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 paperlab-exam-alignmentgit 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/paperlab-exam-alignment)<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab-exam-alignment"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab-exam-alignment/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/paperlab-exam-alignment"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab-exam-alignment.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 | $0.00000 | $0.00365 |
| Opus 5.5 | $0.00000 | $0.00146 |
| Sonnet 5.5 | $0.00000 | $0.00073 |
| Haiku 4.5 | $0.00000 | $0.00036 |
Grade A, and why
paperlab-exam-alignment 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 27d 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.
This is a copy
100% identical to paperlab_exam_alignment — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Skill: PaperLab Exam Alignment
Purpose
Check whether a PaperLab course book prepares students for the associated exercises and exams. Use this when a book has source folders with exams, exercise PDFs, review chapters, learning objectives, or end-of-chapter questions.
Inputs
source_manifest.jsonor a source root containingexams/andexercises/.- Chapter markdown files with learning objectives and exercise sections.
- Review/exam-preparation chapters.
- Optional extracted exam text from
paperlab-source-pdf-to-htmlor OCR.
Workflow
- Run
book-source-inventoryfor source traceability. - Run
python paperflow.py book-exam-alignment <book_dir>. - Review
exam_alignment.mdfor topics markedneeds-review. - For each weak topic, add a worked example, a self-test prompt, or a Chapter 26 review item.
- If exam PDF text is available, rerun with extracted text and update the topic matrix with evidence from actual problem statements.
Output
exam_alignment.jsonexam_alignment.md
Topic Matrix
At minimum, map:
- field-development framing,
- PVT and flow performance,
- processing and separation,
- subsea, wells, and SURF,
- economics and scheduling,
- optimization and uncertainty,
- regulation and standards,
- CCS and gas quality.
Safety Rules
- Do not reproduce confidential or copyrighted exam text unless allowed.
- Use topic frequencies and paraphrased problem types for public reports.
- Mark results as
needs-reviewwhen source PDFs have not been OCR/extracted. - Keep Chapter 26 concise; use cross-references to detailed worked examples in earlier chapters.
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.
- 27d ago First seen · 52 lines · 0 tokens per session scan A 078c8614cb0e
paperlab-exam-alignment is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 365 tokens. A static security scan graded it A with 0 findings. It is 100% identical to paperlab_exam_alignment, differing in 0 lines, and is treated as a copy.
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