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-technical-document-readinggit 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-technical-document-reading)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-technical-document-reading"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-technical-document-reading/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-technical-document-reading"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-technical-document-reading.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00100 | $0.15474 |
| Opus 5.5 | $0.00040 | $0.06190 |
| Sonnet 5.5 | $0.00020 | $0.03095 |
| Haiku 4.5 | $0.00010 | $0.01547 |
Grade A, and why
neqsim-technical-document-reading 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.
How it starts
The opening of the file, as written. The whole thing — 1,453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Document Reading Skill
Extract structured engineering data from technical documents and convert it into formats usable by process simulation, mechanical design, and engineering analysis.
Use the public community skill neqsim-document-intelligence-extraction before this skill when
the task receives files. It owns source inventory, native/OCR/vision routing, provenance,
confidence, review gates, and cross-document conflicts. This skill consumes that evidence package
and adds document-type schemas, engineering normalization, physical validation, and NeqSim
handoffs. Keeping this boundary explicit prevents OCR or multimodal output from being mistaken for
verified engineering data.
Core Principle
Classify → Extract → Normalize → Validate → Output
- Classify the document type to select the right extraction strategy
- Extract raw data using format-specific tools (PDF, Word, Excel)
- Normalize units, component names, and field names to standard conventions
- Validate extracted data against physical bounds and completeness checks
- Output structured JSON/dict for downstream consumption
For P&ID-driven operational studies, also load neqsim-pid-process-operations.
That skill converts symbols into a process graph, classifies valve functions,
maps instrument bubbles to historian tags, and defines steady-state or dynamic
NeqSim scenario actions.
For water-hammer or liquid-hammer screening, also load neqsim-water-hammer.
Extract route geometry, wall thickness, roughness/piping class, fittings, valve
closure timing, design pressure, and tagreader event references so the output can
feed WaterHammerStudy or MCP runWaterHammer.
For trapped-liquid fire rupture studies, also load
neqsim-trapped-liquid-fire-rupture. Extract segment boundaries, line numbers,
pipe geometry, material grade/certificate data, flange/gasket/bolt ratings,
fire/PFP basis, relief availability, acceptance criteria, and explicit evidence
gaps before handing data to the solver.
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 Changed 3b781b15d412
- 23d ago Changed · -46 tokens per session e4d6e8d41e0a
- 1mo ago First seen · 1,453 lines · 146 tokens per session scan A 618b1e52f5e6
neqsim-technical-document-reading is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 100 tokens to every session and 15,474 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 2026-09-03.
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