Living Room Air Quality Monitor#

This skill manages automated collection and reporting of air quality data from the living room ALPSTUGA air sensor connected to the IKEA Dirigera hub.

What It Does#

• Collects data every hour via cron for detailed tracking
• Stores in SQLite database with schema: datetime, temperature, humidity, pm25, co2
• Skips incomplete readings — only saves when all sensor values are present
• Provides query functions for retrieving data by datetime, day, month, or interval
• Calculates averages for any metric (temperature, humidity, PM2.5, CO2) by day or month
• Generates line charts for day, week, month, 3 months, 6 months, or year views
• Sends reports via email or WhatsApp (requires CONTACTS.json configuration)

Note: For smoke/fire detection with alerts, use the separate living-room-smoke-detector skill.

Setup#

1. Database Initialization#

The database is auto-initialized on first data collection. Manual init:

bash

python3 ~/.openclaw/workspace/skills/living-room-air-monitor/scripts/collect_air_data.py

Data folder location: ~/.openclaw/workspace/skills/living-room-air-monitor/data/

Files in data folder:

• air_quality.db — SQLite database with all readings

2. Cron Setup#

Add to crontab for automatic data collection every hour:

bash

0 * * * * /opt/homebrew/bin/python3 /Users/macmini/.openclaw/workspace/skills/living-room-air-monitor/scripts/collect_air_data.py >> /tmp/air_quality_cron.log 2>&1

Scripts Reference#

collect_air_data.py#

Collects a single reading from the air sensor and saves to database.

bash

# Collect data now
python3 scripts/collect_air_data.py

Behavior:

• Fetches data from Dirigera hub (192.168.1.100)
• Requires token in ~/.openclaw/workspace/.dirigera_token
• Skips save if any sensor value is None
• Prints collected values to stdout

query_data.py#

Query the database for readings and averages.

bash

# Get readings for a specific day
python3 scripts/query_data.py --day 2026-02-24

# Get readings for a month
python3 scripts/query_data.py --month 2026-02

# Get averages for a day (all metrics)
python3 scripts/query_data.py --avg-day 2026-02-24

# Get average for specific metric on a day
python3 scripts/query_data.py --avg-day 2026-02-24 --metric temperature

# Get averages for a month
python3 scripts/query_data.py --avg-month 2026-02

# Show available date range
python3 scripts/query_data.py --range

Python API:

python

from scripts.query_data import (
    get_reading_by_datetime,      # Single reading closest to datetime
    get_readings_by_interval,      # All readings in range
    get_readings_by_day,           # All readings for a day
    get_readings_by_month,         # All readings for a month
    get_average_by_day,            # Average for one metric on one day
    get_average_by_month,          # Average for one metric in one month
    get_all_averages_by_day,       # All averages for a day
    get_all_averages_by_month,     # All averages for a month
    get_date_range                 # (earliest, latest) datetime strings
)

generate_chart.py#

Generate line charts for various time periods.

bash

# Chart for today
python3 scripts/generate_chart.py

# Chart for specific day
python3 scripts/generate_chart.py --day 2026-02-24

# Chart for week ending on date
python3 scripts/generate_chart.py --week 2026-02-24

# Chart for month
python3 scripts/generate_chart.py --month 2026-02

# Chart for 3 months ending on date
python3 scripts/generate_chart.py --3month 2026-02-24

# Chart for 6 months ending on date
python3 scripts/generate_chart.py --6month 2026-02-24

# Chart for year
python3 scripts/generate_chart.py --year 2026

# Custom output directory
python3 scripts/generate_chart.py --day 2026-02-24 --output ~/charts

Output: PNG files saved to /tmp/air_charts/ by default.

Python API:

python

from scripts.generate_chart import (
    generate_day_chart,      # (date, output_dir) -> chart_path
    generate_week_chart,     # (end_date, output_dir) -> chart_path
    generate_month_chart,    # (year, month, output_dir) -> chart_path
    generate_3month_chart,   # (end_date, output_dir) -> chart_path
    generate_6month_chart,   # (end_date, output_dir) -> chart_path
    generate_year_chart,     # (year, output_dir) -> chart_path
    generate_chart           # (start_dt, end_dt, output_path, title) -> chart_path
)

send_report.py#

Send comprehensive air quality reports via email or WhatsApp.

bash

# Send today's report via email (default)
python3 scripts/send_report.py

# Send specific day via email
python3 scripts/send_report.py --day 2026-02-24

# Send weekly report via email
python3 scripts/send_report.py --week 2026-02-24

# Send monthly report via email
python3 scripts/send_report.py --month 2026-02

# Send via WhatsApp instead of email
python3 scripts/send_report.py --day 2026-02-24 --channel whatsapp

# Send text-only report (no chart)
python3 scripts/send_report.py --day 2026-02-24 --no-chart

Python API:

python

from scripts.send_report import (
    send_report,          # (start_dt, end_dt, channel, include_chart, chart_path)
    send_daily_report,    # (date, channel='email')
    send_weekly_report,   # (end_date, channel='email')
    send_monthly_report,  # (year, month, channel='email')
    generate_text_report  # (start_dt, end_dt) -> report_text
)

Report Contents#

Reports include:

• Period covered and total readings count
• Average values for all metrics with ranges
• Air quality assessment (Good/Moderate/Poor) for PM2.5 and CO2
• All individual readings with timestamps

PM2.5 thresholds:

• ≤12 µg/m³: Good
• 12.1-35 µg/m³: Moderate

• 35 µg/m³: Unhealthy

CO2 thresholds:

• ≤1000 ppm: Good
• 1001-2000 ppm: Moderate

• 2000 ppm: Poor

Typical Usage Patterns#

User asks: "Send me yesterday's air quality report"#

bash

python3 scripts/send_report.py --day $(date -v-1d +%Y-%m-%d) --channel email

User asks: "What's the average temperature this month?"#

bash

python3 scripts/query_data.py --avg-month $(date +%Y-%m) --metric temperature

User asks: "Show me a chart of the last week"#

bash

python3 scripts/generate_chart.py --week $(date +%Y-%m-%d)

User asks: "Email me the monthly air quality report"#

bash

python3 scripts/send_report.py --month $(date +%Y-%m) --channel email

User asks: "WhatsApp me today's air data"#

bash

python3 scripts/send_report.py --day $(date +%Y-%m-%d) --channel whatsapp

Dependencies#

• Python 3.x
• SQLite3 (built-in)
• matplotlib (auto-installed if missing)
• Dirigera hub access (IP: 192.168.1.100)
• Valid auth token in ~/.openclaw/workspace/.dirigera_token
• gog CLI for email (already configured)
• wacli CLI for WhatsApp (already configured)
• CONTACTS.json in workspace root (for email/WhatsApp reports)

Configuration#

CONTACTS.json#

Create ~/.openclaw/workspace/CONTACTS.json with your contact information:

json

{
  "name": "Your Name",
  "email": "your@email.com",
  "whatsapp": "+614xxxxxxxxx"
}

Note: WhatsApp number must be in international format (e.g., +61400000000)

This file is shared across all skills and prevents hardcoding personal information.

Database Schema#

sql

CREATE TABLE air_quality (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    datetime TEXT NOT NULL UNIQUE,
    temperature REAL,
    humidity REAL,
    pm25 REAL,
    co2 REAL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_datetime ON air_quality(datetime);

Related Skills#

• living-room-smoke-detector — Dedicated smoke/fire detection with continuous Mac speaker alerts. Use this for emergency alerting while air-monitor handles data collection and reporting.