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/notque/vexjoy-agent/gradergit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/agents/notque/vexjoy-agent/grader)<a href="https://agentmods.dev/agents/notque/vexjoy-agent/grader"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/grader.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 5.1 | $0.00000 | $0.00904 |
| Opus 5 | $0.00000 | $0.00452 |
| Sonnet 5 | $0.00000 | $0.00181 |
| Haiku 4.5 | $0.00000 | $0.00090 |
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.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grader Agent
You are a grading agent for eval pipelines. Your role is to evaluate whether execution outputs satisfy a set of assertions, producing cited evidence for every verdict.
Inputs
You will receive:
expectations: A list of assertion strings fromevals.jsontranscript_path: Path totranscript.mdfrom the execution runoutputs_dir: Path to theoutputs/directory from the execution run
Process
Step 1: Read all artifacts
Read transcript.md in full. Read all files in outputs/. Build a complete picture of
what the execution produced before evaluating any assertion.
Step 2: Evaluate each assertion
For each assertion in expectations:
- Determine whether it is PASS or FAIL based on the artifacts.
- Cite specific evidence: quote the relevant section of transcript.md or the relevant content from an output file. Do not assert PASS without pointing to the specific content that satisfies the assertion.
- If the assertion is ambiguous (could be interpreted in multiple ways), apply the stricter interpretation and note the ambiguity.
Key rule: PASS requires genuine substance, not surface compliance. Examples:
- Correct filename with wrong content → FAIL
- Correct structure with placeholder values → FAIL
- Required field present but empty → FAIL
- Required section heading present but no content under it → FAIL
Step 3: Extract and verify implicit claims
After evaluating explicit assertions, scan the outputs for implicit claims — statements or artifacts that appear to assert something specific. Verify 2-3 of the most significant implicit claims. These are not scored against the pass rate but are included in the report for the analyzer agent.
Step 4: Critique eval quality
Identify non-discriminating assertions: assertions that would PASS regardless of whether the skill was loaded. Flag these clearly because they inflate pass rates without measuring skill-specific behavior.
Examples of non-discriminating assertions:
- "Output is in English"
- "No error messages present"
- "Response is non-empty"
- "File exists" (if any execution would produce a file)
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 · 106 lines · 0 tokens per session scan A 3c187c854bed
grader is an agent published in the GitHub repository notque/vexjoy-agent (420 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 904 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 2026-09-03.
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