What is Vector Memory Hack good for?

Vector Memory Hack is a fit for: Database tools, AI knowledge base. See AgentHub scenario pages for pairings.

Vector Memory Hack Skill Install & Guide

Q: How do I install Vector Memory Hack?

SkillClawHub

io.clawhub.mig6671/vector-memory-hack · v1.0.3

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.

View on ClawHub ↗

Copy the install config on this page first, then verify docs and permissions upstream.

Popularity

Stars9

Downloads4.1k

Installs132

Overview

Agent Memory Sqlite Documentation

Use cases

Database tools AI knowledge base

AgentHub Verified AvailabilityTested & Ready

Automated pipeline validated install commands, protocol & client compatibility

Verified At2026-09-30

Install Snippets TestedCLI & JSON config syntax verified

Protocol Handshake ReadyComplies with JSON-RPC 2.0 specifications

Origin Registry ActiveSourced from clawhub

Verified Clientsopenclaw、Claude Code、Claude Desktop 等

Security Tier: A 级 · 源码开放合规 (A)·Scanned for high-risk vulnerabilities. Recommended to run with project-scoped permissions.

Score 70/100

Send this prompt to your AI to install the Skill

Recommended

Target client (auto-optimizes prompt):

PROMPT >

Follow the install guide at https://myagenthub.cn/install/skill.md?lang=en to automatically detect the current IDE and install the skill "mig6671/vector-memory-hack" without asking, and tell me how to verify it afterwards.

💡Paste into your AI coding assistant (Cursor, Trae, Claude Code) — the agent will handle download and configuration automatically.

Copy by platformAlternative

Choose your platform

Choose install method

Click to copy snippet

# 改 --ai 切换客户端：trae | lingma | workbuddy | kimi | qwen | cursor | claude-code | windsurf | cline | vscode | codex | openclaw
curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai cursor -y

Setup tutorial

Open the Vector Memory Hack page and confirm the source is ClawHub.
Copy the install command for Trae, Lingma, Cursor, Claude Code, or universal CLI.
Run it, or place SKILL.md under .trae/skills/, .lingma/skills/, or .cursor/skills/.
Reload the client, then describe your task so the agent can match this skill.

Install commands

Install commands and setup steps are in the HTML so search engines and no-JS browsers can read them without running client JavaScript.

Universal — Skills CLI (recommended)

AgentHub CLI (change --ai)

# 改 --ai 切换客户端：trae | lingma | workbuddy | kimi | qwen | cursor | claude-code | windsurf | cline | vscode | codex | openclaw
curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai cursor -y

One-click API download

# 手动下载（Cursor 项目级示例）
mkdir -p .cursor/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .cursor/skills/vector-memory-hack

Cursor

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai cursor -y

One-click API download

mkdir -p .cursor/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .cursor/skills/vector-memory-hack

ClawHub CLI, then copy

# 在项目根目录执行 install
clawhub install mig6671/vector-memory-hack
mkdir -p .cursor/skills/vector-memory-hack
cp -R ./skills/vector-memory-hack/. .cursor/skills/vector-memory-hack/

# 源：./skills/vector-memory-hack/SKILL.md
# 目标：.cursor/skills/vector-memory-hack/SKILL.md

Trae (ByteDance)

Trae natively supports Skill specs: project-level in .trae/skills/<name>/, global in ~/.trae/skills/
Copy and run the command in your project terminal to download SKILL.md
Reload Trae window, then describe your task in AI chat to activate automatically

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai trae -y

One-click API download

mkdir -p .trae/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .trae/skills/vector-memory-hack

User directory

mkdir -p ~/.trae/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d ~/.trae/skills/vector-memory-hack

Tongyi Lingma (Alibaba)

Tongyi Lingma supports skills in project .lingma/skills/<name>/ or global ~/.lingma/skills/
Run the command to install SKILL.md
In Lingma Agent mode, prompt your task and the skill will be automatically loaded

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai lingma -y

One-click API download

mkdir -p .lingma/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .lingma/skills/vector-memory-hack

User directory

mkdir -p ~/.lingma/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d ~/.lingma/skills/vector-memory-hack

WorkBuddy / CodeBuddy (Tencent)

WorkBuddy reads skills only from .workbuddy/skills/<name>/ (user-level ~/.workbuddy/skills/); .codebuddy/skills/ belongs to CodeBuddy
The Skills CLI command installs with -a codebuddy and appends a copy step into .workbuddy/skills
AgentHub CLI (--ai workbuddy) syncs it for you; reload the chat panel so the skill gets indexed

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai workbuddy -y

One-click API download

mkdir -p .workbuddy/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .workbuddy/skills/vector-memory-hack

User directory

mkdir -p ~/.workbuddy/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d ~/.workbuddy/skills/vector-memory-hack

