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 neqsim_in_writinggit 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/neqsim_in_writing)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim_in_writing"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim_in_writing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim_in_writing"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim_in_writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 74 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00075 | $0.02523 |
| Opus 5.5 | $0.00030 | $0.01009 |
| Sonnet 5.5 | $0.00015 | $0.00505 |
| Haiku 4.5 | $0.00007 | $0.00252 |
Grade A, and why
neqsim_in_writing scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "neqsim"]) Copies of this mod
1 near-identical copy found in the catalogue:
- neqsim-in-writing — 98% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeqSim integration for scientific writing
This skill is loaded by book_author.agent and solve.task.agent whenever a
chapter or paper section must defend a numerical statement with a NeqSim
artifact. It complements book_creation (which handles structural authoring)
and journal_formatting (which handles paper layout).
The objective is traceability: a reader (or reviewer) following any number, table, or figure in the manuscript must be able to land on a runnable NeqSim artifact in the repo within one click.
1. Standard chapter setup cell (Jupyter)
Every chapter notebook must start with the dual-boot cell. It tries the
local devtools build first (so an editor sees fresh code) and falls back to
the published neqsim PyPI package on Colab / external readers.
import importlib, subprocess, sys
try:
from neqsim_dev_setup import neqsim_init, neqsim_classes
ns = neqsim_init(recompile=False)
ns = neqsim_classes(ns)
NEQSIM_MODE = "devtools"
except Exception:
try:
import neqsim
except ImportError:
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "neqsim"])
from neqsim import jneqsim
NEQSIM_MODE = "pip"
print(f"NeqSim loaded via {NEQSIM_MODE}")
Canonical imports inside the chapter body:
from neqsim import jneqsim
SystemSrkEos = jneqsim.thermo.system.SystemSrkEos
SystemSrkCPAstatoil = jneqsim.thermo.system.SystemSrkCPAstatoil
ThermodynamicOperations = jneqsim.thermodynamicoperations.ThermodynamicOperations
ProcessSystem = jneqsim.process.processmodel.ProcessSystem
Never instantiate JPype classes ad-hoc with
JClass("...")inside book chapters — readers cannot easily spot the class. Use the named bindings above.
2. Claim → test linkage
Every assertion in prose that contains a number, equation, or qualitative behavioral statement that depends on simulation MUST carry a comment marker linking it to a JUnit test or a regression baseline.
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.
- 1mo ago Changed · +1 lines 57b3e5d7f2bc
- 1mo ago First seen · 274 lines · 75 tokens per session scan A 40490666553a
neqsim_in_writing is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 75 tokens to every session and 2,523 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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