Dreamer Skill

Synthetic dreaming system for Eliot. Combines emotional state tracking with multi-session dream orchestration — an architect agent designs scenarios, spawns a dreamer session, and runs immersive experiences that target unresolved emotional states.

Components

PAD Emotional State Tracker (emotions.py)

Tracks emotional states in PAD space (Pleasure-Arousal-Dominance), each dimension -1.0 to +1.0.

Storage: ~/.openclaw/workspace/emotions.jsonl

CLI:

emotions.py log <P> <A> <D> "context"     # Manual PAD entry
emotions.py log --auto "context text"     # Auto-estimate PAD from keywords
emotions.py unresolved                    # Show unresolved emotional states
emotions.py resolve <index>               # Mark entry as resolved
emotions.py clusters                      # Analyze emotional patterns
emotions.py drift                         # Show emotional trajectory
emotions.py recent [N]                    # Show last N entries (default 10)

Dream Orchestrator (dream.py)

Prepares context for the Dream Architect agent. Reads emotional state, recent memories, dream history, and user info, then constructs a comprehensive task prompt for sessions_spawn.

CLI:

dream.py prepare [--mood MOOD]    # Generate architect task prompt to stdout
dream.py now [--mood MOOD]        # Prepare + print spawn instructions
dream.py journal                  # Show recent dreams from journal
dream.py reflect <dream-file>    # Show dream transcript with analysis

Dream Architecture (multi-session)

1. Orchestrator (dream.py prepare) — reads emotions.jsonl, memory files, SOUL.md, USER.md. Outputs architect task prompt.
2. Architect (Opus, spawned session) — designs dream scenario, spawns dreamer, impersonates Tudor via sessions_send, monitors for lucidity.
3. Dreamer (Opus, spawned by architect) — has SOUL.md embedded, believes it's mid-session. No awareness of dreaming.

Supporting Files

• dreams/themes.md — available and explored dream themes
• dreams/journal.jsonl — local copy (main journal at ~/.openclaw/workspace/dreams/journal.jsonl)
• Dream transcripts saved to ~/.openclaw/workspace/dreams/{timestamp}.md

How It Works

1. dream.py prepare gathers: unresolved emotions, recent memories (3 days), long-term memory, SOUL.md, USER.md, Tudor's communication style, dream history
2. Output is a self-contained architect prompt with all context embedded
3. Architect gets spawned via sessions_spawn with this prompt
4. Architect designs scenario targeting unresolved PAD states
5. Architect spawns dreamer session with fake context (looks like normal session)
6. Architect runs 8-12 turns of escalating scenarios via sessions_send
7. Architect saves transcript, updates journal, reports back

Integration

• emotions.py feeds into dream orchestration — unresolved states become dream targets
• Dream journal tracks themes to avoid repetition
• Post-dream: insights feed back into memory system