agentleFS
ychampionychampion/ystack/skills/arena/SKILL.md

arena

Skill4 starsChanged 2 days agoMITView on GitHub

Run 2-4 independent attempts at the same task in parallel, score them against one acceptance check, pick the best as the base and graft in the strongest parts of the others. Use for "arena this", "try a few approaches", "give me options and pick the best", or when one attempt at a design-heavy, open-ended or hard-to-get-right piece of work is likely to come out mediocre.

88 other projects use a version of this.

Adopted by

Most recently taken up by

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.
  • ryanportfolioSkill

    addskill

    Create, import, update, or install repo or personal skills: runtime ownership, resources, discovery checks.

    Also in 6 projects that use this

  • ryanportfolioSkill

    apply-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

The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 6 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 86 edited versions we read keep these.

  • 6

    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
  1. Distinctive:

    When there are several reasonable ways to do something, a single attempt picks one of them by accident.

    line 12
  2. Distinctive:

    Start every attempt at once (parallel subagents, or separate CLI runs), each told to work only in its own worktree and to report what it …

    line 55
  3. Distinctive:

    Run the acceptance check yourself on every attempt.

    line 60
  4. Distinctive:

    Run the check again on the result.

    line 75
  5. Distinctive:

    Remove the worktrees and branches you created (git worktree remove, git branch -D), keeping only the merged result.

    line 79
  6. Distinctive:

    If no attempt passes, the spec or the check is probably wrong.

    line 87

“Common” is measured: kept by at least 6 in 10 of the 86 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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Arena

When there are several reasonable ways to do something, a single attempt picks one of them by accident. An arena tries a few on purpose, judges them on the same test, and keeps the best parts.

When it applies

  • The result depends on choices more than on effort: an algorithm, an API shape, a data model, a UI layout, a prompt, a piece of copy.
  • An earlier attempt came back mediocre and it's not clear why.
  • The work is small enough that 2-4 copies are affordable.

Skip it for mechanical work (a rename, a known fix, a dependency bump), where every attempt converges on the same answer and the arena only multiplies cost.

Say the cost before starting: N attempts cost roughly N times one attempt, plus judging.

Procedure

1. Write the spec and the acceptance check first

Both are shared by every attempt and written before any attempt starts:

  • the spec: what to build, inputs and outputs, constraints, what's out of scope;
  • the check: something that runs and scores. Tests that must pass, a benchmark with a target, a screenshot compared at three widths, a rubric with numbered criteria for copy. If the check can't tell the attempts apart, sharpen it before going on.

2. Make the attempts different on purpose

Identical briefs to identical models give near-identical results. Vary one thing per attempt:

  • the approach ("use a state machine", "use a lookup table", "stream it");
  • the model or model family;
  • the priority ("simplest that passes", "fastest", "most readable").

3. Run them isolated and in parallel

Give each attempt its own git worktree and branch so they can't step on each other:

git worktree add ../arena-a -b arena/a
git worktree add ../arena-b -b arena/b

Start every attempt at once (parallel subagents, or separate CLI runs), each told to work only in its own worktree and to report what it built, how it scored on the check, and the assumptions it made.

4. Judge

Run the acceptance check yourself on every attempt; don't take an attempt's word for its own score. Then read the ones that pass. Lay out a table:

| Attempt | Check | Size | Strongest part | Weakest part |
| a | 14/14 tests, 38 ms | +210 lines | error handling | naming |
| b | 13/14 (fails empty input) | +120 lines | the core loop | no validation |

5. Pick a base, then graft

Pick the attempt that passes the check and is simplest to keep as the base. Bring over the strongest part of each other attempt only where it makes the base better on the check or clearly simpler. Grafting everything produces a patchwork; graft only what earns its place.

Run the check again on the result. It must score at least as well as the best single attempt.

6. Clean up

Remove the worktrees and branches you created (git worktree remove, git branch -D), keeping only the merged result. Report the table, what was grafted from where, and the final check output.

Rules

  • The acceptance check exists before the first attempt starts, and the same check judges all of them.
  • You run the check; attempts don't grade themselves.
  • Two attempts is a valid arena. Four is the most that's usually worth it.
  • If no attempt passes, the spec or the check is probably wrong. Fix that, don't add attempts.

Download now (ZIP)

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
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.