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 Notysoty/openagentskills --skill rag-chunking-advisorgit clone --depth 1 https://github.com/Notysoty/openagentskillsWrote 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/notysoty/openagentskills/rag-chunking-advisor)<a href="https://agentmods.dev/skills/notysoty/openagentskills/rag-chunking-advisor"><img src="https://agentmods.dev/badge/skills/notysoty/openagentskills/rag-chunking-advisor/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/notysoty/openagentskills/rag-chunking-advisor"><img src="https://agentmods.dev/badge/skills/notysoty/openagentskills/rag-chunking-advisor.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 | $0.00031 | $0.01453 |
| Opus 5 | $0.00015 | $0.00727 |
| Sonnet 5 | $0.00006 | $0.00291 |
| Haiku 4.5 | $0.00003 | $0.00145 |
Grade A, and why
RAG Chunking Strategy Advisor 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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Chunking Strategy Advisor
What this skill does
This skill analyzes your document types, content structure, and retrieval goals to recommend the right chunking strategy for your RAG pipeline. Poor chunking is the #1 cause of RAG failures — chunks too large lose precision, chunks too small lose context. This skill picks the right strategy and explains exactly how to implement it.
How to use
Claude Code / Cline
Copy this file to .agents/skills/rag-chunking-advisor/SKILL.md in your project root.
Then ask:
- "Use the RAG Chunking Strategy Advisor to help me chunk our legal contract PDFs."
- "What chunking strategy should I use for markdown documentation with code blocks?"
Provide:
- Document type (PDFs, markdown, HTML, code, emails, etc.)
- Typical document length
- What users will search for (questions, keywords, concepts)
- Your embedding model if known
Cursor / Codex
Paste the instructions below along with your document type and retrieval use case.
The Prompt / Instructions for the Agent
When asked to advise on RAG chunking, follow these steps:
Step 1 — Identify document characteristics
Ask or infer:
- Document type: structured (tables, headers) vs. unstructured (prose) vs. code
- Length: short (< 1 page), medium (1–20 pages), long (20+ pages)
- Internal structure: does it have headers, sections, numbered lists, code blocks?
- Query type: factual lookups, conceptual questions, code search, or multi-hop reasoning?
Step 2 — Select the primary chunking strategy
| Document Type | Recommended Strategy | Chunk Size |
|---|---|---|
| Prose (articles, books) | Sentence-window or recursive character | 512–1024 tokens |
| Structured docs (markdown, HTML) | Header-based (split on H2/H3) | Full section |
| PDFs with mixed content | Semantic chunking + page boundary | 512 tokens |
| Source code | Function/class boundary splitting | Full function |
| Tables / spreadsheets | Row-level or table-level | 1 row or full table |
| Emails / short messages | Document-level (no chunking) | Full doc |
| Legal / contracts | Clause-level splitting | 256–512 tokens |
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.
- 9d ago First seen · 143 lines · 31 tokens per session scan A 21f708fff938
RAG Chunking Strategy Advisor is a skill published in the GitHub repository Notysoty/openagentskills (9 stars, last pushed 26d ago), licensed MIT. It adds 31 tokens to every session and 1,453 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 2026-08-31.
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