arena
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
Skill by fitchmultz
Chalk Labs
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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/fitchmultz-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
- 46 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Arena Read [the Pi execution contract](../../docs/harness/pi.md) and select the authorized delegated or inline route. Recheck before delegation. Report self-performed work and any loss of independence, isolation, model diversity, or parallelism. Track one entry per phase (Frame, Candidates, Cross-judge, Pick, Graft, Verify) using an available task list, a task-owned file, or the conversation. ## 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. Choose the candidate count and the distinct directions needed for this task. Honor the caller's count. For architecture, produce at least two structurally distinct candidates. Use configured `arena runners` choices from `~/.pi/agent/pstack-models.md` when present and available; otherwise inherit the session model. Do not invent model IDs or claim diversity you did not obtain. 4. Give each candidate its own output location (a git worktree where appropriate, otherwise a task-owned candidate directory), per the **separate-before-serializing-shared-state** principle skill. ## Phase B: Candidates With an authorized provider, launch candidates with its actual schema and lifecycle. Give each the same task, shared grounding, its own output path, and instructions to produce the artifact and a short rationale naming alternatives considered and rejected. Read actual completed results. Without a usable provider, create the same candidate artifacts serially in their own locations. Record that there was no parallel race or independent author. If a candidate produces no output, report the dropout; create a replacement when the required candidate count is not met. ## Phase C: Cross-judge After all candidates finish, request an independent read-only judge when an authorized provider and suitable model are available. Give it the rubric and candidate paths; it scores each criterion and recommends a base with rationale. Do not start it while candidates are still writing. Otherwise score each candidate yourself against the same rubric and state that the judge was not independent. An optional `pstack_opinion` may provide a text-only perspective on supplied material, but cannot inspect files or count as a tool-using judge. ## 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 an independent cross-judge when one ran. 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 or that Phase C skipped the judge, each graft with its source candidate, what was rejected and why, any convergence or dropouts, and the verification result.
From fitchmultz/pi-pstack, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 1
- Last commit (this folder)
- Oct 20, 2026 (11 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/fitchmultz/pi-pstack
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from fitchmultz
- 0.541score
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fitchmultz
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillAFreshness: AClaude Code +9 - 0.541score
create-verification-skill
fitchmultz
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
SkillAFreshness: AClaude Code +9 - 0.541score
interrogate
fitchmultz
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".
SkillAFreshness: AClaude Code +9 - 0.541score
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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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poteto-mode
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SkillAFreshness: AClaude Code +9 - 0.536score
automate-me
fitchmultz
Use for "automate me", "create/update/refresh my -mode skill", "turn/capture my preferences or working style into a skill", or wanting agents to follow how…
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Cite this page
arena skill. Agent Mart. https://agentmart.dev/skills/fitchmultz-arena (last updated 2026-10-24)