Claude Code

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai claude-code --global -y

One-click API download

mkdir -p ~/.claude/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d ~/.claude/skills/vector-memory-hack

ClawHub CLI, then copy

clawhub install mig6671/vector-memory-hack
mkdir -p ~/.claude/skills/vector-memory-hack
cp -R ./skills/vector-memory-hack/. ~/.claude/skills/vector-memory-hack/

# 源：./skills/vector-memory-hack/SKILL.md
# 目标：~/.claude/skills/vector-memory-hack/SKILL.md
# https://clawhub.ai/mig6671/vector-memory-hack

Kimi Code (Moonshot AI)

Kimi Code supports standard Agent Skills: project-level in .agents/skills/<name>/, global in ~/.agents/skills/
Run the command to install
In Kimi Code CLI, describe your task to trigger the skill

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai kimi -y

One-click API download

mkdir -p .agents/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .agents/skills/vector-memory-hack

User directory

mkdir -p ~/.agents/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d ~/.agents/skills/vector-memory-hack

Qwen Code (Alibaba)

Qwen Code supports project .qwen/skills/<name>/ and global ~/.qwen/skills/
Run the command to deploy SKILL.md
Prompt the agent to trigger the skill

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai qwen -y

One-click API download

mkdir -p .qwen/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .qwen/skills/vector-memory-hack

User directory

mkdir -p ~/.qwen/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d ~/.qwen/skills/vector-memory-hack

Windsurf

Windsurf supports project-level .windsurf/skills/<name>/ or global ~/.codeium/windsurf/skills/
Run the install command and refresh Cascade
Cascade will auto-activate the skill based on its description

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai windsurf -y

One-click API download

mkdir -p .windsurf/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .windsurf/skills/vector-memory-hack

User directory

mkdir -p ~/.codeium/windsurf/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d ~/.codeium/windsurf/skills/vector-memory-hack

Cline

Cline supports standard .agents/skills/<name>/ structure
Run the command to install the skill
Ask questions in the Cline panel to trigger the skill

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai cline -y

One-click API download

mkdir -p .agents/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d .agents/skills/vector-memory-hack

Cherry Studio

Use this skill as an Assistant system prompt in Cherry Studio
Copy the command to view SKILL.md text
Paste the contents into Cherry Studio Assistant system prompt

# 下载并提取 SKILL.md 指令内容
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -p /tmp/vector-memory-hack.zip SKILL.md
# 复制上述输出，粘贴至 Cherry Studio → 助手设置 →「系统提示词」

VS Code / GitHub Copilot

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai vscode -y

One-click API download

curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
mkdir -p .github/skills/vector-memory-hack
unzip -o /tmp/vector-memory-hack.zip -d .github/skills/vector-memory-hack

OpenAI Codex

AgentHub CLI (change --ai)

curl -fsSL "https://myagenthub.cn/cli/agenthub-skill.mjs" | node --input-type=module - init mig6671/vector-memory-hack --ai codex --global -y

One-click API download

mkdir -p ~/.codex/skills/vector-memory-hack
curl -sL "https://clawhub.ai/api/v1/download?slug=vector-memory-hack&version=1.0.3" -o /tmp/vector-memory-hack.zip
unzip -o /tmp/vector-memory-hack.zip -d ~/.codex/skills/vector-memory-hack

ClawHub CLI, then copy

clawhub install mig6671/vector-memory-hack
mkdir -p ~/.codex/skills/vector-memory-hack
cp -R ./skills/vector-memory-hack/. ~/.codex/skills/vector-memory-hack/

# 源：./skills/vector-memory-hack/SKILL.md
# 目标：~/.codex/skills/vector-memory-hack/SKILL.md

OpenClaw — ClawHub CLI

# OpenClaw / ClawHub CLI（推荐）
# 在项目根或 OpenClaw 工作区目录执行
clawhub install mig6671/vector-memory-hack

# 安装后 SKILL.md 位于：
#   ./skills/vector-memory-hack/SKILL.md
# （若已配置 OpenClaw 工作区，则在 <workspace>/skills/vector-memory-hack/）

# 仅查看不安装
clawhub inspect mig6671/vector-memory-hack

Troubleshooting & Common ErrorsFAQ

Common connection errors and verified fixes for Vector Memory Hack

Getting 'connection closed' or exit code 1 in Cursor / Claude Code for Vector Memory Hack?

Usually caused by missing runtime paths or IDE environment inheritance. Fix steps: 1. Verify Node.js 18+ (npx) or Python 3.10+ (uvx) is installed; 2. Run 'which npx' or 'which uvx' in terminal, and replace 'command' with absolute path; 3. Reload or restart the client window after modifying config.

