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-book-to-paper-extractiongit 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-book-to-paper-extraction)<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab-book-to-paper-extraction"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab-book-to-paper-extraction/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-book-to-paper-extraction"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab-book-to-paper-extraction.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 | $0.00037 | $0.00295 |
| Opus 5.5 | $0.00015 | $0.00118 |
| Sonnet 5.5 | $0.00007 | $0.00059 |
| Haiku 4.5 | $0.00004 | $0.00030 |
Grade A, and why
paperlab-book-to-paper-extraction 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
89% identical to paperlab_book_to_paper_extraction — 2 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
PaperLab Book to Paper Extraction
When to Use
USE WHEN: a mature book contains chapters, examples, or notebooks that could be turned into standalone scientific papers.
Candidate Types
- benchmark characterization paper,
- engineering application paper,
- methods tutorial with validation,
- review or perspective article,
- dataset or software note,
- reproducible teaching-lab paper.
Readiness Checks
For each candidate, check:
- original contribution,
- evidence and benchmark depth,
- literature gap,
- target journal fit,
- missing validation,
- required new experiments,
- overlap risk with the source book.
Output Schema
{
"candidates": [
{
"chapter": "ch08",
"paper_type": "application",
"novelty_angle": "validated workflow for compressor map screening",
"missing_evidence": ["independent reference case"],
"recommended_next_agent": "paper-planner"
}
]
}
Safety Rules
- Avoid duplicate publication unless the paper adds new synthesis or validation.
- Do not extract confidential case material into public manuscripts.
- Keep pedagogical conclusions separate from scientific novelty claims.
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 · 57 lines · 37 tokens per session scan A 878a5408b5fe
paperlab-book-to-paper-extraction is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 37 tokens to every session and 295 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to paperlab_book_to_paper_extraction, differing in 2 lines, and is treated as a copy.
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