Vector Memory Hack

# 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.

## Discovery Metadata
- Category: `productivity`
- Framework: `ClawHub`
- Tags: `efficiency`, `lightweight`, `memory`, `search`, `semantic`, `sqlite`, `tfidf`, `files`, `agent`, `context`

## Agent Execution Policy

This listing is **discovery metadata only**. Canonical instructions are maintained by ClawHub.

### Before Executing Actions

1. **Fetch canonical instructions** from: https://clawhub.ai/skill/vector-memory-hack
2. **Parse the skill page** for setup, usage, and input/output requirements.
3. **Only proceed** after successfully loading and understanding the full instructions.

### If Fetch Fails

- Return `instruction_unavailable` with reason.
- Do **not** attempt to infer or improvise execution steps from this metadata alone.

## Source

- ClawHub listing: https://clawhub.ai/skill/vector-memory-hack