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-routergit 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-router)<a href="https://agentmods.dev/skills/sanic732/p2p-4pda-edition/rag-router"><img src="https://agentmods.dev/badge/skills/sanic732/p2p-4pda-edition/rag-router/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-router"><img src="https://agentmods.dev/badge/skills/sanic732/p2p-4pda-edition/rag-router.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 | $0.00154 | $0.00853 |
| Opus 5 | $0.00077 | $0.00426 |
| Sonnet 5 | $0.00031 | $0.00171 |
| Haiku 4.5 | $0.00015 | $0.00085 |
Grade A, and why
rag-router 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 11d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rag-router — выбор среды и стратегии под задачу
Советует, КУДА класть данные и КАК их извлекать, по размеру корпуса, типу задачи и месту данных. Стержень — «задача → инструмент», без гадания.
Когда применять / НЕ применять
Применять: не ясно, брать «+»/NotebookLM/Cowork/чат и какую стратегию/модель.
НЕ применять: готовить файлы (→ rag-prep); писать сам запрос (→ rag-grounding).
Ось 1 — среда (куда грузить)
Строгая опора на документы + цитаты → NotebookLM (облако)
Креатив / микс файлов с вебом → Gemini-чат, файлы через «+»
Многошаговая офисная работа с локальными файлами → Claude Cowork (ПК)
Разработка / правки кода → Claude Code / Antigravity IDE
Просто обсудить, пара файлов → Chat / Project
Развилка «+» vs NotebookLM: напрямую — модель мешает «от себя» (надёжно до ~50K токенов); через NotebookLM — выжимка с цитатами, но риск туннельного зрения.
Ось 2 — стратегия (как извлекать), по размеру корпуса
< 20 док / < 50K токенов → Naive RAG / прямое прикрепление
20–500 документов → RAPTOR (иерархическое дерево сводок)
> 500 док / высокая связность → векторная база + LongRAG (единица — документ)
Глобальные вопросы «темы корпуса» → GraphRAG (community-сводки)
Многошаговые цепочки по докам → multi-hop / итеративный (агентная среда)
Ось 3 — место данных и регуляторика
- Облако Google, нужен grounded-ресёрч → NotebookLM (данные не идут в обучение).
- Локальные файлы/офис на ПК → Cowork.
- Регулируемое (HIPAA/PCI/SOX) → ни Cowork, ни «как есть» — нужен отдельный контур.
Модель под кейс (ориентир)
Строгий grounding/большой контекст → Opus 4.8 / Gemini 3.1 Pro; быстро/массово → Sonnet 4.6 / Gemini 3.5 Flash. Дешёвые (Haiku, Flash-Lite) — не для строгого RAG.
Вывод
Дай 1 рекомендацию (среда + стратегия + модель) + 1–2 строки «почему» и явное «когда это менее уместно». Правильный вопрос — не «что лучше вообще», а «что меньше всего мешает этой задаче».
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.
- 11d ago First seen · 58 lines · 154 tokens per session scan A f185d8ff4532
rag-router is a skill published in the GitHub repository sanic732/P2P-4PDA-edition (17 stars, last pushed 23d ago), licensed MIT. It adds 154 tokens to every session and 853 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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