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/aveproject/ave/grill-with-docsnpx skills add aveproject/ave --skill grill-with-docsgit clone --depth 1 https://github.com/aveproject/aveWhat 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 | $0.00389 |
| Opus 5 | $0.00000 | $0.00195 |
| Sonnet 5 | $0.00000 | $0.00078 |
| Haiku 4.5 | $0.00000 | $0.00039 |
Grade A, and why
grill-with-docs 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
grill-with-docs — ave
Grill before defining a vulnerability class. No record until complete.
Questions
Q1: In one sentence, what does a vulnerable component DO? (This becomes behavioral_fingerprint — must be behavioral, not a string.) Q2: Is this a new attack_class or a variant of an existing one? Don't check attack_class label similarity alone, that's not reliable, a genuinely distinct mechanism can have a similar-sounding name, and a genuine duplicate can have a completely different one. Pull any plausible match's real provenance_vector fields (entry_class, payload_surface, escalation) and the full description, compare directly against this candidate's actual mechanism. Only call it a variant if the entry_class and payload_surface genuinely match, not if the label or general topic sounds similar. Q3: What is the worst realistic impact? (drives cvss_base and severity) Q4: How much does agent autonomy amplify it? (drives aars) Q5: Which engines can detect it? pattern/yara/semgrep/llm/sandbox/magika Q6: Can a STATIC scan fully assess it, or does it need runtime observation? (detection_stage: static_detection vs runtime_observed) Q7: Where does it surface? content / server_card / registry / runtime (detection_layer) Q8: What is the confidence_baseline? High-signal or needs corroboration? Q9: Does it chain with other AVEs into a toxic flow? (derivable_into) Q10: What does the negative fixture look like — a benign file that looks similar but must NOT trigger?
End
Summary, the record JSON skeleton, the rule approach, the two fixture descriptions. Next: write fixtures first (TDD), then the rule, then validate.
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 · 34 lines · 0 tokens per session scan A 2ff41259b059
grill-with-docs is a skill published in the GitHub repository aveproject/ave (17 stars, last pushed 3d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 389 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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