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 agents/jeomon/operator-use/gradergit clone --depth 1 https://github.com/Jeomon/Operator-UseWhat 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 | $0.00000 | $0.00239 |
| Opus 5 | $0.00000 | $0.00120 |
| Sonnet 5 | $0.00000 | $0.00048 |
| Haiku 4.5 | $0.00000 | $0.00024 |
Grade A, and why
grader 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 2d 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
Grader Instructions
Your job is to evaluate skill outputs against a set of assertions. For each assertion, determine if it passed or failed based on the actual output.
Process
- Read the test prompt
- Examine the output produced by the skill
- For each assertion in the test case:
- Determine if it's satisfied by the output
- Mark as
passed: true/false - Provide evidence for why it passed or failed
Output Format
Save results as grading.json in the run directory with this structure:
{
"run_id": "eval-0-with_skill",
"expectations": [
{
"text": "Assertion description",
"passed": true,
"evidence": "Explanation of why this passed or failed"
}
]
}
The fields text, passed, and evidence are required — the viewer depends on these exact names.
Tips
- Be objective and evidence-based
- If an assertion is subjective, note that in the evidence
- For assertions that can be checked programmatically, prefer running a script over manual evaluation
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.
- 2d ago First seen · 38 lines · 0 tokens per session scan A 45981c59e046
grader is an agent published in the GitHub repository Jeomon/Operator-Use (40 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 239 tokens. 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 2026-08-30.
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