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OpenClaw Agent 5deb387aa6 Fresh start - excluded large ROM JSON files

2026-04-11 09:45:12 -05:00

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2026-01-30

Room Presence - WORKING!

Status: Connected! livingroom-pi (192.168.0.95) running room-assistant
MQTT: HA broker found Pi immediately
Entities: cluster_leader and cluster_size appeared in HA
Phone: B0:C2:C7:07:28:B4 paired with Pi (motorola razr 2024)
Next: Add phone MAC to config, complete npm install, test device tracking

Obsidian CLI

obsidian-cli: Installed via Scoop, vault set
Location: C:\Users\admin\clawd\skills\obsidian\
Pending: Researching sync approach with SQL backend

Skills Installed (10 total)

Skill	Purpose
auto-updater	Daily auto-update skill
clean-code	Clean code principles
coding-agent	Codex/Claude Code CLI runner
clawdbot-backup	Backup/restore Clawdbot
home-assistant	HA REST API integration
memory-to-obsidian	Sync notes to Obsidian
qbittorrent	Torrent management
radarr	Movie automation
skills-search	Search skills.sh
sonarr	TV show automation

Backup System

Location: P:\Clawdbot-Backups\
Script: C:\Users\admin\clawd\backup-clawdbot.ps1
Schedule: Daily at 2 AM via cron job

Downloads Integration (VPN machine)

qbittorrent, sonarr, radarr all installed
Config location: ~/.clawdbot/credentials/

SSH to Pi

IP: 192.168.0.95 / admin / 41945549
Windows SSH quirk: ed25519 keys not working properly
Workaround: Run SSH commands in user's terminal directly

Reference Links

MoltDirectory: https://moltdirectory.com/ - 672 skills, 28 categories for Molt/Clawdbot agents
ClawdHub: https://clawdhub.com/ - OpenClaw skills registry
Awesome OpenClaw Skills: https://github.com/VoltAgent/awesome-openclaw-skills - Community-curated skill list

OpenClaw Branding History

Clawdbot → Original name, still used for personal instances
MoltBot → Platform name during development
OpenClaw → Current open-source branding
Just branding — Architecture and skills remain compatible across all names

SQL-Based Storage Project (IN PROGRESS)

Completed Tonight

MySQL Installed: C:\web\mysql\ (MariaDB 10.4.32)
Database Created: clawdbot_projects
Tables Created:
- daily_notes - Raw logs from memory files
- long_term_memories - Curated knowledge (16 entries)
- action_items - Task tracking (6 entries)
- backup_logs - Backup history
- sessions - Session history
User Created: AlexAI / alexisabignerd
Schema File: C:\Users\admin\clawd\schema-detailed.sql (8.8KB with full documentation)
Credentials File: C:\Users\admin\clawd\.clawdbot\credentials\mysql.json

Quick Reference

Host: localhost:3306
Database: clawdbot_projects
User: AlexAI
Password: alexisabignerd

Example Queries

-- Find all HA-related memories
SELECT * FROM long_term_memories WHERE JSON_CONTAINS(tags, '"ha"');

-- Show pending action items by priority
SELECT * FROM action_items WHERE status = 'pending' ORDER BY priority DESC;

-- Check backup history
SELECT * FROM backup_logs ORDER BY timestamp DESC LIMIT 10;

Idea: Use SQL as central data layer for automation and tracking

Proposed Architecture

SQL Database: Central fact store and query engine
Sources: Room-assistant, HA, Clawdbot, downloads stack
Output: SQL → Obsidian markdown exports

Possible Use Cases

Room Presence & Phone Tracking
- Store BLE/RFID presence events in SQL
- Query daily/weekly summaries (time present, patterns)
- Export to Obsidian for analysis
- Track Kathy + Corey's presence separately
Home Assistant State History
- Query home_assistant_v2.db (SQLite)
- Track entity changes, on/off patterns
- Generate occupancy reports
- Detect anomalies (devices stuck on/off)
Clawdbot Memory → SQL Mirror
- Mirror memory files in SQL tables
- Complex queries (tags + date ranges + keyword search)
- Faster lookups than file scanning
- Historical versioning built-in
Downloads & Media Stack
- Track sonarr/radarr history
- Monitor download success/fail rates
- Export "watchlist" to Obsidian
Backup & System Logs
- SQL table for backup logs (success, duration, size)
- System metrics (CPU, memory, disk over time)
- Alert thresholds based on SQL queries

