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_student_readabilitygit 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_student_readability)<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab_student_readability"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_student_readability/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_student_readability"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_student_readability.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 | $0.00046 | $0.00795 |
| Opus 5.5 | $0.00018 | $0.00318 |
| Sonnet 5.5 | $0.00009 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
paperlab_student_readability 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- paperlab-student-readability — 95% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PaperLab Student Readability
When to Use
USE WHEN: improving a PaperLab textbook or course book for student learning, especially after the scientific content is broadly complete. This skill checks whether a chapter is easy to enter, easy to follow, and useful for exam and exercise preparation.
DO NOT USE for: journal-paper conciseness, renderer debugging, or technical
validation of NeqSim calculations. Pair with paperlab_scientific_traceability_audit
for scientific claims and paperlab_book_typesetting_release for final formats.
Reader Model
Assume the primary reader is a student who:
- knows basic petroleum engineering terminology but not every industrial acronym,
- learns best from worked examples and recurring case threads,
- needs to connect equations, figures, and decisions,
- will use the book for exercises, exams, and project work,
- may be reading in English as a second language.
Chapter Checklist
For each chapter, check:
- Learning objectives: 3 to 7 objectives, written as observable actions. Prefer verbs such as calculate, compare, explain, diagnose, design, and evaluate. Avoid vague objectives such as "understand processing".
- Prerequisites: identify any concepts that must already be known. If a prerequisite is not covered earlier, add a short bridge paragraph or a cross-reference.
- Concept sequence: introduce physical intuition before equations, and equations before computational examples.
- Worked examples: include at least one compact worked example in core engineering chapters. The example should state inputs, method, result, interpretation, and common mistakes.
- Figures as teaching objects: each non-cover figure should be followed by
discussion using
figure-discussion. - Exercises: include a mix of conceptual questions, calculation questions, and short design-decision questions.
- Summary: close with decision-useful takeaways, not a table of contents in prose.
- Glossary and acronyms: define important first-use terms and keep names consistent with the book glossary and acronym list.
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.
- 1mo ago Changed · +1 lines 0352809266a6
- 1mo ago First seen · 90 lines · 46 tokens per session scan A 1da8eff2a750
paperlab_student_readability is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 46 tokens to every session and 795 once invoked, about $0.0002 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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