Borrowing it
Nothing to install: this file belongs to aveproject/ave. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aveproject/ave/main/.claude/skills/diagnose/SKILL.mdgit clone --depth 1 https://github.com/aveproject/aveWrote 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/aveproject/ave/diagnose)<a href="https://agentmods.dev/skills/aveproject/ave/diagnose"><img src="https://agentmods.dev/badge/skills/aveproject/ave/diagnose/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/aveproject/ave/diagnose"><img src="https://agentmods.dev/badge/skills/aveproject/ave/diagnose.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 | $0.00000 | $0.00160 |
| Opus 5.5 | $0.00000 | $0.00064 |
| Sonnet 5.5 | $0.00000 | $0.00032 |
| Haiku 4.5 | $0.00000 | $0.00016 |
Grade A, and why
diagnose 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 1mo 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
diagnose — ave
Rule misfires (false positive on negative fixture)
Repro: run the rule against the negative fixture in isolation. Minimize: trim the negative fixture to the smallest triggering line. Hypothesize: which pattern/condition is too broad? Fix: tighten the rule. Re-run both fixtures.
Record fails validation
Repro: python scripts/validate_records.py Read the jsonschema error — it names the failing field. Common: severity/aivss_score mismatch, missing required field, ave_id format wrong.
Scanner does not pick up a new record
Check: is the record in records/ with valid JSON? Check: does evidence_basis_engines list an engine that has a rule? Check: does the rule reference the correct ave_id?
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.
- 1mo ago First seen · 19 lines · 0 tokens per session scan A 4785026d5e21
diagnose is a skill published in the GitHub repository aveproject/ave (24 stars, last pushed 2d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 160 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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