest-signal part of the record. Future readers learn from what you considered and dropped, not just what you kept. 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. The arena does not earn you a pass. 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.
From shrimpwtf/oh-my-pstack, MIT.
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Signals
- Stars
- 39
- Forks
- 6
- Open issues
- 2
- Last commit (this folder)
- Aug 26, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/shrimpwtf/oh-my-pstack
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Configure which host-supported roles or models pstack uses per workflow role.
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Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".
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Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillBFreshness: B★ 39Claude Code +9 - 0.592score
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Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
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Cite this page
arena skill. Agent Mart. https://agentmart.dev/skills/shrimpwtf-arena (last updated 2026-10-24)