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 sanic732/P2P-4PDA-edition --skill notebook-packgit clone --depth 1 https://github.com/sanic732/P2P-4PDA-editionWrote 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/sanic732/p2p-4pda-edition/notebook-pack)<a href="https://agentmods.dev/skills/sanic732/p2p-4pda-edition/notebook-pack"><img src="https://agentmods.dev/badge/skills/sanic732/p2p-4pda-edition/notebook-pack/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/sanic732/p2p-4pda-edition/notebook-pack"><img src="https://agentmods.dev/badge/skills/sanic732/p2p-4pda-edition/notebook-pack.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00151 | $0.00916 |
| Opus 5 | $0.00076 | $0.00458 |
| Sonnet 5 | $0.00030 | $0.00183 |
| Haiku 4.5 | $0.00015 | $0.00092 |
Grade A, and why
notebook-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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
notebook-pack — готовый комплект источников под RAG
End-to-end сборка: из набора сырых материалов делает структурированную базу,
которую остаётся загрузить в NotebookLM / Project / Cowork и сразу спрашивать.
Объединяет rag-prep (чистка/структура), rag-grounding (мастер-промпт) и
rag-router (выбор стратегии).
Когда применять / НЕ применять
Применять: есть материалы → нужен цельный комплект под загрузку. НЕ применять:
надо обработать один файл (→ rag-prep) или только собрать запрос (→ rag-grounding).
Что собирает (структура комплекта)
notebook/
├── _MASTER_PROMPT.md grounding-каркас (PERSONA/TASK/CONTEXT/OUTPUT/GUARD),
│ в синтаксисе целевой модели — системный промпт блокнота
├── _INDEX.md карта файлов + граф зависимостей (тег → файлы)
├── _GLOSSARY.md термины проекта (терминологический якорь)
└── <зоны>.md источники: одна зона = один файл, frontmatter+TAGS+якоря
Процесс
- Маршрут (rag-router): оцени объём/тип → стратегия (naive/RAPTOR/LongRAG), среда (NotebookLM/Cowork), целевая модель.
- Подготовка (rag-prep): каждый источник → чистый Markdown, одна зона = один
файл, заголовки под чанкинг, frontmatter +
## TAGS+ якоря, без разрыва IF/THEN. - Индекс и глоссарий: собери
_INDEX.md(таблица «ID → назначение → связи») и_GLOSSARY.md(ключевые термины с определениями своими словами). - Мастер-промпт (rag-grounding):
_MASTER_PROMPT.mdпод целевую модель, с принуждением к цитатам и маркером UNKNOWN, guard сформулирован позитивно. - Чек запуска: прогнать чек-лист готовности (ниже).
Полные образцы и стандарт — в reference/rag_notebook_prompt.md (готовый системный
промпт + рабочая инструкция) и reference/rag_metaprompt_standard.md (стандарт
разметки мультифайловых систем). Открывать при сборке.
Чек-лист готовности комплекта
-
_MASTER_PROMPTвставлен, значения подставлены под проект и модель. - Есть
_INDEX.mdс картой и зависимостями; есть_GLOSSARY.md. - Каждый файл: одна зона, frontmatter +
## TAGS, якоря; нет «простыней». - Дубли заменены ссылками; IF/THEN не разорваны.
- Версия = отдельный блокнот (для крупных версий).
- Прогон 10 тестовых запросов: цитаты точные? правильный файл? нет смешения тем?
- Все файлы — UTF-8.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 60 lines · 151 tokens per session scan A 084e64a779e2
notebook-pack is a skill published in the GitHub repository sanic732/P2P-4PDA-edition (17 stars, last pushed 22d ago), licensed MIT. It adds 151 tokens to every session and 916 once invoked, about $0.0008 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 2026-08-30.
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