Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/machbuilds/atom/model-racenpx skills add machbuilds/atom --skill model-racegit clone --depth 1 https://github.com/machbuilds/atomWrote 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/machbuilds/atom/model-race)<a href="https://agentmods.dev/skills/machbuilds/atom/model-race"><img src="https://agentmods.dev/badge/skills/machbuilds/atom/model-race.svg" alt="Measured on agentmods" 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 | $0.00024 | $0.00483 |
| Opus 5 | $0.00012 | $0.00242 |
| Sonnet 5 | $0.00005 | $0.00097 |
| Haiku 4.5 | $0.00002 | $0.00048 |
Grade A, and why
model-race 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 3d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
model-race skill (Claude wrapper)
Source of truth: see the Tooling > model-race section of AGENTS.md
in this project. That file holds the canonical instructions every AI
tool reads.
This file is the Claude-specific wrapper. It exists so Claude Code's
Skill tool can register model-race as an invocable skill. Behavior
comes from AGENTS.md.
Quick reference
model-race start <feature> --spec <file> # create worktrees, write spec
model-race status # show race state
model-race launch <model> # open AI CLI in the model's worktree
model-race score # run automated scorecard
model-race judge # opt-in LLM evaluation
model-race merge <winner> # cherry-pick winner, clean losers
model-race abort # tear down (destructive)
model-race --help and model-race <command> --help show all flags.
When this skill activates (Claude-specific)
You should suggest model-race to the user only when:
- The user is about to make a non-obvious decision with multiple reasonable approaches (algorithm choice, API shape, refactor pattern).
- The work is high-stakes enough that the comparison cost is worth paying — typically anything that will be hard to change later.
- The user has not already chosen a path.
Do NOT suggest it for:
- CRUD endpoints, boilerplate, glue code.
- Anything where the answer is obvious.
- Bug fixes (these have one correct answer).
When the user races, the spec is critical
A race is only as good as its spec. Before model-race start, help the
user write a spec that:
- States the feature in one paragraph.
- Lists testable acceptance criteria (what makes it correct).
- Names constraints (perf budgets, API contracts, file boundaries).
- Excludes solution details (don't pre-decide the approach).
A weak spec produces three confused implementations and no clear winner. A strong spec produces three differentiated approaches you can actually compare.
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.
- 3d ago First seen · 59 lines · 24 tokens per session scan A e5fe4efebab4
model-race is a skill published in the GitHub repository machbuilds/atom (2 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 483 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
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