paperlab_notebook_regression_baselines

paperlab_notebook_regression_baselines is a skill for Claude Code, Codex from equinor/neqsim. It costs 52 tokens per session (490 once invoked), scanned A, original, Apache-2.0.

A workflow for checking whether notebook outputs remain reliable as NeqSim or other software changes. A baseline is a saved expected result used for comparison, with allowed tolerances for normal numerical drift.

In plain words
What is it for?
Use it to define tolerances and metadata, compare scalar and table results, check figure freshness, and classify notebook status.
Why use it?
It identifies changed, outdated, missing, or broken figures, tables, calculations, and claims before they undermine a book.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to define tolerances and metadata, compare scalar and table results…

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim/paperlab_notebook_regression_baselines
Install

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.

Any agent
npx skills add equinor/neqsim --skill paperlab_notebook_regression_baselines
Clone the repo
git clone --depth 1 https://github.com/equinor/neqsim

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for paperlab_notebook_regression_baselines

README.md
[![agentmods](https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_notebook_regression_baselines.svg)](https://agentmods.dev/skills/equinor/neqsim/paperlab_notebook_regression_baselines)
Your own site
<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>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 490 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash 8c73e4fd4142, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

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.

.github/skills/paperlab_notebook_regression_baselines/SKILL.md · 66 lines

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-patterns for notebook execution structure,
  • neqsim-regression-baselines for numerical drift principles,
  • paperlab_scientific_traceability_audit for 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.
Changes

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.

  1. 3d ago First seen · 66 lines · 52 tokens per session scan A 8c73e4fd4142

Subscribe to this mod's changes

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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