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jbvc/rag-implementation

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

52

Quality

65%

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SecuritybySnyk

High

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Security

1 high severity finding. You should review these findings carefully before considering using this skill.

High

W007: Insecure credential handling detected in skill instructions.

What this means

The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.

Why it was flagged

The prompt includes code that embeds API keys as literal strings (e.g., pinecone.init(api_key="your-api-key")), which encourages placing secrets directly in code/output and could lead the LLM to handle or reproduce secret values verbatim.

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