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 field-layout-importgit 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/field-layout-import)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/field-layout-import"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/field-layout-import/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/field-layout-import"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/field-layout-import.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 | $0.00070 | $0.01432 |
| Opus 5 | $0.00035 | $0.00716 |
| Sonnet 5 | $0.00014 | $0.00286 |
| Haiku 4.5 | $0.00007 | $0.00143 |
Grade A, and why
neqsim-field-layout-import 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 9d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Field Layout Import
Use this skill to normalize a supplied subsea map into a clean node list for downstream geometry and routing screening. It accepts an already-parsed GeoJSON FeatureCollection of point features or a list of CSV-like rows, and produces a validated list of nodes (well, manifold, host, riser base) with coordinates, water depth, and kind. It is intentionally simple, performs no file or network I/O, and should feed validated NeqSim routing and hydraulic workflows.
When to Use
- When a user supplies a parsed GeoJSON layout or a table of node coordinates and wants a clean, validated node list.
- When an agent needs a normalized layout to feed
subsea-layout-geometryorpipe-route-profile. - When data quality issues (missing depth, duplicate names, unknown kind) should be flagged before screening.
Inputs
from_geojson(obj): an already-parsed GeoJSONFeatureCollectiondict withPointfeatures. Each feature'sgeometry.coordinatesis[x, y](or[longitude, latitude]), andpropertiesmay includename,water_depth_m, andkind.from_rows(rows): a list of dicts withname,x/y(orlongitude/latitude), optionalwater_depth_m, and optionalkind.
Outputs
nodes: the normalized node list, each withname,x,y,water_depth_m, andkind.node_count: the number of normalized nodes.issues: data-quality findings (missing depth defaulted, unknown kind normalized, duplicate name, skipped feature).neqsim_available: whether the optional NeqSim package is importable.assumptions: public assumptions and required follow-up.
Engineering Method
Each feature or row is mapped to a node with a non-empty name, finite x and y coordinates, a non-negative water_depth_m (defaulted to 0 with an issue when missing), and a kind normalized to a known set (well, manifold, host, riser_base, template, tie_in); unknown kinds are coerced to well with an issue. Duplicate names and features that cannot be parsed are recorded as issues rather than raising, so a partial map still yields a usable node list. This is a data-shaping step only and performs no geometry, routing, or hydraulic calculation.
What ships with it
6 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.
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.
- 9d ago First seen · 113 lines · 70 tokens per session scan A 99dcb8d49196
neqsim-field-layout-import is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 70 tokens to every session and 1,432 once invoked, about $0.0003 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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