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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.

dylanbaker240 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install dylanbaker24/memory-hygiene

Inspect the source and compatibility requirements, then follow its installation steps.

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View source ↗Guides

Skill instructions

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

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:

{
  "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

Source information

Source
github
Repository
openclaw/skills
Path
skills/dylanbaker24/memory-hygiene
Version
—
Commit
763cf130ddaa1133fda299afa7e2d0bc7ca86c8c
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# 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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