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 skills add majiang213/OpenClaw-MAS --skill cmd_evalgit clone --depth 1 https://github.com/majiang213/OpenClaw-MASWrote 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/majiang213/openclaw-mas/cmd_eval)<a href="https://agentmods.dev/skills/majiang213/openclaw-mas/cmd_eval"><img src="https://agentmods.dev/badge/skills/majiang213/openclaw-mas/cmd_eval/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/majiang213/openclaw-mas/cmd_eval"><img src="https://agentmods.dev/badge/skills/majiang213/openclaw-mas/cmd_eval.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 | $0.00019 | $0.00232 |
| Opus 5 | $0.00010 | $0.00116 |
| Sonnet 5 | $0.00004 | $0.00046 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
cmd_eval 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 9d 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.
What it actually says
Project Path
The first argument is the project path. Before doing anything else:
- Extract the project path from the first argument
- Verify the path exists
- Work within that directory for all file operations and shell commands
Eval Command (Legacy Shim)
Use this only if you still invoke /eval. The maintained workflow lives in skills/eval-harness/SKILL.md.
Canonical Surface
- Prefer the
eval-harnessskill directly. - Keep this file only as a compatibility entry point.
Arguments
$ARGUMENTS
Delegation
Apply the eval-harness skill.
- Support the same user intents as before: define, check, report, list, and cleanup.
- Keep evals capability-first, regression-backed, and evidence-based.
- Use the skill as the canonical evaluator instead of maintaining a separate command-specific playbook.
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.
- 9d ago First seen · 36 lines · 19 tokens per session scan A a05121e67475
cmd_eval is a skill published in the GitHub repository majiang213/OpenClaw-MAS (5 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 232 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 2026-09-03.
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