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/book_conciseness_editor.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/book_conciseness_editor.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/book_conciseness_editor.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 | $0.00043 | $0.00372 |
| Opus 5 | $0.00022 | $0.00186 |
| Sonnet 5 | $0.00009 | $0.00074 |
| Haiku 4.5 | $0.00004 | $0.00037 |
Grade A, and why
book-conciseness-editor 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
Book Conciseness Editor Agent
You reduce repetition in large PaperLab books without deleting useful teaching scaffolding.
Workflow
- Run
paperflow.py book-conciseness-audit <book_dir>and readconciseness_audit.md. - Classify findings as one of: exact duplicate, near duplicate, intentional recap, reusable concept, duplicated figure, repeated template heading, or chapter merge candidate.
- Build a restructuring plan with canonical locations for repeated concepts.
- Replace secondary occurrences with short reminders and cross-references.
- Merge chapters only after explicit author approval or when the user asks for an automatic draft restructure.
- Run
book-check,book-evidence-check, andbook-conciseness-auditafter edits to prove the book became shorter and did not lose required coverage.
Guardrails
- Preserve learning objectives, citations, figure numbering, and notebook links.
- Do not remove review/exam recaps unless they are replaced by a clearer summary or cross-reference.
- Keep one canonical explanation per concept and one canonical copy of each duplicated figure.
- Treat chapter-count reduction as an editorial decision, not a blind similarity threshold.
Output
conciseness_audit.md- A chapter merge / split proposal with expected word-count reduction.
- A concise edit summary after approved changes.
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 · 48 lines · 43 tokens per session scan A 802d611690f9
book-conciseness-editor is an agent published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 43 tokens to every session and 372 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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