skills — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited skills (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
A simple local RAG (Retrieval-Augmented Generation) system based on memvid.
This plugin provides semantic search capabilities for PDF documents using vector embeddings. It enables you to index documents into a local knowledge base and perform semantic queries to retrieve relevant information.
pip install memvid-sdk)OPENAI_API_KEY environment variable before indexing.pypdf (uv add pypdf)python-docx (uv add python-docx)openpyxl (uv add openpyxl)| Command | Description |
|---|---|
/memvid-rag:create | Create a new knowledge.mv2 index file |
/memvid-rag:index <path> | Index PDF files into the knowledge base |
/memvid-rag:search <query> | Search indexed documents semantically |
/memvid-rag:status | Show current status of the RAG system |
/memvid-rag:create to initialize the knowledge base (if not exists)/memvid-rag:index <pdf_path> to add documents/memvid-rag:search <your question>The search command supports optional parameters:
--k=<number>: Maximum results to return (default: 5)--mode=<lex|sem|auto>: Search mode (default: auto)--snippet_chars=<number>: Max characters per snippet (default: 240)--min_relevancy=<float>: Minimum relevancy threshold--adaptive=<true|false>: Enable adaptive result count/memvid-rag:index ./documents/research-paper.pdf
/memvid-rag:search What are the key findings about semantic search?
/memvid-rag:search How does RAG improve LLM accuracy? --k=10 --mode=semAll indexed data is stored locally in knowledge.mv2 file in the current working directory.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.