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 skills add probabl-ai/skills --skill audit-ml-pipelinegit clone --depth 1 https://github.com/probabl-ai/skillsWrote 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/probabl-ai/skills/audit-ml-pipeline)<a href="https://agentmods.dev/skills/probabl-ai/skills/audit-ml-pipeline"><img src="https://agentmods.dev/badge/skills/probabl-ai/skills/audit-ml-pipeline/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/probabl-ai/skills/audit-ml-pipeline"><img src="https://agentmods.dev/badge/skills/probabl-ai/skills/audit-ml-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00594 | $0.05738 |
| Opus 5 | $0.00297 | $0.02869 |
| Sonnet 5 | $0.00119 | $0.01148 |
| Haiku 4.5 | $0.00059 | $0.00574 |
Grade A, and why
audit-ml-pipeline 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 13d 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 — 413 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit ML Pipeline
Per-experiment, human-readable, agent-executable narrative of a skore
report — produced by executing a bare-expression # %% file and
reading the digest. Read-only against the skore Project.
Next-step pointers
| Came here from… | After audit, next is… |
|---|---|
iterate-ml-experiment § 4 record-outcome |
→ Read audit digest, fill Status block + JOURNAL row |
| User free-text ("audit 02", "re-audit 04") | → Surface metrics to the user; no further dispatch |
| Re-run of an existing experiment | → Re-execute the existing audit file; surface diff if metrics changed |
The audit is dispatched FIRST in § 4, before any scratch probes.
The digest carries the checks summary and the metrics summary — it
replaces ad-hoc scratch/<ts>_inspect_*.py files for the metric
extraction step.
Where things live — visual map
| Path | Durability | Who writes it | What it holds |
|---|---|---|---|
audit/<NN>_<short_name>.py |
Durable (in git) | This skill, once per experiment | The bare-expression cells. Source of truth. Can be opened as a notebook in JupyterLab / VS Code for the rich HTML view |
scratch/audit/<stem>/audit.md |
Ephemeral (gitignored), optional | run_cells.py when given a 2nd arg |
Per-cell markdown digest: source + stdout + last-expression repr. Same content as stdout |
Stdout from run_cells.py |
Captured by the bash tool | run_cells.py (always) |
Streamed digest — the agent reads this directly from the tool output |
Mnemonic: audit/ is source (in git); scratch/audit/ and
stdout are output. Never put the source .py under
scratch/audit/. Never commit anything under scratch/audit/.
Read-only contract
The central rule. Surfaced as the first Stop condition below.
Allowed in audit/<stem>.py:
skore.Project(...)— open the project this experiment wrote to.project.summarize()— list(key, id)pairs.project.get(id)— load a specific report by id.- Every
report.*accessor. - Imports from
<pkg>(read-only inspection).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 413 lines · 594 tokens per session scan A 851ec25dfeb3
audit-ml-pipeline is a skill published in the GitHub repository probabl-ai/skills (122 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 594 tokens to every session and 5,738 once invoked, about $0.0030 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 2026-08-30.
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