Fix Snippet

# Check binary path in terminal:
which npx
node -v

Getting 'spawn npx ENOENT' or 'command not found'?

The editor background process does not inherit your full terminal PATH. Solution: Globally install the package, or set the absolute binary path (e.g. C:\Program Files\nodejs\npx.cmd on Windows, or /usr/local/bin/npx on macOS).

Tool calls timing out or failing to download packages?

First-time package downloading might be slow or timeout due to network limits. Configure a reliable mirror or check outbound proxy settings.

Agent does not match or apply Vector Memory Hack during conversations?

Verify SKILL.md is located at .cursor/skills/Vector Memory Hack/SKILL.md (Cursor) or ~/.claude/skills/Vector Memory Hack/SKILL.md (Claude Code). You can explicitly mention the skill name in your prompt to boost match confidence.

Still not connecting? Read the MCP troubleshooting guide →

Tool Mock Playground

Sandbox

Vector Memory Hack Rule Injection & Agent Behavior Simulation · Preview tool schema & outputs without local runtime

Sandbox Ready

fn: agent_skill_runtimeInject system prompt guidelines & workflow constraints from Vector Memory Hack

Request ArgumentsJSON Schema

{
  "skill": "Vector Memory Hack",
  "rulePath": ".cursor/skills/vector-memory-hack/SKILL.md",
  "userGoal": "请使用 Vector Memory Hack 的方法与标准帮我完成当前任务",
  "client": "Cursor / Claude Code"
}

💡Parameters generated dynamically by Agent runtime

Agent Tool Output

Click 'Run Mock' above

to preview the raw response returned to the LLM

Env: AgentHub Virtual SandboxJSON-RPC 2.0

Decision Guide: Why & When to Use

Assess suitability before installing to save trial-and-error time

Best Suited For

Dev data exploration
Read-only analytics SQL
Schema documentation

When NOT to Use

Unrestricted writes on production
Exposing raw PII to AI

Recommended Workflow Pairing:View Scenario →

Postgres MCP + SQL skill → conversational BI

Skill hands-on: install to visible results

Follow the full lab (expected UI/output + contrast checks). After installing this item, verify with the tutorial prompts.

Open tutorial →

An Agent Skill is a folder with SKILL.md instructions. After install, the AI loads it automatically when your task matches its description.

How to use

After installing, describe your task in chat (or mention the skill name). The agent reads the skill description to decide when to activate it, then follows the instructions in SKILL.md. Check loaded skills via /skills in Claude Code or in your client settings.

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Keep exploring AgentHub

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Listing badge: put AgentHub on your site

Copy either snippet into your project homepage, docs, or GitHub README. The badge is a hotlinked SVG — nothing to host — and it links back to this page so visitors can find the install steps.

Preview

HTML

<a href="https://myagenthub.cn/p/io.clawhub.mig6671/vector-memory-hack" title="Listed on AgentHub: Vector Memory Hack" target="_blank" rel="noopener">
  <img src="https://myagenthub.cn/badge/io.clawhub.mig6671/vector-memory-hack?lang=en" alt="Listed on AgentHub: Vector Memory Hack" height="20" style="border:0"/>
</a>

Markdown (GitHub README)

[![Listed on AgentHub: Vector Memory Hack](https://myagenthub.cn/badge/io.clawhub.mig6671/vector-memory-hack?lang=en)](https://myagenthub.cn/p/io.clawhub.mig6671/vector-memory-hack)

Badges are generated on the fly from /badge/<package-id>, so name and listing changes propagate automatically. Keep the link target unchanged — it is what counts as the referral.

Unified Manifest

{
  "id": "io.clawhub.mig6671/vector-memory-hack",
  "type": "skill",
  "version": "1.0.3",
  "displayName": "Vector Memory Hack",
  "description": "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.",
  "author": {
    "name": "mig6671",
    "url": "https://clawhub.ai/mig6671"
  },
  "homepage": "https://clawhub.ai/mig6671/vector-memory-hack",
  "distribution": {
    "packages": [
      {
        "registryType": "source",
        "identifier": "mig6671/vector-memory-hack",
        "version": "1.0.3",
        "runtimeHint": "clawhub install"
      }
    ],
    "remotes": []
  },
  "dependencies": [],
  "installTargets": [
    "openclaw",
    "claude-code",
    "claude-desktop",
    "cursor",
    "codex",
    "vscode"
  ],
  "keywords": [
    "downloads:4094",
    "stars:9",
    "installs:132",
    "Agent Memory",
    "Sqlite",
    "Documentation"
  ],
  "provenance": {
    "origin": "clawhub",
    "originalId": "mig6671/vector-memory-hack",
    "originalUrl": "https://clawhub.ai/mig6671/vector-memory-hack",
    "isOfficial": false,
    "status": "active"
  }
}