A maintenance skill designed to audit, purge, and optimize Clawdbot's LanceDB vector memory for better performance and lower token costs.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memory-hygiene
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install memory-hygiene using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Memory Hygiene is a critical utility for Openclaw Skills users who want to maintain a lean and efficient AI agent. Over time, vector memory can become cluttered with junk memories like heartbeats or transient logs, leading to high token usage and irrelevant auto-recalls. This skill provides the tools and protocols necessary to audit existing memory, wipe bloated databases, and reseed essential facts.
By synthesizing the core value of intentional storage, Memory Hygiene ensures your agent remains focused on high-value data like project decisions, user preferences, and key architectural choices while ignoring transient noise. It is an essential component for long-term agent stability and cost-efficiency.
To configure Memory Hygiene within the Openclaw Skills ecosystem, first disable autoCapture via gateway action=config.patch:
{
"plugins": {
"entries": {
"memory-lancedb": {
"config": {
"autoCapture": false,
"autoRecall": true
}
}
}
}
}
To perform a manual wipe of the memory, run the following command in your terminal followed by a gateway restart:
rm -rf ~/.clawdbot/memory/lancedb/
clawdbot gateway restart
Memory Hygiene interacts with the LanceDB vector store and organizes data based on importance and category metadata. Entries are optimized when kept under 100 words.
| Category | Content Type | Importance Score |
|---|---|---|
| preference | Workflow, tools, and communication styles | 0.8 - 1.0 |
| fact | Accounts, contacts, and credential locations | 0.9 - 1.0 |
| decision | Project choices and architectural paths | 0.7 - 0.9 |
| lesson | Retrospectives and optimized patterns | 0.6 - 0.8 |
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