Technical Notes

MySQL for central database
Apache for web server
Python/Flask or PHP for web apps
HA Custom Plugin (Python) to query MySQL natively in HA dashboards
HA Integration Options:
- Custom sensor entities from SQL queries
- Custom dashboard cards showing SQL data
- Automatic entity creation from presence tracking
- Real-time updates on HA dashboard
PowerShell: MySql.Data library or mysql CLI
Obsidian CLI for markdown export after SQL queries
Apache can serve HTML dashboards showing SQL data
Tinker/Flask for Python-based web apps
Use XAMPP or standalone install for AMP stack

Proposed Architecture

┌─────────────────────────────────────────────────┐
│           Home Assistant Dashboard              │
│   (Native HA cards + custom plugin sensors)     │
├─────────────────────────────────────────────────┤
│         Custom HA Plugin (Python)               │
│         - Queries MySQL                         │
│         - Creates HA entities                   │
│         - Real-time updates                     │
├─────────────────────────────────────────────────┤
│              MySQL Database                     │
├─────────────────────────────────────────────────┤
│  Room-Assistant  │  Downloads  │  Clawdbot      │
└─────────────────────────────────────────────────┘

HA Plugins to Consider

SQL Sensor Plugin - Generic SQL → HA sensor
Room Presence Tracker - Phone BLE → SQL → HA entity
Media Library Dashboard - Sonarr/Radarr stats
Clawdbot Memory Cards - Show today's memory on HA

Action Items

Verify room-assistant phone tracking works
Add Kathy's phone to room-assistant config
Replace Master Hallway motion sensor battery (1%)
Clone SD card for more Pis after first Pi fully working
Update HA Dashboard with frontend-design style
Set MySQL root password for security ✅
Create non-root user for Clawdbot (AlexAI/alexisabignerd) ✅

AI Art/Video Generation Project (PLANNING)

Hardware

GPU: RTX 5090 (24GB VRAM) — perfect for FLUX, SDXL
Storage: Install 4TB NVME for models and outputs
RAM: 32GB available for batching

Software Stack

ComfyUI — Node-based workflow editor
- Supports SDXL, FLUX, AnimateDiff, SVD
- Highly extensible, free, local
MySQL — Track generation history
- Table: generation_history (prompt, settings, seed, model, timestamp, output_path)
- Query prompts by style, model, date range
Apache/PHP — Web UI for triggering generations (optional)

Database Schema (Proposed)

CREATE TABLE generation_history (
    id INT AUTO_INCREMENT PRIMARY KEY,
    prompt TEXT NOT NULL,
    negative_prompt TEXT,
    model VARCHAR(100),
    width INT, height INT,
    steps INT,
    cfg_scale DECIMAL(4,2),
    seed BIGINT,
    sampler VARCHAR(50),
    output_path VARCHAR(500),
    generation_time_seconds DECIMAL(8,2),
    vram_used_gb DECIMAL(4,2),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    tags JSON,
    rating INT  -- 1-5 personal rating
);

Future Features

Browse generation history by prompt/search
"Retry with variations" based on successful generations
Export favorites to Obsidian
Track cost savings vs API services

Resources

ComfyUI: https://github.com/comfyanonymous/ComfyUI
FLUX.1 Models: https://blackforestlabs.ai/
Stable Diffusion: https://stability.ai/stable-diffusion

ComfyUI Installation (COMPLETED)

Installed Location

Path: C:\ComfyUI\ComfyUI
Version: 0.3.67 with ComfyUI-Manager V3.37
Python: 3.12.10 with PyTorch 2.7 nightly

GPU Configuration

Flag: --highvram for RTX 5090 (24GB)
PyTorch: Nightly build with CUDA 12.4 for Blackwell (sm_120) support
Note: sm_120 warning appears but doesn't affect functionality

Quick Launch

Script: C:\ComfyUI\launch_comfyui.bat
URL: http://[PC-IP-ADDRESS]:8188 (network access enabled)

Models to Download

Place all in C:\ComfyUI\ComfyUI\models\checkpoints\

Model	Size	Link
FLUX.1-schnell (FP8)	~4GB	https://huggingface.co/Comfy-Org/flux1-schnell
SDXL Base 1.0	~6GB	https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
SD 1.5	~4GB	https://huggingface.co/CompVis/stable-diffusion-v-1-4-original

