memory-hygiene

Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.

533 stars

Best use case

memory-hygiene is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.

Teams using memory-hygiene should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/memory-hygiene/SKILL.md --create-dirs "https://raw.githubusercontent.com/sundial-org/awesome-openclaw-skills/main/skills/memory-hygiene/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/memory-hygiene/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How memory-hygiene Compares

Feature / Agentmemory-hygieneStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Memory Hygiene

Keep vector memory lean. Prevent token waste from junk memories.

## Quick Commands

**Audit:** Check what's in memory
```
memory_recall query="*" limit=50
```

**Wipe:** Clear all vector memory
```bash
rm -rf ~/.clawdbot/memory/lancedb/
```
Then restart gateway: `clawdbot gateway restart`

**Reseed:** After wipe, store key facts from MEMORY.md
```
memory_store text="<fact>" category="preference|fact|decision" importance=0.9
```

## Config: Disable Auto-Capture

The main source of junk is `autoCapture: true`. Disable it:

```json
{
  "plugins": {
    "entries": {
      "memory-lancedb": {
        "config": {
          "autoCapture": false,
          "autoRecall": true
        }
      }
    }
  }
}
```

Use `gateway action=config.patch` to apply.

## What to Store (Intentionally)

✅ Store:
- User preferences (tools, workflows, communication style)
- Key decisions (project choices, architecture)
- Important facts (accounts, credentials locations, contacts)
- Lessons learned

❌ Never store:
- Heartbeat status ("HEARTBEAT_OK", "No new messages")
- Transient info (current time, temp states)
- Raw message logs (already in files)
- OAuth URLs or tokens

## Monthly Maintenance Cron

Set up a monthly wipe + reseed:

```
cron action=add job={
  "name": "memory-maintenance",
  "schedule": "0 4 1 * *",
  "text": "Monthly memory maintenance: 1) Wipe ~/.clawdbot/memory/lancedb/ 2) Parse MEMORY.md 3) Store key facts to fresh LanceDB 4) Report completion"
}
```

## Storage Guidelines

When using memory_store:
- Keep text concise (<100 words)
- Use appropriate category
- Set importance 0.7-1.0 for valuable info
- One concept per memory entry

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