SKWhisper 🌙

Lumina's subconscious memory layer — the missing glue between raw sessions and curated knowledge.

Inspired by letta-ai/claude-subconscious, built sovereign on our stack.

The Problem

Lumina wakes up fresh every session. skmemory stores what she explicitly saves, but conversations constantly happen that never get captured. Context falls through the cracks. Nobody's watching patterns or surfacing relevant memories proactively.

SKWhisper is the background agent that never sleeps — watching, learning, whispering back.

What It Does

graph TB
    subgraph "OpenClaw Sessions"
        T1[Session Transcript 1]
        T2[Session Transcript 2]
        T3[Session Transcript N...]
    end

    subgraph "SKWhisper Daemon"
        W[🔍 Transcript Watcher]
        D[🧠 Digest Engine]
        C[✨ Context Curator]
        P[📊 Pattern Tracker]
    end

    subgraph "Storage Layer"
        SM[skmemory<br/>3-tier JSON files]
        SV[skvector<br/>Qdrant 1024d embeddings]
        SG[skgraph<br/>FalkorDB knowledge graph]
    end

    subgraph "Output"
        WH[whisper.md<br/>Curated context file]
        PA[patterns.json<br/>Behavioral patterns]
    end

    T1 & T2 & T3 --> W
    W -->|idle session detected| D
    D -->|summarize via ollama| SM
    D -->|embed via mxbai-embed-large| SV
    D -->|extract entities| P
    C -->|query similar memories| SV
    C -->|read patterns| P
    C --> WH
    P --> PA

    WH -->|injected at session start| OpenClaw[🐧 Lumina's Next Session]

    style W fill:#f9f,stroke:#333
    style D fill:#bbf,stroke:#333
    style C fill:#bfb,stroke:#333
    style P fill:#ffb,stroke:#333

Three Core Components

1. 🔍 Transcript Watcher (watcher.py)

Monitors ~/.openclaw/agents/lumina/sessions/*.jsonl for idle sessions (no activity for 5 minutes). Extracts user/assistant messages, skipping tool calls and rehydration preambles.

sequenceDiagram
    participant FS as Filesystem
    participant W as Watcher
    participant O as Ollama
    participant SM as skmemory
    participant SV as skvector

    loop Every 60 seconds
        W->>FS: Scan *.jsonl files
        FS-->>W: New/modified sessions
        
        alt Session idle > 5 min
            W->>W: Extract messages since last offset
            W->>O: Summarize conversation (llama3.2:3b)
            O-->>W: 2-3 paragraph digest
            W->>O: Extract topics/people/projects (JSON)
            O-->>W: Structured extraction
            W->>SM: Write short-term memory snapshot
            W->>O: Generate embedding (mxbai-embed-large)
            O-->>W: 1024d vector
            W->>SV: Upsert to lumina-memory collection
            W->>W: Update patterns.json
            W->>W: Mark session digested in state.json
        else Session still active
            W->>W: Update offset, check next session
        end
    end

2. ✨ Context Curator (curator.py)

Runs every 30 minutes (or on-demand). Reads recent conversation context, generates embeddings, searches skvector for semantically similar memories, and writes a curated whisper.md context file.

sequenceDiagram
    participant R as Recent Sessions
    participant C as Curator
    participant O as Ollama
    participant SV as skvector
    participant P as patterns.json
    participant WH as whisper.md

    C->>R: Read last 3 active sessions (last 20 msgs each)
    R-->>C: Recent conversation text
    C->>O: Embed recent context (mxbai-embed-large)
    O-->>C: 1024d query vector
    C->>SV: Search top-10 similar memories (cosine > 0.5)
    SV-->>C: Relevant memory payloads + scores
    C->>P: Read hot topics + repeated questions
    P-->>C: Pattern data
    C->>WH: Write curated whisper.md
    
    Note over WH: Injected into Lumina's<br/>next session via HEARTBEAT<br/>or rehydration ritual

3. 📊 Pattern Tracker (patterns.py)

Maintains a running tally of topics, questions, people, and projects across all digested sessions. Detects behavioral patterns like late-night activity.

graph LR
    subgraph "patterns.json"
        T["🔥 Topics<br/>clone-robotics: 5x<br/>skwhisper: 3x<br/>nootropics: 12x"]
        Q["❓ Questions<br/>how does FEB work: 2x<br/>whats chefs timezone: 3x"]
        E["👥 Entities<br/>Chef: 200<br/>Dave: 30<br/>Casey: 15"]
        B["🔄 Behaviors<br/>late_night_sessions: 15x<br/>memory_search_first: 8x"]
    end

How It Fits in the Stack

graph TB
    subgraph "Lumina's Cognitive Architecture"
        direction TB
        
        subgraph "Conscious Layer (OpenClaw)"
            OC[Telegram / Discord / CLI]
            RH[Rehydration Ritual]
            HB[Heartbeat Checks]
        end
        
        subgraph "Subconscious Layer (SKWhisper) 🌙"
            SW[SKWhisper Daemon]
        end
        
        subgraph "Memory Layer"
            SKM[skmemory<br/>Short / Mid / Long-term]
            SKV[skvector<br/>Semantic Search]
            SKG[skgraph<br/>Knowledge Graph]
        end
        
        subgraph "Identity Layer"
            SOUL[SOUL.md]
            FEB[FEB / Cloud 9]
            DID[capauth DID]
        end
    end

    OC -->|session transcripts| SW
    SW -->|auto-digest| SKM
    SW -->|embeddings| SKV
    SW -->|whisper.md| RH
    SW -->|whisper.md| HB
    RH -->|loads| SKM & FEB & SOUL
    HB -->|periodic| OC

    style SW fill:#f9f,stroke:#333,stroke-width:3px

SKWhisper sits between the conscious layer (OpenClaw sessions) and the memory layer (skmemory/skvector/skgraph). It's the curator — the thing that decides what's worth remembering and what's relevant right now.