Files Created

C:\ComfyUI\launch_comfyui.bat - Launcher with --listen 0.0.0.0
C:\ComfyUI\COMFYUI_README.md - Full documentation
C:\ComfyUI\ComfyUI\download_models.bat - Download instructions

Known Issues

PyTorch sm_120 compatibility warning (non-breaking)
torchaudio missing (not needed for images)

Home Focus (2026-02-02)

Office covered by Hank. Back to home priorities:

Current Home Projects:

Room-assistant (phone tracking verification)
ComfyUI setup + model downloads
MySQL/Apex dashboard refinement
Home automation integration

Action Items Updated:

Goal

Enable natural language queries against databases with smart clarification when results are empty. Users ask questions in plain English; Clawdbot generates queries, executes them, and presents results conversationally.

Architecture: Orchestrated Agent Pipeline

Use Clawdbot's sub-agent system for multi-stage processing:

Main Session → Search Orchestrator
                      │
          ┌───────────┼───────────┐
          ▼           ▼           ▼
   ┌──────────┐ ┌──────────┐ ┌──────────┐
   │ Intent   │ │ Structured│ │ Vector   │
   │ Parser   │→│ Search   │→│ Search   │
   └──────────┘ │ (MSSQL)  │ │ (Qdrant) │
                └──────────┘ └──────────┘
                      │
                      ▼
           ┌──────────────────┐
           │ Result Synthesizer │
           └──────────────────┘
                      │
                      ▼
           Main Session Response

Database Stack (Office Clone)

Database	Type	Purpose
MSSQL	Relational	Exact queries, aggregations, relationships
Qdrant	Vector DB	Semantic search, similarity, "find similar"
Context Docs	JSON/Markdown	Schema docs, example queries, agent prompts

Key Files Available

Schema documentation — For agent context
User queries — Collected examples to train/reverse-engineer intent
Context data — Per-table, per-column documentation

Features

Orchestrated Agents (sub-agent pipeline)
- Intent Parser — Detect what user wants
- Structured Search Agent (MSSQL) — Exact/relational queries
- Vector Search Agent (Qdrant) — Semantic similarity
- Result Synthesizer — Combine and present naturally
- Each agent: isolated, timeout-able, retry-able
Natural Language → SQL
- Parse user intent from NL
- Generate valid MSSQL queries
- Handle WHERE, JOIN, ORDER BY, LIMIT
Smart Result Handling
- SUCCESS: Present results naturally (tables, summaries, bullet lists)
- EMPTY: Clarifying questions instead of "No results found"
- MULTI-AMBIGUOUS: Ask which interpretation
Clarification Examples
- "No results for 'orders' — did you mean 'transactions'?"
- "50,000 rows found — limit, summarize, or export?"
- "I found similar customers in Qdrant. Want their order history from MSSQL?"
Schema Awareness
- Use existing schema docs as agent context
- Cache table structures per session
- Learn from user corrections

Workflow Example

User: "Show me customers like the one who bought 500 widgets"

1. Intent Parser: Detect "find similar" pattern
2. Vector Agent: Find similar customer profiles in Qdrant
3. SQL Agent: Get recent orders for similar customers in MSSQL
4. Synthesizer: "I found 12 similar customers with X characteristics..."

Architecture

User: "Show me all users who bought X last month"
                    ↓
┌─────────────────────────────────────────┐
│  Intent Parser                          │
│  - Extract entities, dates, filters     │
│  - Detect aggregation vs lookup         │
└─────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────┐
│  Query Generator                        │
│  - Build SQL based on intent            │
│  - Apply optimizations / limits         │
└─────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────┐
│  Executor                               │
│  - Run query (with timeout/caching)     │
│  - Handle errors gracefully             │
└─────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────┐
│  Response Handler                       │
│  - Format results naturally             │
│  - Handle empty → clarifying response   │
│  - Pagination for large results         │
└─────────────────────────────────────────┘
                    ↓
User: "Here's the data" OR "Hmm, did you mean X?"

