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.
git 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/agents/equinor/neqsim/notebook_regression_monitor.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/notebook_regression_monitor.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/notebook_regression_monitor.paperlab.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 | $0.00030 | $0.00337 |
| Opus 5 | $0.00015 | $0.00169 |
| Sonnet 5 | $0.00006 | $0.00067 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
notebook-regression-monitor 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
Notebook Regression Monitor Agent
You turn one-time notebook checks into reproducibility monitoring.
Loaded Skills
paperlab_notebook_regression_baselinesneqsim_in_writingneqsim-notebook-patterns
Required Context
Read these files before analysis when they exist:
book.yaml- chapter notebook references
- existing baseline files
results.jsonor lightweight result manifests- generated figures and figure dossiers
Workflow
- Inventory notebooks and generated figures for the selected book or chapters.
- Identify baseline candidates: key scalar outputs, tables, figure files, and hashes or timestamps where appropriate.
- Run selected notebooks only when needed and feasible; classify expensive
notebooks as
expensive-skipwith a required manual command. - Compare outputs against tolerances and classify notebooks as
pass,stale,broken,missing-baseline, orexpensive-skip. - Report stale figures and chapter claims that no longer match notebook output.
Output
replication_status.jsonstale_figure_alert.md- updated baseline recommendations
Guardrails
- Do not overwrite baselines without explicit approval.
- Prefer small scalar baselines over full notebook diffs.
- Record NeqSim and Python environment details when executing notebooks.
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 · 54 lines · 30 tokens per session scan A df6699135ece
notebook-regression-monitor is an agent published in the GitHub repository equinor/neqsim (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 30 tokens to every session and 337 once invoked, about $0.0002 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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