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-workflow-plannergit 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-workflow-planner)<a href="https://agentmods.dev/skills/notysoty/openagentskills/rag-workflow-planner"><img src="https://agentmods.dev/badge/skills/notysoty/openagentskills/rag-workflow-planner.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 | $0.00037 | $0.02370 |
| Opus 5 | $0.00018 | $0.01185 |
| Sonnet 5 | $0.00007 | $0.00474 |
| Haiku 4.5 | $0.00004 | $0.00237 |
Grade A, and why
RAG Workflow Planner 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 8d 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Workflow Planner
What this skill does
This skill walks through your RAG use case requirements and designs a complete, production-ready Retrieval-Augmented Generation pipeline. It recommends a chunking strategy, embedding model, vector store, retrieval approach, and reranking strategy — each choice justified against your specific requirements. The output includes an ASCII architecture diagram, a component-by-component breakdown, and a starter code outline.
Use this when you're starting a new RAG project or when an existing RAG pipeline is producing poor retrieval quality and you want a systematic redesign.
How to use
Claude Code / Cline
Copy this file to .agents/skills/rag-workflow-planner/SKILL.md in your project root.
Then describe your use case and ask:
- "Use the RAG Workflow Planner skill to design a pipeline for our customer support chatbot."
- "Plan a RAG architecture for our internal knowledge base search using the RAG Workflow Planner skill."
The more detail you provide about document types, query patterns, and latency requirements, the more precise the output.
Cursor
Add the "Prompt / Instructions" section to your .cursorrules file. Describe your RAG use case in the chat.
Codex
Describe your use case and answer the requirements questions in the instructions below, then include the full instructions. Codex will produce the architecture design.
The Prompt / Instructions for the Agent
When asked to design a RAG pipeline, follow these steps:
- Gather requirements. If not provided, ask for:
- Document types: PDFs, HTML pages, markdown files, database records, code files, emails, etc.
- Corpus size: A few dozen documents, thousands, millions?
- Query types: Short factual questions? Multi-step reasoning queries? Semantic search? Code search?
- Latency requirements: Real-time chat (< 2 seconds) or batch/async?
- Freshness requirements: Static documents, or does the corpus update frequently?
- Language: English only, or multilingual?
- Budget constraints: Self-hosted vs. managed APIs?
- Existing tech stack: Are there database or cloud provider preferences?
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.
- 8d ago First seen · 207 lines · 37 tokens per session scan A 6a15363b9cab
RAG Workflow Planner is a skill published in the GitHub repository Notysoty/openagentskills (9 stars, last pushed 24d ago), licensed MIT. It adds 37 tokens to every session and 2,370 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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