arena
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
Skill by hidenobunagai
Chalk Labs
Need help deploying arena?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
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/hidenobunagai-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.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: productivity
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
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 Open a `todo_write` list 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 from the `arena` role, one call per seat. Spawn more runners when the arena covers multiple design directions. Use the same route N times when the work is generation-bound rather than judgment-sensitive. Per the shared rule below. **Child model selection may be unavailable.** Before the first spawn in a session, glance at the `subagent` tool's own schema. When it declares `provider`, `model`, and `reasoning_effort`, pass the role's route from `config/models.yml` of this repo (or the user's `~/.dsh/pstack-models.yml`). When it declares only `prompt`, `description`, and `cwd`, every child runs on the parent's route: omit the route entirely, and get the diversity this step needs from the briefs instead. Never pass a field the schema does not declare. 4. Give each candidate its own output location. Use `jj workspace add` when the candidate writes into a repo, otherwise `/tmp/arena-<slug>/candidate-<n>/`, per the **separate-before-serializing-shared-state** principle skill. ## Phase B: Fan out Issue all N `subagent` calls in one message. They run concurrently, so there is no background flag. Each gets 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, take the `arena_judge` role. If the role is missing, use the `fallback`. Prefer a different model family from the parent's. Spawn one read-only judge subagent on that route, stating in its brief that it must not edit files. 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 hidenobunagai/dsh-pstack, MIT.
More from hidenobunagai/dsh-pstack
- 0.520score
poteto-mode
hidenobunagai
poteto's agent style for concise, detailed responses, deliberate subagents, unslopped prose, simple code, and verified work.
SkillAFreshness: AClaude Code +9 - 0.520score
create-verification-skill
hidenobunagai
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
SkillAFreshness: AClaude Code +9 - 0.520score
how
hidenobunagai
Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which…
SkillAFreshness: AClaude Code +9 - 0.520score
interrogate
hidenobunagai
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 - 0.520score
architect
hidenobunagai
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillAFreshness: AClaude Code +9 - 0.518score
correct
hidenobunagai
Find the mistakes agents keep repeating in this repo and make each one impossible.
SkillAFreshness: AClaude Code +9 - 0.517score
no-comments
hidenobunagai
Spawn Comment Sicko, fix accepted findings, and offer encodings for claimed constraints.
SkillAFreshness: AClaude Code +9 - 0.515score
blast-radius
hidenobunagai
Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of…
SkillAFreshness: AClaude Code +9 - 0.515score
maintain-verification-skill
hidenobunagai
Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature…
SkillAFreshness: AClaude Code +9
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 22, 2026 (8 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/hidenobunagai/dsh-pstack
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from hidenobunagai
- 0.568score
architect
hidenobunagai
Sketch data shapes (grain, keys, schemas), function signatures, and the module or pipeline structure before code, then stay in the loop while implementation…
SkillAFreshness: A★ 1Claude Code +9 - 0.568score
astack-help
hidenobunagai
Help a user pick the right astack skill, analyst-mode playbook, or principle for a data analysis task, such as EDA, a metric move, a notebook, a pipeline, or…
SkillAFreshness: A★ 1Claude Code +9 - 0.563score
benchmark-checklist
hidenobunagai
Vet a runtime measurement for a query, dataframe transform, pipeline, or notebook before you report or act on it: limiter, tuning, data volume, warm vs cold…
SkillAFreshness: A★ 1Claude Code +9 - 0.563score
analyst-mode
hidenobunagai
Data analysis style for an analysis repo.
SkillAFreshness: A★ 1Claude Code +9 - 0.563score
blast-radius
hidenobunagai
Find what a change to a table, column, transform, metric, or notebook could break downstream (dashboards, saved queries, notebooks, consumers) before it…
SkillAFreshness: A★ 1Claude Code +9 - 0.560score
no-comments
hidenobunagai
Spawn the comment-critic agent on a data repo's code, notebooks, or SQL, act on its accepted findings, and offer encodings for claimed constraints.
SkillAFreshness: A★ 1Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get arena running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
arena skill. Agent Mart. https://agentmart.dev/skills/hidenobunagai-arena (last updated 2026-10-24)