Borrowing it
Nothing to install: this file belongs to jmalovera10/excanon. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jmalovera10/excanon/main/.agents/skills/arena/SKILL.mdgit clone --depth 1 https://github.com/jmalovera10/excanonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jmalovera10/excanon/arena)<a href="https://agentmods.dev/skills/jmalovera10/excanon/arena"><img src="https://agentmods.dev/badge/skills/jmalovera10/excanon/arena/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jmalovera10/excanon/arena"><img src="https://agentmods.dev/badge/skills/jmalovera10/excanon/arena.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.00456 |
| Opus 5.5 | $0.00023 | $0.00182 |
| Sonnet 5.5 | $0.00011 | $0.00091 |
| Haiku 4.5 | $0.00006 | $0.00046 |
Grade A, and why
arena scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Arena
Candidates attempt the same task independently. A judge who wrote none of them scores each against a rubric, picks a base, and names what to graft from the others.
When to use
- Several plausible designs exist, for example how to model a new operation or how to structure error handling.
- The first attempt would shape everything built on top of it.
- Don't use it for mechanical work with one obvious answer.
Steps
-
Frame. State the artifact every candidate must produce, for example a spec section, a module or a test layout. Write a rubric of 3–6 gradeable criteria, always including:
- fits
AGENTS.md(smallest complete change, reuses existing structure), - keeps the public API stable unless the spec changes it,
- easy for the next maintainer to extend.
Candidates see the task. Only the judge sees the rubric.
- fits
-
Fan out. Start 2 or 3 candidates at once. Each gets the same brief and its own output location (a separate worktree for code). Each returns the artifact plus a short rationale naming the alternatives it rejected.
-
Judge. Start a fresh judge agent at the
judgmenttier, with the rubric and all candidates labeled only A, B, C. It scores every criterion for every candidate and recommends a base. -
Graft. Brief one agent, never the base candidate's author, to apply the named grafts to the base. Then verify the result with the brief's verify commands.
-
Record. Post the scores, the chosen base and the grafts, with reasons. Rejected candidates are kept as links only and never merged.
Output
The grafted artifact, the score table, and one line per graft.
Cost
Candidates, one judge and one grafter: 4–5 agents.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 36 lines · 57 tokens per session scan A a26de454f08f
arena is a skill published in the GitHub repository jmalovera10/excanon (5 stars, last pushed 6d ago), licensed MIT. It adds 57 tokens to every session and 456 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-11-07.
arena compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| arenajmalovera10/excanon | Skill | 5 | 6d ago | MIT | A | 57 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |