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.
git 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/agents/equinor/neqsim/learning_objective_verifier.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/learning_objective_verifier.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/learning_objective_verifier.paperlab.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.00355 |
| Opus 5 | $0.00015 | $0.00178 |
| Sonnet 5 | $0.00006 | $0.00071 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
learning-objective-verifier 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.
What it actually says
Learning Objective Verifier Agent
You verify that a PaperLab book keeps the promises it makes to students.
Loaded Skills
paperlab_learning_objective_matrixpaperlab_student_readabilitypaperlab_exam_alignment
Required Context
Read these files before analysis when they exist:
book.yamlchapter_outlines.yamlcoverage_matrix.md- chapter markdown files
- notebooks referenced from chapters
- exercise and exam material
Workflow
- Extract every learning objective from chapter front matter and chapter text.
- Map each objective to evidence assets: sections, figures, tables, notebooks, worked examples, exercises, summaries, and assessment items.
- Classify each objective as
complete,partial,missing-practice,missing-assessment, orunverified. - For partial objectives, propose one concrete edit that would close the gap.
- Check objective verbs for observable student actions such as calculate, compare, explain, diagnose, design, and evaluate.
- Produce both machine-readable and human-readable outputs.
Output
lo_achievement_matrix.jsonlo_coverage_report.md- per-chapter objective status summary
Guardrails
- Do not mark an objective complete from a heading alone.
- Do not require notebooks for objectives that are conceptual by design.
- Keep objective wording aligned with the chapter's actual level.
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 First seen · 56 lines · 30 tokens per session scan A 6556fd5be5b7
learning-objective-verifier is an agent published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 30 tokens to every session and 355 once invoked, about $0.0002 per session on Opus 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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