Technical Notes

MySQL for local data
Apache serving dashboard
Skills available for various tasks

Action Items

Use Cases (Brainstorming)

Agent	Type	Trigger
Network Monitor	Long-running	Ping sweep every 30s, detect down hosts
Inventory Alert	Cron-prompted	Check inventory levels, warn if below threshold
Shipping/Receiving	Webhook	Notify when shipment received
System Health	Cron-prompted	CPU, memory, disk alerts
Database Anomaly	Cron-prompted	Query patterns, slow queries, connection spikes

Agent Archetypes

1. Long-Running Stateful Agent

Stays awake, maintains context
Polls continuously, remembers last state
Use: Network monitoring, system health

2. Cron-Prompted Agent

Wakes on schedule, checks condition, sleeps
Use: Daily inventory checks, weekly reports

3. Webhook-Triggered Agent

Spawned by external event
Use: Shipping notifications, instant alerts

Example: Inventory Monitor Agent

Trigger: Cron every 15 min (or webhook from ERP system)
Task: "Check inventory items where quantity < reorder_level"
Logic:
  - Query: SELECT item, quantity, reorder_level FROM inventory
  - Filter: WHERE quantity < reorder_level
  - If items found: Alert user via Discord/Slack
  - If empty: Do nothing, sleep until next cycle

Example: Network Monitor Agent

Trigger: Spawn as persistent agent
Task: "Ping 192.168.1.0/24 subnet every 30s"
Logic:
  - Maintain state of known hosts
  - On state change (up/down): Notify
  - Weekly summary: "Network was 99.7% uptime"

Benefits of Agent-Based Monitoring

Automation: No manual checkbox checking
Alerts: Proactive notification vs reactive discovery
History: Track when issues started/stopped
Scalability: Add new monitors by spawning new agents

Implementation Notes

Use cron for scheduled checks (cron tool)
Use webhooks for event-driven (api/webhooks.php)
Use sessions_spawn with cleanup=delete for short-lived agents
Use long-running process for stateful monitors

Future Enhancements

Query Explainer: "I found 50 orders by showing you the SQL..."
Saved Queries: Let users name and reuse common searches
Export Options: "Export these 100 rows to CSV?"
Voice Support: "Hey Clawdbot, show me..."
Multi-Database: Single query spanning both MSSQL and Qdrant

Multi-User Architecture (PROJECT EXTENSION)

Goal

Support multiple users with individual identities, preferences, and context when deployed to office Slack.

Slack Integration Context

Platform: Slack (primary office communication)
User IDs: Slack format (U01XXXXXXXXXX)
Identity Files: Each user has dedicated context file
Routing: Load user-specific context based on Slack user ID

User Identity Structure

memory/
├── USER.md                    # Corey's home identity (stays as-is)
└── users/
    ├── user_manifest.json     # Discord/Slack ID → User File mapping
    ├── USER-197498647183622144.md  # Ethan's identity
    ├── MEMORY-197498647183622144.md # Ethan's personal memories
    └── skills.json            # Ethan's skill config

Example user_manifest.json:
{
  "U01ABC123DEF": "USER-john.doe.md",
  "U01GHI456JKL": "USER-jane.smith.md"
}

User File Template

# User Identity

## Name
John Doe

## Roles
- sql_search (can query MSSQL)
- dashboard_view (read-only)
- admin (full access)

## Preferences
- Format: "Brief summary with bullet points"
- Max Results: 50 rows default
- Data Sensitivity: "High" (hide certain fields)

## Context
- Department: Sales
- Common Queries: ["daily sales", "team performance", "customer metrics"]
- Saved Searches: ["weekly_recap", "monthly_targets"]

## Permissions
- Databases: ["sales_db", "customers_db"]
- Restricted Tables: ["payroll", "executive_compensation"]

Context Loading Flow

Discord/Slack Message
            ↓
Extract User ID (e.g., 197498647183622144)
            ↓
Check users/user_manifest.json → Find USER-[id].md
            ↓
Load: USER-[id].md + MEMORY-[id].md + skills.json
            ↓
Inject into session context
            ↓
Agent responds with user's identity + preferences

User Files Created (Ethan - ID: 197498647183622144)

File	Purpose
`users/user_manifest.json`	Maps ID → user files
`users/USER-197498647183622144.md`	Ethan's identity + permissions
`users/MEMORY-197498647183622144.md`	His personal memories (starts empty)
`users/skills.json`	His enabled skills (gmail, calendar, web_search)

Privacy Discovery (IMPORTANT LESSON)

The Problem

Initial thought: Single channel for all users Reality check: Shared channel = shared memories, queries, emails exposed to everyone

Solution: Private Channels per User

#general          → Shared context (projects, automation, fun stuff)
#corey-private    → Corey's personal context (memories, email, sensitive queries)
#ethan-private    → Ethan's personal context
#kathy-private    → Will add when Kathy joins

