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.
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Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install vector-memory-hackOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install vector-memory-hackβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/vector-memory-hack/π‘Extract and place the folder at the path above, then restart your agent.
Category
Documents & NotesWhat Vector Memory Hack can do for your AI workflow
Fast semantic search for ai directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
Trusted by 2,856+ developers worldwide
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Vector Memory Hack
Help me get started with Vector Memory Hack
Explains what Vector Memory Hack does, walks through the setup, and runs a quick demo based on your current project
Use Vector Memory Hack to fast semantic search for AI agent memory files using TF-IDF and SQLite
Invokes Vector Memory Hack with the right parameters and returns the result directly in the conversation
What can I do with Vector Memory Hack in my documents & notes workflow?
Lists the top use cases for Vector Memory Hack, with example commands for each scenario
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Vector Memory Hack extends your AI assistant with the ability to 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. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Vector Memory Hack as its underlying capability.
Vector Memory Hack works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Vector Memory Hack once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
To install Vector Memory Hack, copy the skill folder to `~/.claude/skills/vector-memory-hack/` for use across all your projects, or `.claude/skills/vector-memory-hack/` for a single project. Restart Claude and the skill is immediately active β invoke it with `/vector-memory-hack` or just describe your goal and the AI will pick it up automatically.
Vector Memory Hack has been installed 2,856 times, making it one of the more actively used skills in the Documents & Notes category. The install rate suggests it solves a real, recurring need rather than a niche edge case. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/vector-memory-hack/ for personal use (all projects), or .claude/skills/vector-memory-hack/ for project-specific use. Restart your AI client, then invoke with /vector-memory-hack or let the AI discover it automatically.
Vector Memory Hack supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Vector Memory Hack is free to install. Check the repository for licensing information.
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.
Automate my documents & notes tasks using Vector Memory Hack
Identifies repetitive steps in your workflow and sets up Vector Memory Hack to handle them automatically
Vector Memory Hack is categorized under Documents & Notes. These skills help AI agents perform specialized tasks in this domain.
Local memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.