System Health

Name: helix-stats
Rating: 92
Author: enzokro

Display memory system statistics and learning loop status.

Execution

bash

HELIX="$(cat .helix/plugin_root)"
python3 "$HELIX/lib/memory/core.py" health

Output Includes

•status: HEALTHY or NEEDS_ATTENTION
•total_insights: Count of all insights
•by_tag: Breakdown by tags (debugging, pattern, eval, etc.)
•effectiveness: Average effectiveness of insights with feedback
•with_feedback: Count of insights that have received feedback (use_count > 0)
•issues: List of problems if any

Example Output

json

{
  "status": "HEALTHY",
  "total_insights": 30,
  "by_tag": {"debugging": 6, "pattern": 10, "eval": 5, ...},
  "effectiveness": 0.65,
  "with_feedback": 12,
  "issues": []
}

Interpreting Results

Metric	Healthy	Action if Unhealthy
total_insights	> 0	Run helix to build memories
with_feedback	> 0	Ensure feedback() called after builds
effectiveness	> 0.5	Prune low performers
issues	empty	Address listed issues

Maintenance Commands

bash

HELIX="$(cat .helix/plugin_root)"

# Decay dormant insights toward neutral (0.5) effectiveness
python3 "$HELIX/lib/memory/core.py" decay --days 30

# Prune insights that consistently fail (effectiveness < 0.25 with 3+ uses)
python3 "$HELIX/lib/memory/core.py" prune --threshold 0.25 --min-uses 3

Feedback Mechanics

When feedback(names, outcome) is called:

•outcome="delivered" → effectiveness moves toward 1.0
•outcome="blocked" → effectiveness moves toward 0.0
•Uses EMA update: new_eff = old_eff * 0.9 + outcome * 0.1
•use_count increments, last_used updates

Database Location

bash

# Default location
.helix/helix.db

# Inspect directly
sqlite3 .helix/helix.db "SELECT name, effectiveness, use_count FROM insight ORDER BY effectiveness DESC LIMIT 10"