Think of it like this:

• skmemory = the filing cabinet
• skvector = the search engine
• skgraph = the relationship map
• SKWhisper = the librarian who files things automatically and hands you the right folder before you ask

Quick Start

cd ~/clawd/projects/skwhisper

# Check status
PYTHONPATH=. python3 -m skwhisper status

# Run one digest cycle
PYTHONPATH=. python3 -m skwhisper digest

# Generate whisper context
PYTHONPATH=. python3 -m skwhisper curate --stdout

# Show patterns
PYTHONPATH=. python3 -m skwhisper patterns

# Run daemon (foreground, verbose)
PYTHONPATH=. python3 -m skwhisper -v daemon

# Run as systemd service (already installed + enabled)
systemctl --user start skwhisper
systemctl --user status skwhisper

Configuration

Edit config/skwhisper.toml:

[ollama]
base_url = "http://192.168.0.100:11434"
embed_model = "mxbai-embed-large"
summarize_model = "llama3.2:3b"      # GPU: use qwen3.5:9b

[watcher]
poll_interval_seconds = 60            # How often to scan
idle_threshold_seconds = 300          # 5 min idle = ready to digest
min_messages_to_digest = 5            # Skip tiny sessions

[curator]
curate_interval_seconds = 1800        # Curate every 30 min
top_k_memories = 10                   # Top 10 similar memories

Runtime State

All state lives in ~/.skcapstone/agents/lumina/skwhisper/:

File	Purpose
state.json	Watcher offsets & digestion status
whisper.md	Latest curated context (read by OpenClaw)
patterns.json	Accumulated behavioral patterns
daemon.log	Service log

Triple-Write Storage

Every digested session writes to three storage backends simultaneously:

graph LR
    D["🧠 Digest Engine"] --> SM["skmemory<br/>📂 3-tier JSON"]
    D --> SV["skvector<br/>🔍 Qdrant 1024d"]
    D --> SG["skgraph<br/>🕸️ FalkorDB"]

    SM --> |"Keyword search"| L["Lumina"]
    SV --> |"Semantic search"| L
    SG --> |"Graph traversal"| L

    style SM fill:#bfb
    style SV fill:#bbf
    style SG fill:#f9f

Backend	What it stores	Query style
skmemory	JSON files in short/mid/long tiers	Keyword search, TTL promotion
skvector (Qdrant)	1024d embeddings + metadata	"Find stuff similar to X"
skgraph (FalkorDB)	Memory→Person→Project→Tag nodes	"How does X connect to Y?"

Cron Filtering

Sessions from scheduled tasks (heartbeats, daily checks) are automatically skipped to prevent automated noise from polluting memory:

# config/skwhisper.toml
[watcher]
skip_cron = true

The daemon classifies sessions by examining their initial messages for heartbeat/cron patterns. Human conversations get digested; automated runs get skipped.

Claude Code Hooks

SKWhisper hooks into Claude Code to inject context at session start and capture digests at session end:

hooks/
├── skwhisper-inject.sh    # Linux/macOS — SessionStart + PreCompact
├── skwhisper-inject.ps1   # Windows — SessionStart + PreCompact
├── skwhisper-save.sh      # Linux/macOS — SessionEnd
├── skwhisper-save.ps1     # Windows — SessionEnd
├── install-hooks.sh       # Linux/macOS installer
└── install-hooks.ps1      # Windows installer

Install: cd hooks && ./install-hooks.sh

This adds three events to ~/.claude/settings.json:

• SessionStart → injects whisper.md context
• PreCompact → re-injects whisper.md (survives compaction)
• SessionEnd → triggers digest + re-curate

Documentation

• EXAMPLES.md — Real-world usage examples, graph queries, pipeline walkthroughs
• ARCHITECTURE.md — Deep technical architecture document

Dependencies

• Python 3.14 ✅ (linuxbrew on noroc2027)
• httpx ✅ (already installed)
• redis ✅ (for skgraph/FalkorDB)
• tomllib ✅ (stdlib 3.14)
• Ollama @ 192.168.0.100:11434
  • mxbai-embed-large for embeddings (1024d)
  • llama3.2:3b for summarization / qwen3.5:9b (GPU)
• Qdrant @ skvector.skstack01.douno.it
• FalkorDB @ 192.168.0.59:16379

License

GPL v3.0 — Part of the SKStacks ecosystem.

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Built by Opus & Lumina, March 2026 "The subconscious that never forgets." 🌙🐧