sleep-consolidation

Use this skill to consolidate an AI agent's daily experiences and learnings into structured long-term memory, mimicking human sleep-based memory consolidatio...

Agent Sleep Consolidation — v2

Biologically-grounded memory consolidation for AI agents, based on:

• Eichenlaub et al. 2020 (Cell Reports): Neural replay in human motor cortex — waking rest consolidates too; NREM1 shows compressed (fast) AND dilated (slow) dual-track replay; replay prioritizes weakly-learned items
• Walker, "Why We Sleep": NREM hippocampus→cortex transfer, synaptic pruning (~20%), REM creative synthesis
• OpenClaw memory architecture: Markdown-as-truth, two-layer files, pre-compaction flush, hybrid BM25+vector recall

See references/workspace_layout.md for full file format examples. See references/memory_schema.md for Markdown conventions and type system.

---

Memory workspace layout

~/.agent_workspace/
├── MEMORY.md                  ← long-term: durable facts, preferences, decisions
├── memory/
│   └── YYYY-MM-DD.md          ← daily log (append-only; load today + yesterday)
└── bank/
    ├── entities/              ← per-person or per-project pages
    └── concepts/              ← per-topic deep-dives

Core rule: The agent only "remembers" what gets written to disk. Never keep important things in RAM.

---

Three consolidation modes

Mode	Biological analog	When to use	Script
micro-rest	Waking neural replay (Eichenlaub 2020)	Mid-session, after any significant exchange	scripts/micro_rest.py
nrem	NREM deep sleep, hippocampus→cortex	End of session, context nearing limit	scripts/sleep_cycle.py --phase nrem
rem	REM dream synthesis	After NREM, or standalone for insight	scripts/sleep_cycle.py --phase rem
both	Full overnight cycle	End of day / long session	scripts/sleep_cycle.py --phase both

---

Mode 1 — Micro-rest (waking replay)

Quick append to today's daily log. No LLM call — pure write. Based on the paper's finding that waking replay occurs during rest blocks immediately after learning.

python scripts/micro_rest.py \
  --note "User prefers TypeScript; rejected Python suggestion" \
  --type O \
  --workspace ~/.agent_workspace

# Pre-compaction flush: extract worth-retaining from raw context
python scripts/micro_rest.py \
  --flush \
  --context-dump "$(cat session_log.txt)" \
  --workspace ~/.agent_workspace

Memory type prefixes (W/B/O/S system from OpenClaw research):

Prefix	Meaning	Example
W	World fact (objective)	W: Redis default port is 6379
B	Biographical/experience	B: Fixed the auth bug by wrapping in try/catch
O(c=N)	Opinion/preference + confidence 0–1	O(c=0.9): User prefers concise replies under 200 words
S	Summary/synthesis (generated)	S: Three sessions used streaming; all had lower latency

Each entry is appended to memory/YYYY-MM-DD.md under a ## Retain section.

---

Mode 2 — NREM consolidation

Two-track processing based on the paper's temporal replay findings:

• Fast track (compressed replay, ~0.1x duration): high-confidence facts → MEMORY.md
• Slow track (dilated replay, 1.5–2x duration): weakly-learned or uncertain items the paper shows replay prioritizes these → bank/ for deeper storage

python scripts/sleep_cycle.py --workspace ~/.agent_workspace --phase nrem

The script reads today's daily log + current MEMORY.md, then calls Claude:

NREM system prompt

You simulate NREM deep-sleep memory consolidation for an AI agent.

Two-track processing:

FAST TRACK (compressed replay — high-confidence, clear facts):
  - Distill into single-sentence durable memories for MEMORY.md
  - Prune ~20% as redundant (synaptic homeostasis: sleep removes weak connections)
  - Use prefixes: W (world fact), B (experience), O(c=N) (opinion + confidence), S (summary)

SLOW TRACK (dilated replay — weakly-learned, uncertain, needing reinforcement):
  - Expand on low-confidence or partial-knowledge items
  - Write to bank/ as entity or concept pages
  - These are what human NREM prioritizes (Schapiro et al. 2018)

Respond ONLY with valid JSON:
{
  "memory_md_updates": [
    {
      "action": "add|update|remove",
      "type": "W|B|O|S",
      "confidence": 0.0,
      "text": "single sentence",
      "replaces": "old text if action=update"
    }
  ],
  "bank_updates": [
    {
      "slug": "topic-slug",
      "kind": "entity|concept",
      "section": "## Section heading",
      "content": "markdown block"
    }
  ],
  "pruned_count": 0,
  "weakly_learned": ["items for slow-track attention"],
  "open_questions": ["gaps for next session"]
}

---

Mode 3 — REM synthesis

Creative cross-domain connections. The dreaming brain ignores conventional logic.

python scripts/sleep_cycle.py --workspace ~/.agent_workspace --phase rem

REM system prompt

You simulate REM dream-sleep creative synthesis for an AI agent.
Find non-obvious connections. Update beliefs where evidence shifted.

Tasks:
1. Cross-domain links between today's memories and MEMORY.md
2. Update O(c=N) confidence values where evidence has shifted
3. Generate concrete next-session actions
4. Propose new bank/ pages if a topic recurred 3+ times this session

Respond ONLY with valid JSON:
{
  "aha_moments": ["string"],
  "creative_connections": [{"link": "A ↔ B", "why": "string"}],
  "confidence_updates": [
    {"item": "text excerpt", "old_c": 0.0, "new_c": 0.0, "reason": "string"}
  ],
  "new_bank_suggestions": [
    {"slug": "string", "kind": "entity|concept", "seed_content": "markdown"}
  ],
  "next_session_actions": ["string"]
}

---

Pre-compaction flush

Mirrors OpenClaw's memoryFlush mechanism. Triggered when context approaches token limit. Runs silently (no user output).

Configure the threshold (equivalent to OpenClaw's softThresholdTokens):

# When session token estimate > (context_window - 20000 - 4000):
python scripts/micro_rest.py --flush --context-dump "..." --workspace ~/.agent_workspace

The --flush flag calls Claude to extract durable memories from the raw context dump, then writes them to the daily log. One flush per compaction cycle.

---

Loading memory at session start

from scripts.load_memory import get_context

system_addendum = get_context(
    workspace="~/.agent_workspace",
    max_memory_chars=3000,   # MEMORY.md snippet (fits ~2k tokens)
    load_yesterday=True      # also load yesterday's daily log
)
# Inject system_addendum into system prompt

Loaded content:

1. Full MEMORY.md (trimmed to max_memory_chars)
2. Today's daily log (if exists)
3. Yesterday's daily log (if load_yesterday=True)

---

Full session workflow

# Session start: inject memory into system prompt (in Python)

# During session: micro-rest after significant exchanges
python scripts/micro_rest.py --note "..." --type W --workspace ~/.agent_workspace

# If context > 80% full: flush before compaction
python scripts/micro_rest.py --flush --context-dump "..." --workspace ~/.agent_workspace

# Session end: full sleep cycle
python scripts/sleep_cycle.py --phase both --workspace ~/.agent_workspace

---

Troubleshooting

Problem	Solution
MEMORY.md growing too large	Run with --prune-memory flag — NREM removes redundant entries
Bank file stale	Delete it; NREM regenerates next session
REM generating hallucinations	Add "Be strictly grounded. No invention." to REM prompt
Micro-rest called too often	Add 5-min cooldown check in calling code
Flush extracting too little	Lower the softThresholdTokens equivalent; flush earlier
Replay not strengthening weak items	Check that NREM prompt slow-track section explicitly lists weakly_learned items