rag-implementation
Build Reliable RAG Systems
RAG projects fail when retrieval quality, source controls, and evaluation are designed too late. This skill guides Claude, Codex, and Claude Code through vector stores, chunking, retrieval, reranking, citations, and tests.
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Review the Skillstore skill "rag-implementation" from https://skillstore.io/skills/sickn33-rag-implementation.md and its manifest at https://skillstore.io/api/skills/sickn33-rag-implementation/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
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Test it
Using "rag-implementation". Build a support documentation Q&A assistant.
Expected outcome:
A concise architecture plan covering document ingestion, chunk sizing, vector store choice, hybrid retrieval, citations, and groundedness checks.
Using "rag-implementation". Retrieval returns irrelevant answers for policy questions.
Expected outcome:
A troubleshooting checklist for metadata filters, query variants, reranking, chunk overlap, missing documents, and answer prompts.
Using "rag-implementation". Compare vector stores for an internal knowledge base.
Expected outcome:
A decision summary comparing managed, open-source, local, and high-performance stores by scale, operations, filtering, and deployment model.
Security Audit
SafeThe review found no executable behavior in the skill package. The external command alerts are Markdown code fences around Python RAG examples. The URL and API key alerts are illustrative configuration placeholders, and no prompt injection or exfiltration intent was found.
Risk Factors
⚙️ External commands (37)
🌐 Network access (1)
🔑 Env variables (1)
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https://skillstore.io/skills/sickn33-rag-implementation/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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APA citation
sickn33. (2026). rag-implementation security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-rag-implementation/audits/4BibTeX citation
@techreport{sickn33-sickn33-rag-implementation-2026,
author = {sickn33},
title = {rag-implementation security audit report (audit version 4)},
institution = {Skillstore},
year = {2026},
number = {4},
url = {https://skillstore.io/skills/sickn33-rag-implementation/audits/4},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "rag-implementation security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-07"
url: "https://skillstore.io/skills/sickn33-rag-implementation/audits/4"
identifiers:
- type: other
value: "skillstore:sickn33-rag-implementation:audit:4"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
wshobson-rag-implementation
2026-08-21
sickn33-rag-implementation
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Prototype a document Q&A assistant
Developers can plan a chatbot that retrieves product, support, or policy documents before answering user questions.
Improve enterprise knowledge search
Data engineers can compare storage, metadata, update cadence, and retrieval methods for internal documents.
Evaluate grounded AI answers
Product teams can design test questions, groundedness checks, and citation behavior before launch.
Try These Prompts
Use the RAG implementation skill to design a basic document Q&A system for [corpus]. Include corpus scope, chunking, embeddings, vector store, retrieval flow, and success checks.
Compare dense, sparse, hybrid, and multi-query retrieval for [use case]. Recommend one approach with tradeoffs, metadata filters, reranking needs, and evaluation metrics.
Design a production RAG pipeline for [domain]. Include ingestion, access control, incremental updates, chunking, embeddings, vector store configuration, retrieval, reranking, citations, monitoring, and rollback.
Analyze this RAG failure: [symptom]. Build a troubleshooting plan for chunk size, embeddings, query reformulation, metadata filters, reranking, prompt grounding, and test cases.
Best Practices
- Define access rules before indexing documents, especially for restricted or customer data.
- Evaluate retrieval and answer quality with test questions before production use.
- Return citations and source metadata when responses depend on retrieved content.
Avoid
- Indexing documents before defining chunking rules, metadata, and refresh cadence.
- Using a single retrieval method without testing sparse, dense, or hybrid search.
- Treating high similarity scores as proof that an answer is grounded.
Frequently Asked Questions
Does this skill build a full RAG app?
Which vector database should I choose?
Can it help reduce hallucinations?
Does it include security guidance?
Can Claude Code or Codex use it?
What data do I need before using it?
Developer Details
Author
sickn33License
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/rag-implementationRef
9f814fc6a43fd99946f2da5e0df231c65a38bc76
Maintenance freshness
7/18/2026
Usage
6 downloads · 115 views
File structure
📄 SKILL.md