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 rag-prepgit 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/rag-prep)<a href="https://agentmods.dev/skills/sanic732/p2p-4pda-edition/rag-prep"><img src="https://agentmods.dev/badge/skills/sanic732/p2p-4pda-edition/rag-prep/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/rag-prep"><img src="https://agentmods.dev/badge/skills/sanic732/p2p-4pda-edition/rag-prep.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 | $0.00168 | $0.00967 |
| Opus 5.5 | $0.00067 | $0.00387 |
| Sonnet 5.5 | $0.00034 | $0.00193 |
| Haiku 4.5 | $0.00017 | $0.00097 |
Grade A, and why
rag-prep 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 1mo 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rag-prep — подготовка источника под RAG
Превращает сырой/длинный документ в источник, который RAG-движок хорошо режет на чанки и точно извлекает. Это «предварительное редактирование» перед загрузкой в NotebookLM / Claude Project / Cowork.
Когда применять / НЕ применять
Применять: перед загрузкой материала в RAG-источник; чистка «простыни»;
структурирование под retrieval. НЕ применять: нужно задать запрос к уже
готовой базе (→ rag-grounding); выбрать инструмент/стратегию (→ rag-router).
Принцип
Качество ответа RAG = качество попавших в контекст чанков. Значит каждый чанк должен быть самодостаточным и точно именованным. Правило: один файл = одна семантическая зона, один параграф = одна концепция, один тег = один сигнал.
Что делает (по шагам)
- Структура. Режет текст на разделы по Markdown-заголовкам (
#,##) — это естественные границы чанков. Никакого «полотна» без заголовков. - Чанк-безопасность. Логические блоки (
IF … THEN, определение+пояснение) держит целиком в одном разделе. Где нужно — закладывает overlap-формулировки на границах. - Frontmatter в шапку файла:
source_id,tags, при необходимостиdepends_on(граф связей). - Якоря и теги. Стабильные
#IDна ключевых блоках (для точного lookup) +## TAGS: …в конце (тематическая выборка). - Чистка и дедуп. Убирает шум/повторы; дубли заменяет ссылкой, а не копией.
- Дробление. Если файл несёт несколько зон — предлагает разбить на отдельные
файлы (одна зона = один файл) и даёт строку для
_INDEX.md.
Полный стандарт разметки (frontmatter, rag_zone, rag_anchor, дедуп, граф
зависимостей) — в reference/rag_metaprompt_standard.md; открывать при сложной
многофайловой структуре.
Важно (анти-мифы)
- Большое окно (1M) не отменяет структуру: retrieval-качество ≠ размер окна.
- Символьный тег
[A-P_RULES]в dense-поиске «растворяется» — для надёжного lookup дублируй ID в frontmatter/метаданных и рассчитывай на гибридный поиск.
What ships with it
1 file 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.
- 1mo ago First seen · 63 lines · 168 tokens per session scan A cc36f6227aa0
rag-prep is a skill published in the GitHub repository sanic732/P2P-4PDA-edition (17 stars, last pushed 2mo ago), licensed MIT. It adds 168 tokens to every session and 967 once invoked, about $0.0007 per session on Opus 5.5. 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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markitdown
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Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM…
surfsense-documents
Make or change a Word document (.docx), a PDF, a PowerPoint deck (.pptx) or an Excel workbook (.xlsx) by writing a Python script that surfsenserenderdocument runs, optionally starting from a source's own Word or PowerPoint file. Load it whenever the user asks for one of these, or for a change to one you made.
surfsense-html
Make or change a web page from the user's sources - a report, a briefing, a dashboard, a comparison, a timeline, an explainer, an interactive calculator or a figure that shows how something works - by writing an HTML file and keeping it with surfsensesavepage. Load it whenever the user asks for a web page, an HTML…