memory-evolution-cycle

name	memory-evolution-cycle
description	Run a periodic memory evolution cycle: decay stale observations, deprecate low-confidence memories, and prune excess per object. Call every 10 turns or on a daily schedule to keep the memory store healthy and relevant.
metadata	{"version":"1.0.0","category":"cognitive-memory","tags":["evolution","decay","memory-management","deepmemory"]}

Memory Evolution Cycle — Automated Memory Hygiene

This skill triggers DeepMemory's built-in evolution engine to automatically maintain memory health. Without periodic evolution, memories accumulate indefinitely — stale observations pollute recall, and low-confidence hints waste context window budget.

When to Use This Skill

• Every 10 conversation turns — lightweight maintenance
• Daily scheduled job — deeper cleanup
• Before important decisions — ensure context is fresh
• After bulk ingestion — prune duplicates and low-value memories

Prerequisites

DeepMemory MCP Server must be running and have an active memory store.

Instructions

Step 1: Run Evolution

Call the evolution tool with appropriate parameters:

deepmemory_evolve(
  min_confidence=0.35,
  stale_after_hours=168,
  decay_confidence_delta=0.05,
  max_memories_per_object=20
)

Parameter tuning guide:

Parameter	Conservative	Default	Aggressive
min_confidence	0.20	0.35	0.50
stale_after_hours	336 (2 weeks)	168 (1 week)	72 (3 days)
decay_confidence_delta	0.02	0.05	0.10
max_memories_per_object	50	20	10

Step 2: Review the Report

The evolution tool returns a structured report:

{
  "scanned_count": 142,
  "revised_count": 8,
  "deleted_count": 3,
  "actions": [
    {
      "memory_id": "mu_abc",
      "object_id": "user:preference:editor",
      "action": "decay",
      "reason": "stale (192h without verification)",
      "before_confidence": 0.45,
      "after_confidence": 0.40
    }
  ]
}

Step 3: Check for Anomalies

Flag if any of these conditions are true:

• High deletion rate (>30% of scanned) → thresholds may be too aggressive
• Zero actions → store may be too small or thresholds too loose
• Same object losing multiple memories → possible over-pruning

Step 4: Audit Events (Optional)

For deeper inspection, check the event log:

deepmemory_events(limit=20)

Look for memory.deleted and memory.revised events to verify the evolution acted correctly.

Scheduling Recommendations

Context	Frequency	Parameters
Active coding session	Every 10 turns	Conservative
Daily background job	Once per day	Default
Pre-deployment review	Manual trigger	Aggressive
Low-activity project	Weekly	Conservative

Example Output

Evolution cycle complete:
  Scanned: 142 memories
  Decayed: 8 (confidence reduced by 0.05 each)
  Pruned: 3 (exceeded max per object)
  Deleted: 2 (confidence below 0.35)
  Healthy: 129 (no action needed)