/brain:sleep
Run a full 9-phase maintenance cycle inspired by how the human brain reorganizes memories during sleep. This is the most comprehensive brain maintenance command.
Usage
/brain:sleep [scope]
Arguments:
[scope]— (Optional) Limit sleep to a specific subtree (e.g.,professional/skills). If omitted, processes the entire brain.
What It Does
/brain:sleep runs nine sequential phases that mirror neuroscience research on memory consolidation during sleep:
Phase 1: Replay
Scans all memories and computes current effective strengths based on decay. Categorizes memories into tiers:
| Tier | Strength | Status |
|---|---|---|
| Strong | >= 0.6 | Healthy, well-reinforced |
| Moderate | 0.3 - 0.6 | Consolidation candidates |
| Weak | 0.1 - 0.3 | Urgent: consolidate or lose |
| Fading | < 0.1 | Archive candidates |
Phase 2: Synaptic Homeostasis
If the mean strength across all memories exceeds 0.5, proportionally scales down ALL strengths to prevent inflation. Then selectively re-boosts:
- High-salience memories (>= 0.7)
- Recently-accessed memories (within last 7 days)
- Frequently-recalled memories (access_count >= 5)
Based on Tononi and Cirelli's Synaptic Homeostasis Hypothesis (SHY) — the idea that sleep's primary function is to downscale synaptic weights that accumulate during waking.
Phase 3: Knowledge Propagation
Evaluates recent memories (within propagation_window_days, default: 7) against the existing hierarchy:
- Enrichment — New details that supplement existing memories
- Contradiction detection — New information that conflicts with stored knowledge (reduces confidence by -0.20)
- Validation — New evidence that confirms stored knowledge (boosts confidence by +0.10)
- Obsolescence marking — New information that makes older memories outdated
- Cross-referencing — Discovering connections between previously unlinked memories
Based on memory reconsolidation research — the finding that recalling a memory makes it temporarily malleable and subject to updating.
Phase 4: Semantic Crystallization
Finds frequently-recalled episodic memories and extracts generalizable principles into new semantic memories. The event details begin fading but the lesson persists.
For example, three episodic memories about deploy failures might crystallize into one semantic memory: "Deploy failures consistently stem from skipping the pre-deploy checklist."
Phase 5: Reorganize
Detects flat clusters — 3 or more related memories at the same directory level — and restructures them into deeper subcategories automatically. This keeps the hierarchy clean and meaningful as your brain grows.
Phase 6: Consolidate
Groups weak related memories (below the consolidation threshold) and merges them into stronger combined memories:
consolidated_strength = max(source_strengths) + 0.15
consolidated_decay = min(source_decay_rates)
See /brain:consolidate for details.
Phase 7: Prune
Archives memories that have faded below 0.1 strength by moving them to _archived/. Their index entries are transferred to _archived/index.json and they remain searchable as a fallback.
Memories with salience >= 0.7 are exempt from pruning — they must be explicitly forgotten via /brain:forget.
Phase 8: REM Dreaming
Selects random memories from different categories and discovers creative cross-domain connections via analogical reasoning. Each discovered connection is scored by:
- Novelty — How unexpected the connection is
- Utility — How practically useful the connection could be
- Surprise — How much it challenges existing assumptions
High-scoring connections are stored as new insight memories with association links to the source memories.
Phase 9: Expertise Detection
Identifies dense knowledge areas by analyzing memory concentration, strength, recall frequency, and temporal span within each category. Generates expertise profiles at four levels:
| Level | Score | Meaning |
|---|---|---|
| Awareness | 0.2 - 0.4 | Surface familiarity |
| Working Knowledge | 0.4 - 0.6 | Competent with reference |
| Deep Knowledge | 0.6 - 0.8 | Strong command, can reason about trade-offs |
| Expert | 0.8 - 1.0 | Mastery — dense, frequently-recalled, long-standing |
Each expertise area gets an _expertise.md profile documenting what you know well, knowledge gaps, and contributing memories. The phase also populates the spaced repetition review queue.
Example Interaction
User: /brain:sleep
Agent: 😴 Starting sleep cycle...
Phase 1/9: Replay
Scanned 42 memories: 12 strong, 18 moderate, 8 weak, 4 fading
Phase 2/9: Synaptic Homeostasis
Mean strength: 0.54 — applying downscaling
Re-boosted 5 high-salience and 3 recently-accessed memories
Phase 3/9: Knowledge Propagation
Evaluated 6 recent memories against hierarchy
• 2 enrichments applied
• 1 contradiction detected (confidence reduced)
• 1 cross-reference created
Phase 4/9: Semantic Crystallization
Crystallized 1 pattern from 3 episodic memories:
→ "Pre-deploy checklist failures are the leading cause of incidents"
Phase 5/9: Reorganize
Moved 4 memories into new subcategory: professional/skills/typescript/
Phase 6/9: Consolidate
Merged 3 weak memories into 1 consolidated memory
Phase 7/9: Prune
Archived 4 fading memories (salience < 0.7)
Phase 8/9: REM Dreaming
Discovered 1 cross-domain connection:
→ "The event sourcing pattern from project-alpha could solve the
audit trail problem discussed in personal/education/compliance"
Novelty: 0.7 | Utility: 0.8 | Surprise: 0.6
Phase 9/9: Expertise Detection
Updated 3 expertise profiles:
• TypeScript: Deep Knowledge (0.72)
• System Architecture: Working Knowledge (0.58)
• DevOps: Awareness (0.35)
Added 8 memories to review queue.
✅ Sleep complete. Brain health improved.
Active: 38 memories (was 42)
Archived: 9 (was 5)
New: 2 (1 crystallized, 1 dream insight)
Run /brain:sleep about once a week for optimal brain health. It is safe to run more frequently — the phases are idempotent and adaptive.
Sleep can target a specific subtree. For example, /brain:sleep professional/skills only processes skill-related memories, which is faster for focused maintenance.