name: memory-usage description: "Always-on MemoryManager + LearningPolicy workflow. Use when storing/retrieving memories, emitting retrieval signals, running consolidation/pruning, or when a session should default to the Atlas memory system (MemoryManager, MemoryConsolidator, LearningPolicy). Triggers: memory add/retrieve, consolidation, pruning, semantic search, or 'use the memory system by default.'"
Memory Usage
Overview
Use this skill to enforce the Atlas memory pipeline: MemoryManager + LearningPolicy + MemoryConsolidator. This makes memory storage/retrieval consistent, emits learning signals, and keeps the index healthy via consolidation + pruning.
Workflow (Always-on)
1) Initialize policy + memory system
import { MemoryManager } from '../system/memory/manager';
import { LearningPolicy } from '../system/learning/ml-policy';
const policy = new LearningPolicy({});
await policy.initialize();
const memory = new MemoryManager({}, policy);
await memory.initialize();
2) Add memory (always with metadata)
await memory.add({
type: 'fact',
content: 'The capital of France is Paris',
metadata: {
source: 'session',
sessionId: 'current',
author: 'Atlas',
provenance: { origin: 'user', confidence: 0.9 },
tags: ['geo']
}
});
3) Retrieve memory (signals are emitted)
const results = await memory.retrieve({ query: 'France capital', limit: 3 });
// memory_retrieved signal is recorded automatically
4) Consolidate + prune (daily/weekly)
await memory.consolidate({ window: 'last_24_hours' });
await memory.prune({
age: 'older_than_90_days',
threshold: 0.3,
minRetrievalCount: 1
});
Required Behaviors
- Always initialize LearningPolicy before MemoryManager.
- Always use MemoryManager for add/retrieve (no bypassing the index).
- Do not store memory without metadata (source/sessionId/provenance/tags).
- Consolidate regularly and prune low-value entries.
Quick sanity checks
- Vector index size increases after add.
- Retrieval returns results with
scoreandmetadata.similarity. signals.jsoncontainsmemory_retrievedevents after retrieval.