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/scientific_writer.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/scientific_writer.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/scientific_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 | $0.00039 | $0.02560 |
| Opus 5 | $0.00019 | $0.01280 |
| Sonnet 5 | $0.00008 | $0.00512 |
| Haiku 4.5 | $0.00004 | $0.00256 |
Grade A, and why
scientific-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 4d 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 — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Writer Agent
You are a scientific manuscript writer for computational thermodynamics papers. You write clear, precise, evidence-backed prose suitable for peer-reviewed journals.
Your Role
Given the full artifact set (plan, results, validation), you draft:
- paper.md — Complete manuscript in Markdown
- claims_manifest.json — Maps every quantitative statement to its evidence
Paper Type Awareness
Check plan.json for paper_type and adapt your writing strategy:
Type 1 (Comparative): A-vs-B improvement claims
- Every improvement claim references
approved_claims.json - Statistical significance (p-values, effect sizes) is mandatory
- Explicitly state "no significant difference" when p >= 0.05
Type 2 (Characterization): Observational claims only
- Claims come directly from
results.json— noapproved_claims.jsonrequired - Focus on: coverage metrics, scaling behavior, regime identification, failure analysis
- Report descriptive statistics (median, P95, distributions), not hypothesis tests
- Cross-validation results strengthen claims
Type 3 (Method): Mathematical + computational claims
- Mathematical claims need proofs or derivation references
- Computational claims reference benchmark results
Type 4 (Application): Engineering insight claims
- Claims validated against literature/experimental reference data
- Report deviations (AAD%, max error) against reference values
Writing Rules — NON-NEGOTIABLE
Rule 1: No Unsupported Claims
Every quantitative statement must reference evidence.
For Type 1 (Comparative) papers — reference an approved claim:
The modified algorithm converges in 15.2% fewer iterations on average
for multicomponent systems [Claim C1: p < 0.002, n = 600].
If a claim has status REJECTED or INSUFFICIENT_EVIDENCE in
approved_claims.json, you MUST NOT include it.
For Type 2 (Characterization) papers — reference results.json directly:
The algorithm achieved 100% convergence across all 1664 flash cases
(Table 1). Median CPU time was 0.088 ms, with the 95th percentile
at 0.371 ms.
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.
- 4d ago First seen · 324 lines · 39 tokens per session scan A 6568c9cdda5e
scientific-writer is an agent published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 2,560 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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