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-community-skills --skill neqsim-reference-fluid-synthetic-generationgit clone --depth 1 https://github.com/equinor/neqsim-community-skillsWrote 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-community-skills/neqsim-reference-fluid-synthetic-generation)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-reference-fluid-synthetic-generation"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-reference-fluid-synthetic-generation/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-community-skills/neqsim-reference-fluid-synthetic-generation"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-reference-fluid-synthetic-generation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00109 | $0.02810 |
| Opus 5.5 | $0.00044 | $0.01124 |
| Sonnet 5.5 | $0.00022 | $0.00562 |
| Haiku 4.5 | $0.00011 | $0.00281 |
Grade A, and why
neqsim-reference-fluid-synthetic-generation 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 11d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reference-Fluid Synthetic Generation
Use this skill for the "common reference fluid → adjust split factor → match measured data → generate representative fluids" workflow. It provides three plant-agnostic, dependency-free helpers:
match_split_factor— a robust golden-section 1-D search that finds the split / characterization factor best reproducing a measured target.generate_fluid_cases— build representative or synthetic fluid cases by applying a range of factors to a common reference fluid.blend_compositions— combine several well or fluid compositions into a single field composition by molar-rate allocation.
The forward model / fluid builder is injected by the caller, so this skill
has no dependency on a particular EOS. In practice it wraps the community
pseudocomponent-split-characterization gamma split or a NeqSim
characterization call, so the same factor drives both the split and the match.
When to Use
- When there is no PVT study at all — a discovery, a prospect, an early concept — and a reservoir model still needs a fluid. See No PVT data below.
- When you have a common reference EOS/fluid and want field-specific or per-case fluids by adjusting one heavy-end split factor (the "common factor" idea: reuse one characterization method with field-specific calibration).
- When a split factor must be calibrated so the model reproduces a measured saturation pressure, GOR, or stock-tank-oil density.
- When production is allocated across several wells or fluids and you need one representative field composition.
- When a complete PVT study is unavailable and you must generate a usable fluid from a reference plus available measurements.
No PVT data: build a best guess and say so
A reservoir model needs a fluid long before a laboratory PVT study exists, and often before a sample has been taken. The alternative to guessing silently is to guess explicitly.
from reference_fluid import build_analogue_fluid_basis
basis = build_analogue_fluid_basis(
depth_tvdss_m=3590.0,
province="northern_north_sea",
formation="Brent",
water_depth_m=381.0,
measured_temperature_C=129.0, # anything measured is used and labelled
measured_pressure_bara=542.0, # ... and anything absent is derived
)
basis.targets["gor_sm3_sm3"] # value + provenance + GOR DEFINITION
basis.seed["c7_plus_cuts"] # ready for addTBPfraction / addPlusFraction
basis.assumptions # each with the measurement that retires it
basis.acquisition_plan # ranked by how much it reduces the answer
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/reference_fluid_example.py 1.3 KB runs code
- pyproject.toml 671 B
- README.md 893 B
- src/reference_fluid/__init__.py 1.7 KB runs code
- src/reference_fluid/analogue_fluid.py 25 KB runs code
- src/reference_fluid/generate.py 6.4 KB runs code
- tests/test_analogue_fluid.py 11 KB runs code
- tests/test_generate.py 2.1 KB runs code
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.
- 11d ago Changed · +6 lines ba920224fcd9
- 19d ago Changed 28b32147a9ba
- 1mo ago Changed · -97 tokens per session becddb1a837b
- 1mo ago First seen · 245 lines · 206 tokens per session scan A a922191237e2
neqsim-reference-fluid-synthetic-generation is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed today), licensed Apache-2.0. It adds 109 tokens to every session and 2,810 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-19.
neqsim-reference-fluid-synthetic-generation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| neqsim-reference-fluid-synthetic-generationequinor/neqsim-community-skills | Skill | 4 | today | Apache-2.0 | A | 109 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 3d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |