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/emaballarin/ccplugins/auditnpx skills add emaballarin/ccplugins --skill auditgit clone --depth 1 https://github.com/emaballarin/ccpluginsWhat 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 | $0.00183 | $0.01403 |
| Opus 5 | $0.00092 | $0.00701 |
| Sonnet 5 | $0.00037 | $0.00281 |
| Haiku 4.5 | $0.00018 | $0.00140 |
Grade A, and why
audit 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 yesterday.
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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/tml:audit — what is this pipeline leaving on the table
Produce a short, ranked, graded list of what could be improved, what each item
would cost to take, and what it would silently break. Auditing changes nothing and
decides nothing — /tml:plan decides.
First action, always
Establish what already exists before reading a line of model code:
ls -la ./.tml/ 2>/dev/null; git -C . log --oneline -3 2>/dev/null
If findings.md is already there, read it, say when it was written, and audit as
a delta — re-confirming what changed, not re-deriving what did not. An audit
that silently re-proposes what was already rejected is noise.
Hard rules
- Read-first. Never edit project code. The only path written is
./.tml/findings.md, and only after the findings have been shown. No global or shared state is touched. - Every finding carries a grade. From the ladder in
references/evidence-grades.md§1. An ungraded claim is not emitted;folkloreis a respectable answer. - Every finding is priced in time-to-target, decomposed into
steps-to-target × time-per-step, and states whether it moves the other factor adversely. Throughput is a diagnostic, never a result. - Every tier-A/B/D item declares a quality exposure, separately from its radius. Radius says how to verify; exposure says whether you are allowed to.
- Recommending nothing is a valid outcome. A pipeline with no worthwhile changes should be told so, in one paragraph.
Procedure
1. Scope, and the two questions that change the answer
Find the training entry point, the data path, the eval path, and the harness. Then establish, by asking rather than inferring:
- The target. What quality, on what metric, measured how? Without it nothing can be priced, because "time-to-target" has no target.
- The constraint. Wall-clock, device-hours, memory, or deadline?
If the pipeline is already instrumented, read references/regime.md §1 and note
the parallelism regime — it changes what /tml:plan can propose next.
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.
- yesterday First seen · 116 lines · 183 tokens per session scan A 6098fd84259d
audit is a skill published in the GitHub repository emaballarin/ccplugins (3 stars, last pushed 26d ago), licensed MIT. It adds 183 tokens to every session and 1,403 once invoked, about $0.0009 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 2026-08-31.
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