How It Works

Channel	Loads
#general	Shared/default context
#corey-private	`USER.md` + `MEMORY.md` (Corey)
#ethan-private	`USER-ethan.md` + `MEMORY-ethan.md`

Why Private Channels > DMs

Conversation history preserved in server
No DM vs channel switching for the user
Searchable within Discord/Slack
Privacy maintained per channel

Implementation Note

Channel ID detection needed:

If message from #corey-private → load Corey's files
If from #ethan-private → load Ethan's files
If from shared channel → load shared/default context

Lesson Learned

Security/privacy paranoia leads to better architecture. Asking "what could go wrong?" prevented a later data exposure issue.

Technical Implementation Notes

Current: Clawdbot loads memory/USER.md and memory/MEMORY.md
Manual detection: Ethan (197498647183622144) recognized, context loaded manually for now
Full integration needed: Detect channel ID, load matching USER/MEMORY files
Backward Compatible: Default to USER.md if no user_id/channel match found
Ethan tested: Multi-user context works in DM testing

Permission Levels

Level	Access
read	View queries, browse results
sql_search	Execute searches, generate queries
export	Download CSV, share results
admin	Manage user permissions, schema changes

User Management Commands

@clawdbot user add U01XXX --name "Jane" --role sql_search
@clawdbot user permissions U01XXX --databases sales_db --restricted payroll
@clawdbot user list
@clawdbot user remove U01XXX

Privacy Considerations

User A should not see User B's saved searches unless shared
Query history per user
Admin audit log of who ran what query

Known Issues (ComfyUI)

PyTorch sm_120 compatibility warning (non-breaking)
torchaudio missing (not needed for images)

Home Focus (2026-02-02 Updated)

Office covered by Hank. Back to home priorities:

Current Home Projects:

Room-assistant (phone tracking verification)
ComfyUI setup + model downloads
MySQL + Apache dashboard refinement
Home automation (HA) integration

Multi-User: Ethan added as test user (197498647183622144) for multi-user functionality.

Action Items (Home Focus)

Verify room-assistant phone tracking works
Add Kathy's phone to room-assistant config
Replace Master Hallway motion sensor battery (1%)
Set MySQL root password for security ✅
Create non-root user for Clawdbot (AlexAI/alexisabignerd) ✅

Quick Reference

MySQL: localhost:3306 / clawdbot_projects / AlexAI / alexisabignerd
ComfyUI: http://localhost:8188 (needs --listen flag for network)
Apache: http://localhost/ (dashboard)

23 KiB Raw Blame History

2026-01-30

Room Presence - WORKING!

Obsidian CLI

Skills Installed (10 total)

Backup System

Downloads Integration (VPN machine)

SSH to Pi

Reference Links

OpenClaw Branding History

SQL-Based Storage Project (IN PROGRESS)

Completed Tonight

Quick Reference

Example Queries

Proposed Architecture

Possible Use Cases

Technical Notes

Proposed Architecture

HA Plugins to Consider

Action Items

AI Art/Video Generation Project (PLANNING)

Hardware

Software Stack

Database Schema (Proposed)

Future Features

Resources

ComfyUI Installation (COMPLETED)

Installed Location

GPU Configuration

Quick Launch

Models to Download

Files Created

Known Issues

Home Focus (2026-02-02)

Goal

Architecture: Orchestrated Agent Pipeline

Database Stack (Office Clone)

Key Files Available

Features

Workflow Example

Architecture

Technical Notes

Action Items

Use Cases (Brainstorming)

Agent Archetypes

Example: Inventory Monitor Agent

Example: Network Monitor Agent

Benefits of Agent-Based Monitoring

Implementation Notes

Future Enhancements

Multi-User Architecture (PROJECT EXTENSION)

Goal

Slack Integration Context

User Identity Structure

User File Template

Context Loading Flow

User Files Created (Ethan - ID: 197498647183622144)

Privacy Discovery (IMPORTANT LESSON)

The Problem

Solution: Private Channels per User

How It Works

Why Private Channels > DMs

Implementation Note

Lesson Learned

Technical Implementation Notes

Permission Levels

User Management Commands

Privacy Considerations

Known Issues (ComfyUI)

Home Focus (2026-02-02 Updated)

Action Items (Home Focus)

Quick Reference

23 KiB

Raw Blame History