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 equinor/neqsim --skill paperlab_notebook_regression_baselinesgit clone --depth 1 https://github.com/equinor/neqsimWrote 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/equinor/neqsim/paperlab_notebook_regression_baselines)<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab_notebook_regression_baselines"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_notebook_regression_baselines.svg" alt="Measured on agentmods" 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 | $0.00052 | $0.00490 |
| Opus 5 | $0.00026 | $0.00245 |
| Sonnet 5 | $0.00010 | $0.00098 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
paperlab_notebook_regression_baselines 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 3d 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
PaperLab Notebook Regression Baselines
When to Use
USE WHEN: a book has notebooks whose outputs support chapter figures, tables, worked examples, or quantitative claims.
Pair with:
neqsim-notebook-patternsfor notebook execution structure,neqsim-regression-baselinesfor numerical drift principles,paperlab_scientific_traceability_auditfor claim provenance.
Baseline Types
| Type | Example | Tolerance Pattern |
|---|---|---|
| scalar | pressure drop, NPV, water dew point | absolute + relative tolerance |
| table | stream table, sensitivity output | per-column tolerance |
| figure | generated PNG path | existence + freshness + optional hash |
| environment | NeqSim version, Python package list | recorded metadata |
| claim | chapter number tied to notebook output | text-value match tolerance |
Classification
pass: outputs are within tolerance and figures are fresh.stale: notebook likely runs, but figure or claim output is outdated.broken: execution or import fails.missing-baseline: notebook has important outputs but no baseline yet.expensive-skip: execution is known expensive and requires manual review.
Output Schema
{
"notebook": "chapters/ch10/notebooks/ch10_s02_teg_contactor.ipynb",
"status": "pass",
"engine": "neqsim_dev_setup",
"baselines": [
{
"name": "dry_gas_water_ppm",
"value": 32.4,
"unit": "ppm",
"abs_tol": 0.5,
"rel_tol": 0.02
}
],
"figures": [
{"path": "figures/ch10_teg_sensitivity.png", "status": "fresh"}
]
}
Safety Rules
- Do not overwrite accepted baselines without explicit approval.
- Record environment metadata whenever execution occurs.
- Prefer stable engineering outputs over fragile full-notebook diffs.
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.
- 3d ago First seen · 66 lines · 52 tokens per session scan A 8c73e4fd4142
paperlab_notebook_regression_baselines is a skill published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 490 once invoked, about $0.0003 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 2026-09-03.
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