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Commits, branches and sync with the but CLI
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Designing a RAG retrieval system v1.0.0
Use this resource to design and evaluate a RAG system with embeddings and a vector store
New
- 544 downloads
- 157 likes
- 236 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install rag-retrieval-pipeline-setupWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent first checks that RAG is needed and compares simpler alternatives, then follows an eight-phase process covering the knowledge base, embeddings, vector architecture, retrieval pipeline and evaluation. You get an implementation plan and a checklist.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: need check, then eight-phase implementation
- README.md: overview and principles of the method
Good fit for
- You want answers drawn from your own documents
- You need to ground a language model's answers in internal data
Not for
Tags
- RAG
- embeddings
- vector store
- retrieval
About
You say
“Help me design a RAG over our internal docs: chunking, embeddings and evaluation.”
Skill published by daffy0208 under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to design and evaluate a RAG system with embeddings and a vector store
- Review and adjust the result
Content preview
Files
- LICENSE1.1 KB
- manifest.yaml3.3 KB
- NOTICE-PALATEKIT.md0.5 KB
- README.md5 KB
- SKILL.md14.1 KB
Origin and licence
Listed unmodified
- Author
- daffy0208
- Source
- github.com/daffy0208/ai-dev-standards/tree/32c3241e48f9fe2c3de611ad9168503ea41f28fd/skills/rag-implementer
- Licence
- MIT
Notice
« RAG Implementer » (daffy0208, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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