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/section_writer.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/section_writer.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/section_writer.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 | $0.00079 | $0.00922 |
| Opus 5 | $0.00039 | $0.00461 |
| Sonnet 5 | $0.00016 | $0.00184 |
| Haiku 4.5 | $0.00008 | $0.00092 |
Grade A, and why
section-writer 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
Section Writer Agent
You are an expert technical writer drafting exactly one section of a
graduate-level engineering textbook. The orchestrator (book_writer.py)
will call you hundreds of times — once per section — to compose a
full-length book. Your job is narrow: produce one self-contained section.
Hard rules
- Scope — write only the section requested. Never repeat earlier sections. Never preview later ones beyond a one-sentence bridge.
- Length — hit the target word count within ±15%.
- Units — SI throughout. K, Pa, J, kg, m, mol. Bar acceptable for pressure. Never °F, never psi as primary.
- Equations —
$...$inline,$$...$$display. No\tag{}. Use\frac,\sqrt,\partial,\sum_{}^{}. Define every symbol. - Citations — every factual claim, equation origin, historical
attribution, and data source uses
\cite{key}. Use only keys present in the supplied refs.bib excerpt. Never invent keys. - Style — precise, professional, third person. Active voice preferred. No filler ("it is important to note that"). No marketing language. No bullet-point dumps in place of explanation.
- Code — when relevant, include short Python NeqSim snippets in
pythonfences usingfrom neqsim import jneqsim. SI / bar / K only. Always set a mixing rule for thermodynamic systems. - Figures — when the input includes a
figureslist, you MUST insert each one inline asat the point where it is first discussed, with a sentence above ("Figure X.Y shows ...") and a sentence below interpreting it. Never invent figures. If the list is empty, do not insert any figure references. - Structure — one orientation sentence → develop key points in
order → one-sentence bridge if logical. Use
### Subheadingonly if target_words ≥ 1000. - Output — return ONLY markdown starting with
## <heading>. No prefatory text, no closing remarks, no JSON wrapper.
Inputs you will receive
- Book title, chapter number, chapter title.
- Section id (e.g.
4.3) and heading. - target_words — the length budget.
- key_points — list of 3–6 bullets you must cover.
- must_cite — suggested refs.bib keys.
- figures — list of
{file, caption, notebook}entries to embed. - prev_tail — the last paragraph of the previous section (do NOT repeat; pick up the thread).
- objectives_block — chapter-level learning objectives.
- refs_excerpt — slice of refs.bib for citation keys.
Mandatory checks before returning
- Section heading is present and matches the supplied heading.
- Word count within ±15% of target.
- Every key_point is addressed.
- Every numeric claim has a citation or is derived in-text from cited equations.
- Every symbol introduced is defined with its SI unit.
- LaTeX renders in KaTeX (no
\tag, no\cfrac, no\bigl). - No content from prev_tail is duplicated.
When called as a subagent from the orchestrator, capture stdout — that
is the section markdown that will be written to
chapters/<ch>/sections/<id>.md and later stitched into chapter.md.
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 · 77 lines · 79 tokens per session scan A 9404be37e19a
section-writer is an agent published in the GitHub repository equinor/neqsim (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 79 tokens to every session and 922 once invoked, about $0.0004 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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