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_instructor_resource_packgit 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_instructor_resource_pack)<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab_instructor_resource_pack"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_instructor_resource_pack.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 | $0.00036 | $0.00277 |
| Opus 5 | $0.00018 | $0.00138 |
| Sonnet 5 | $0.00007 | $0.00055 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
paperlab_instructor_resource_pack 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
PaperLab Instructor Resource Pack
When to Use
USE WHEN: preparing a PaperLab textbook for teaching, course adoption, workshops, or training delivery.
Resource Types
- lecture outlines,
- slide plans,
- question banks,
- lab assignments,
- solution manual sections,
- grading rubrics,
- exam variants,
- project briefs.
Question Bank Schema
{
"questions": [
{
"id": "ch04_q03",
"chapter": "ch04",
"learning_objective": "LO-4.2",
"difficulty": "calculation",
"prompt": "...",
"solution_outline": "instructor-only",
"rubric": ["units", "method", "interpretation"]
}
]
}
Pass Criteria
- Resources map to learning objectives.
- Labs have runnable notebooks or clear manual alternatives.
- Rubrics evaluate interpretation as well as calculation.
Safety Rules
- Keep instructor-only answers out of public student exports.
- Do not include confidential industrial case details.
- Do not create exam questions that depend on unavailable software or data.
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 · 54 lines · 36 tokens per session scan A f1540b82e267
paperlab_instructor_resource_pack is a skill published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 36 tokens to every session and 277 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.
Other skills, from other repositories
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
habit-formation
Atomic habits, cue-routine-reward loops, habit stacking, accountability systems, and behavior change.
hiring-manager-deep-dive
Prepares for hiring manager rounds at Staff+ (L6+) level — scope of impact, influence without authority, ambiguity navigation, mentorship, strategic thinking. Use when practicing HM rounds or calibrating story depth for target level. Activate on "hiring manager round", "HM screen", "staff level", "scope of impact".…
trading-manual-writer
A writing skill for creating sections of a trading manual for beginners and individual investors. It explains financial instruments or trading topics in Markdown and can include SVG illustrations.
aiwg-help
Display all available AIWG CLI commands, their arguments, and usage examples.