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 test-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/test-ml-pipeline)<a href="https://agentmods.dev/skills/probabl-ai/skills/test-ml-pipeline"><img src="https://agentmods.dev/badge/skills/probabl-ai/skills/test-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/test-ml-pipeline"><img src="https://agentmods.dev/badge/skills/probabl-ai/skills/test-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 | $0.00401 | $0.02576 |
| Opus 5 | $0.00200 | $0.01288 |
| Sonnet 5 | $0.00080 | $0.00515 |
| Haiku 4.5 | $0.00040 | $0.00258 |
Grade A, and why
test-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 10d 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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test ML Pipeline (router)
Where tests for an ML workspace live, what gets paired with what, and which subskill owns the body of each test category.
First action (every turn)
Before answering anything else:
- Confirm an approved design note exists for the test
the user is asking about. The pairing rule is hard:
tests/<category>/test_NN_<short_name>.pyonly exists ifjournal/NN_<short_name>.mdis at leastapprovedandexperiments/NN_<short_name>.pyis the matching script. If the design note doesn't exist, hand back toiterate-ml-experiment. - Emit the Pre-flight checklist (below) as visible text in your response, with each box marked.
- Use the Dispatch table to pick the subskill that owns the test category, then hand off.
Pre-flight — emit this checklist as visible text before any test work
Pre-flight (test-ml-pipeline):
- [ ] `journal/NN_<short_name>.md` exists and is at least `approved`
(or confirmed n/a — about to hand off to `iterate-ml-experiment`)
- [ ] `experiments/NN_<short_name>.py` exists with the matching stem
(or confirmed n/a — about to hand off to `organize-ml-workspace`)
- [ ] Test category picked: smoke | regression | distribution | …
- [ ] Subskill dispatched: `smoke-test-ml-pipeline` | …
- [ ] Test file stem decided: `tests/<category>/test_NN_<short_name>.py`
- [ ] pytest is on the project's dependency manifest (per
`data-science-python-stack` § Tier 1)
Stop conditions — read before anything else
- No test without an approved design note. Never create
tests/<category>/test_NN_*.pyif the matchingjournal/NN_*.mdisn't on disk and at leastapproved. The design note is the contract; the test asserts the contract holds. Reverse order is incoherent. - The stem rule is hard. Test file basename is
test_NN_<short_name>.py(with thetest_prefix that pytest expects); theNN_<short_name>portion matches the experiment exactly. One experiment → one test file per category. Notest_<NN>_v2.py, notest_NN_<short_name>_2.py. If a test needs to evolve, edit it in place; the pairing must stay 1:1. - One subskill per category. This skill only places the empty
test file and hands off. Don't write assertion bodies, fixture
construction, or test-specific logic in this skill — that belongs
to the matching subskill (
smoke-test-ml-pipeline, etc.). - pytest is the runner. Tests are pytest tests, not
jupytext-style
# %%scripts. The experiment scripts live inexperiments/and stay# %%-style for interactive iteration; the tests are binary pass/fail and benefit from pytest's reporting. Don't mix the two conventions.
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.
- 10d ago First seen · 218 lines · 401 tokens per session scan A 8b18dd19268e
test-ml-pipeline is a skill published in the GitHub repository probabl-ai/skills (122 stars, last pushed 23d ago), licensed BSD-3-Clause. It adds 401 tokens to every session and 2,576 once invoked, about $0.0020 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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