Vector Memory Hack

Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, <10ms search time.

This skill appears to implement local TF-IDF search for an agent's MEMORY.md, but there are inconsistencies and privacy concerns you should address before installing: - Verify dependency claims: the code comments mention scikit-learn but the README and SKILL.md say "zero dependencies." Open the full scripts/vector_search.py and ensure it does not import non-stdlib packages; if it does, vendor or install those explicitly and review them. - Update default paths before running: change MEMORY_PATH and VECTORS_DIR in scripts/vector_search.py to point to a safe test file/directory. Don't run it against your real workspace until you confirm behavior. - Inspect MEMORY.md for secrets: because the tool is designed to surface items like server addresses and credentials, remove or rotate any secrets in MEMORY.md or restrict the file before using the skill. - Confirm absence of network I/O: scan the full script for imports like requests, urllib, socket, or subprocess calls that could transmit data. In the provided excerpts there are no obvious network calls, but review the rest of the file to be sure. - Note missing files/claims: README and SKILL.md mention a CLI wrapper 'vsearch' but that wrapper isn't included — if you rely on that, either create a safe wrapper or call the Python script directly. - Test in a sandbox: run the script in an isolated container or VM on a non-sensitive MEMORY.md to validate behavior and performance claims before integrating with agents. If you want, I can: 1) scan the remainder of scripts/vector_search.py for any network or obfuscated behavior, 2) produce a safe replacement wrapper that respects a configurable MEMORY_PATH, or 3) show exact edits to hardcoded paths and logging to make operation explicit and safer.

Type: OpenClaw Skill Name: Developer: Version: Description: OpenClaw Agent Skill The OpenClaw skill 'vector-memory-hack' provides a lightweight, local semantic search for an AI agent's `MEMORY.md` file using TF-IDF and SQLite. All file operations in `scripts/vector_search.py` are confined to the agent's workspace (`/root/.openclaw/workspace/`), and the script uses only Python's standard library, with no external dependencies or network calls. The `SKILL.md` instructions are clear, directly related to the stated purpose, and do not contain any prompt injection attempts to exfiltrate data or perform unauthorized actions. The mention of 'Server addresses and credentials' in `SKILL.md` is in the context of the agent retrieving this information from its own memory for a task, not stealing it.