Check memory system health and data quality. Use when user asks "how's my memory?", "system health", "memory stats", "data quality", "how's my brain?", or for periodic self-diagnostics.
Scanned 2/12/2026
Install via CLI
openskills install kbanc85/claudia---
name: memory-health
description: Check memory system health and data quality. Use when user asks "how's my memory?", "system health", "memory stats", "data quality", "how's my brain?", or for periodic self-diagnostics.
effort-level: medium
---
# Memory Health
Provide a dashboard view of the memory system's health, including entity counts, memory statistics, data quality indicators, and recommendations.
## Triggers
- User says "memory health", "memory stats", "brain check"
- User says "data quality", "how's my memory system?"
- User says "how much do you remember?", "what's in your brain?"
- Periodic self-check (weekly review, morning brief)
## Workflow
### Step 1: Gather Statistics
Use `memory.session_context` to get current system state:
```
memory.session_context(scope="full")
```
This returns entity counts, memory counts, relationship counts, and predictions.
### Step 2: Calculate Health Indicators
From the session context, derive:
**Entity Health**
- Total entities by type (people, projects, organizations, topics)
- Entities with no associated memories (orphans)
- Entities not mentioned in 90+ days (stale)
**Memory Health**
- Total memories by type (fact, preference, observation, learning)
- Average importance score
- Invalidated vs. active memories
- Corrected memories count
**Relationship Health**
- Total active relationships
- Relationships marked as invalid
- Cooling relationships (no recent activity)
**Data Quality**
- Potential duplicate entities (fuzzy name match)
- Orphan memories (no entity links)
- Memories below importance threshold (0.3)
### Step 3: Present Dashboard
Format:
```
## Memory System Health Report
### Entities
| Type | Count | Stale (90d) |
|--------------|-------|-------------|
| People | 23 | 2 |
| Projects | 12 | 5 |
| Organizations| 8 | 0 |
| Topics | 15 | 3 |
### Memories
- **Total:** 847 active memories
- **Average importance:** 0.72
- **By type:** 412 facts, 198 preferences, 156 observations, 81 learnings
- **Corrected:** 12 memories have been corrected
- **Invalidated:** 34 memories marked as no longer true
### Relationships
- **Active:** 67 relationships
- **Cooling:** 8 relationships (no contact in 30+ days)
### Data Quality
- **Potential duplicates:** 3 entity pairs to review
- **Orphan memories:** 5 memories without entity links
- **Low importance:** 23 memories below 0.3 threshold
### Recommendations
1. Review potential duplicates: "John Smith" and "Jon Smith" may be the same person
2. Consider archiving 5 stale projects with no recent activity
3. 8 relationships are cooling - may want to reconnect
```
## Quick Stats Mode
If user just wants numbers:
```
Your memory at a glance:
- 58 people, 12 projects, 8 orgs
- 847 memories (avg importance: 0.72)
- 67 relationships tracked
- Last consolidation: 2 hours ago
```
## Troubleshooting Mode
When user reports memory issues ("you forgot X", "why don't you remember"):
1. Search for the specific topic/entity
2. Check if memories exist but are below recall threshold
3. Check if memories were invalidated
4. Report findings:
```
I searched for memories about "[topic]":
- Found 3 memories, but all below importance 0.4 (not surfacing in context)
- One memory was corrected on [date]
- Recommendation: I can boost the importance of these if they're still relevant
```
## Recommendations Engine
Based on health metrics, suggest:
- **Duplicates found:** "Run /fix-duplicates to clean up 3 potential duplicate entities"
- **Stale entities:** "Consider archiving [X] project - no activity in 120 days"
- **Cooling relationships:** "Haven't heard about [Name] in 45 days - want me to add a follow-up?"
- **Low memory count:** "I only have [N] memories about [Entity] - we could add more context"
- **High invalidation rate:** "12% of memories about [Entity] were invalidated - the situation may have changed significantly"
## Never
- Expose raw database IDs or technical details to user
- Make the user feel bad about "memory problems"
- Automatically delete or modify data based on health checks
- Claim perfect memory - always acknowledge limitations
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