arena
Skill3 starsChanged 7 days agoMITView on GitHub
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.
84 other projects use a version of this, 57 of them edited.
Adopted by
Most recently taken up by
- Yeachan-Heo/oh-my-claude-sisyphusedited, first seen Nov 8, 2026
- anorth/agent-skillsedited, first seen Nov 8, 2026
- thatmike1/claude-skillstheir own version, first seen Nov 8, 2026
- Yeachan-Heo/oh-my-claudecodeedited, first seen Nov 8, 2026
- endqwerty/endstackedited, first seen Nov 8, 2026
- tristanphvn/keeledited, first seen Nov 8, 2026
Used with
Projects with it also use addskill, apply-firmware and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
ryanportfolioSkilladdskill
Create, import, update, or install repo or personal skills: runtime ownership, resources, discovery checks.
Also in 6 projects that use this
ryanportfolioSkillapply-firmware
Bring current folder (plain folder, existing repo, old or partial firmware copy) up to full Harness Firmware in place. Mirror external repo → adopt-repo.
Also in 6 projects that use this
- +10 more from ryanportfolio
Other versions84 projects use a version of this.
Word for word · 0
None found.
Edited · 57
- Yeachan-Heo/oh-my-claude-sisyphus★ 41k15% same
- Yeachan-Heo/oh-my-claudecode★ 41k15% same
- backnotprop/pstack★ 3.8k53% same
- moeru-ai/xsai★ 66764% same
- jtaroreh/agystack★ 12812% same
- uzairansaruzi/p3-stack★ 12633% same
- dsebban/skills★ 7145% same
- overextended/skills★ 2247% same
- creedants/pstack-t3★ 1842% same
- painhardcore/pstack★ 1865% same
47 more not listed
Wrote their own version · 27
- TheOnlyFusionCube/potetos-for-everyone★ 21same name
- korallis/skills-collection★ 14same name
- thatmike1/claude-skills★ 10same name
- Jakeschincariol/arena-skill★ 6same name
- therealtinhtute/mono-harness★ 5same name
- kinqsradio/orbyn-fullstack-skills★ 4same name
- orbyn-dev/orbyn-fullstack-skills★ 4same name
- requie/endostack★ 4same name
- 0xUrsanomics/utopia-os★ 2same name
- EzraApple/skills-init★ 2same name
17 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository33 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- sync-upstreamSkill
- architectSkill
- automate-meSkill
- benchmark-checklistSkill
- blast-radiusSkill
- broSkill
- correctSkill
- create-formal-verification-skillSkill
- create-verification-skillSkill
- figure-it-outSkill
- howSkill
- interrogateSkill
- in-your-own-wordsSkill
- ketchupSkill
- ketchup-videoSkill
- maintain-formal-verification-skillSkill
- maintain-verification-skillSkill
- no-commentsSkill
- poteto-helpSkill
- poteto-modeSkill
- recallSkill
- reflectSkill
- sharpenSkill
- show-me-your-workSkill
- statechartSkill
- swarmSkill
- tddSkill
- teachSkill
- technical-writingSkill
- typescript-best-practicesSkill
- unslopSkill
- whySkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 83 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
Every candidate gets the same prompt, so the prompt is the contract.
- Distinctive: line 24
If a candidate produces no output, proceed with N-1 and note the dropout.
- Distinctive: line 40
If the candidates converge on one shape, that is strong agreement.
“Common” is measured: kept by at least 6 in 10 of the 83 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Arena
Open a todolist 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.
- State the artifact each candidate is producing.
- 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.
- Pick the runners from the
arena runnersrole in${CLAUDE_SKILL_DIR}/../poteto-mode/references/models.md, one per entry. Spawn more runners when the arena covers multiple design directions. Use the same model N times when the work is generation-bound rather than judgment-sensitive. - Give each candidate its own output location (
isolation: "worktree"where possible, otherwise/tmp/arena-<slug>/candidate-<n>/), per principle-separate-before-serializing-shared-state. Principles live at${CLAUDE_SKILL_DIR}/../poteto-mode/principles/<name>.md.
Phase B: Fan out
Runners load no pstack skill, so make every skill-relative path and principle name in the task absolute before you spawn them. Spawn all N subagents with the Agent tool 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.
Phase C: Cross-judge
After all Phase B candidates complete, choose the first model in the arena cross-judge pool role in models.md that differs from the parent's model. If every entry matches the parent's model, skip the judge, pick the base alone in Phase D, and say so. Spawn one judge on that model with subagent_type: "pstack:read-only". 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 when Phase C ran one. 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 principle-redesign-from-first-principles, 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 principle-prove-it-works. 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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

