vector-memory-hackFast 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.
Install via ClawdBot CLI:
clawdbot install mig6671/vector-memory-hackGrade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
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https://img.shields.io/badge/License-MIT-yellow.svgAudited Apr 16, 2026 · audit v1.0
Generated Mar 1, 2026
AI agents use this skill to quickly retrieve relevant context from memory files before executing tasks, such as finding backup rules or security protocols, reducing token usage and improving response accuracy. Ideal for autonomous systems handling routine IT operations or customer support.
Developers integrate this tool into their workflows to semantically search through large markdown documentation, like API guides or internal wikis, for specific rules or code snippets without manual scanning. Enhances productivity in software development and DevOps.
Deployed on resource-constrained edge devices or VPS, this skill enables efficient retrieval of configuration rules and procedures from local memory files, supporting maintenance tasks without cloud dependencies. Suitable for IoT or remote infrastructure management.
Organizations with multilingual documentation use this skill to perform semantic searches across content in languages like Czech, English, or German, aiding in compliance checks or knowledge management without heavy translation tools.
Startups and researchers leverage this lightweight skill for quick prototyping of AI agents that need memory retrieval, avoiding setup overhead from heavy embedding models. Accelerates development in hackathons or proof-of-concept projects.
Offer the core skill as free open-source software to build a user base, then monetize through premium features like advanced analytics, cloud sync, or enterprise support. Generates revenue from subscriptions and consulting services.
Package the skill as a cloud-based service with APIs for easy integration into existing AI platforms or documentation tools. Charge based on usage tiers, such as search volume or number of documents indexed.
Sell licensed versions to enterprises for internal deployment, with added features like enhanced security, custom tokenization, and dedicated support. Focus on industries needing offline, low-latency memory search solutions.
💬 Integration Tip
Before each agent task, run a search to fetch relevant context from memory files, ensuring efficient token usage and accurate execution without reading entire documents.
Scored Apr 19, 2026
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