Shared Pinecone RAG

Use the shared Pinecone RAG index for any agent in this workspace. Use when an agent needs to ingest markdown/text docs into pulse-rag or query semantic cont...

This skill is plausible for sharing a Pinecone-based RAG, but there are several red flags to check before installing or invoking it: - The manifest does not declare PINECONE_API_KEY or any required env vars, but SKILL.md says the key must be set in rag-pinecone-starter/.env. Confirm where the key lives and whether it should be explicitly declared in the skill manifest. - The instructions and scripts use a hard-coded absolute path (/home/Mike/.openclaw/workspace/rag-pinecone-starter). Ensure the path is correct for your environment and not pointing to another user's home. Prefer a workspace-relative path. - The actual behavior is inside ingest.py and query.py, which are not included here. Do NOT run the scripts until you inspect those Python files to confirm they only contact Pinecone (or other expected endpoints), handle secrets safely, and do not exfiltrate data to unknown endpoints. - Treat the Pinecone API key as sensitive. If you proceed, limit the key's permissions where possible and rotate it if you later remove the skill. - Consider running the ingest process in an isolated environment (container or dedicated service account) and test with non-sensitive data first. If you can provide ingest.py and query.py (or modify the skill to declare the required env var and use workspace-relative paths), the assessment could be upgraded to benign if their code matches the stated purpose.

Type: OpenClaw Skill Name: shared-pinecone-rag Version: 1.0.1 The skill bundle is classified as benign. Its purpose is to provide a shared Pinecone RAG (Retrieval Augmented Generation) capability, which involves ingesting documents and querying an index. The `SKILL.md` provides clear, non-malicious instructions for the AI agent, without any prompt injection attempts. The shell scripts (`scripts/ingest-shared-rag.sh`, `scripts/query-shared-rag.sh`) are straightforward wrappers that execute local Python scripts (`ingest.py`, `query.py`) within a virtual environment. While the content of these Python scripts is not provided, the shell scripts themselves handle arguments safely and do not exhibit any malicious behaviors such as data exfiltration, unauthorized remote execution, persistence mechanisms, or obfuscation. The file and network access are consistent with the stated RAG functionality.