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-pdf-ocrgit 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-pdf-ocr)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-pdf-ocr"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-pdf-ocr/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-pdf-ocr"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-pdf-ocr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Prompt Injection · line 64 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00113 | $0.02546 |
| Opus 5.5 | $0.00045 | $0.01018 |
| Sonnet 5.5 | $0.00023 | $0.00509 |
| Haiku 4.5 | $0.00011 | $0.00255 |
Grade A, and why
neqsim-pdf-ocr 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 today.
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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeqSim PDF OCR Skill
Extract machine-readable text from PDFs that don't have an embedded text layer —
scanned documents, P&IDs, mechanical arrangement drawings, vendor datasheets
exported as raster, inspection reports, and old paper-original technical
requirements. Complements neqsim-technical-document-reading:
| Use case | Skill |
|---|---|
| Text-layer PDFs, Word, Excel | neqsim-technical-document-reading |
| Visual analysis of figures/drawings | neqsim-technical-document-reading (uses view_image) |
| No text layer / scanned / P&ID tags | neqsim-pdf-ocr (this skill) |
When to Use This Skill
Load this skill when any of these are true:
- The user mentions OCR, scanned PDFs, or P&ID text/tag extraction
- A PDF returns empty or near-empty text from
pymupdf/pdfplumber - A document is a P&ID and you need equipment/instrument tag numbers as strings
- A vendor datasheet is a scan of a paper original (common pre-2010)
- An engineering drawing has labels, line numbers, or revisions that must be searchable
Do not load this skill for born-digital PDFs that already have a text layer —
those are faster and more accurate via pdfplumber / pymupdf (covered in
neqsim-technical-document-reading).
Detection: Is OCR Actually Needed?
Always try fast text extraction first. Only fall back to OCR when the yield is too low. The utility implements this automatically; the rule of thumb is:
average_chars_per_page < 50 → treat as scanned, run OCR
import fitz
doc = fitz.open("document.pdf")
yield_chars = sum(len(page.get_text("text")) for page in doc) / len(doc)
if yield_chars < 50:
# scanned — needs OCR
...
Tool Selection Matrix
| Tool | Best for | Why |
|---|---|---|
| OCRmyPDF | Add a searchable text layer to a scanned PDF | Idempotent (--skip-text skips already-OCR'd pages), preserves original, deskew/rotate built-in, parallel by default |
| pytesseract + pdf2image | Per-page OCR with custom PSM, P&ID tag extraction, programmatic control | Lets you set page-segmentation mode 11 (sparse text) for tag-heavy drawings, returns text directly |
| pdfplumber / pymupdf | Text-layer extraction (run before OCR) | Fast, no external binaries — use whenever the text layer exists |
| opencv (preprocessing) | Deskew / denoise rotated P&ID scans before OCR | Improves Tesseract accuracy on poor scans |
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.
- today Changed 77562cbc3403
- 1mo ago First seen · 216 lines · 113 tokens per session scan A 23cd096b1006
neqsim-pdf-ocr is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 113 tokens to every session and 2,546 once invoked, about $0.0005 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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