pgvector

PostgreSQL vector database skill with pgvector extension. Enables vector similarity search, embeddings storage, RAG (Retrieval-Augmented Generation) pipeline...

This skill appears to be a straightforward pgvector/Postgres cheat-sheet and is consistent with its description. Before installing or invoking it: (1) ensure you only connect the agent to a test or controlled Postgres instance first — the SQL examples include CREATE/INSERT/UPDATE/DELETE which can modify data; (2) do not use the example 'empty' password in production and prefer a least-privilege DB user; (3) ensure the pgvector extension is installed on the target DB and that the agent has only the permissions it needs (read-only if you only want retrieval); (4) note the skill is instruction-only from an unknown/anonymous source (no homepage) — if you need guarantees about correctness or safety, review the SQL commands yourself or run them in a sandbox before letting an agent execute them automatically.

Type: OpenClaw Skill Name: pgvector Version: 1.0.0 The skill provides standard documentation, SQL templates, and a Python snippet for using the pgvector extension in PostgreSQL. It includes connection details for a local development environment (user 'damien', port 5433) and common operations like similarity search and RAG pipelines, with no evidence of malicious intent, data exfiltration, or prompt injection.