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_learning_objective_matrixgit 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_learning_objective_matrix)<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab_learning_objective_matrix"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_learning_objective_matrix/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_learning_objective_matrix"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_learning_objective_matrix.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 | $0.00045 | $0.00515 |
| Opus 5.5 | $0.00018 | $0.00206 |
| Sonnet 5.5 | $0.00009 | $0.00103 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
paperlab_learning_objective_matrix 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-learning-objective-matrix — 100% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PaperLab Learning Objective Matrix
When to Use
USE WHEN: a book or chapter has learning objectives and needs proof that each objective is supported by teaching and assessment assets.
Pair with:
paperlab_student_readabilityfor objective wording,paperlab_exam_alignmentfor assessment coverage,paperlab_notebook_regression_baselinesfor computational objectives.
Objective Asset Types
Map objectives to these assets:
| Asset | Required When |
|---|---|
| theory_section | every objective |
| worked_example | calculation, design, or diagnosis objectives |
| figure_or_table | visual or comparative objectives |
| notebook | computational or NeqSim-backed objectives |
| exercise | every objective |
| assessment | course-release objectives |
| summary_takeaway | every chapter objective |
Status Values
complete: objective has teaching and assessment evidence.partial: objective has teaching evidence but weak practice.missing-practice: objective is explained but not exercised.missing-assessment: objective has no assessment item.unverified: objective cannot be mapped from available files.
Output Schema
{
"chapter": "ch10_acid_gas_removal_dehydration",
"objectives": [
{
"id": "ch10-lo3",
"text": "Calculate TEG circulation sensitivity for dehydration duty.",
"verb": "Calculate",
"status": "partial",
"assets": {
"theory_section": ["10.2"],
"notebook": ["notebooks/ch10_s02_teg_contactor.ipynb"],
"exercise": []
},
"recommended_edit": "Add one exercise that varies TEG circulation and interprets water dew point."
}
]
}
Pass Criteria
- Every objective has a section and an exercise or assessment item.
- Computational objectives point to a notebook, script, or verified example.
- Objective verbs are observable and match chapter difficulty.
- Summary takeaways reflect the objectives rather than restating headings.
Safety Rules
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 First seen · 77 lines · 45 tokens per session scan A 48dd1c227f0e
paperlab_learning_objective_matrix is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 515 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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