who-growth-chartsGenerate WHO child growth charts (height, weight, BMI) with percentile curves. Downloads official WHO reference data on demand. Use when users ask about child growth tracking, percentiles, or want growth charts for their kids.
Install via ClawdBot CLI:
clawdbot install odrobnik/who-growth-chartsGenerate WHO Child Growth Standards charts with percentile curves and child data overlay.
| Height | Weight | BMI |
|--------|--------|-----|
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Install Python dependencies:
pip install pandas matplotlib scipy openpyxlpython3 ./scripts/growth_chart.py "Child Name" "DD.MM.YYYY" --sex F --type allArguments:
name: Child's name (used in chart title)birthdate: Date of birth in DD.MM.YYYY format--sex / -s: F (female) or M (male) — default: F--type / -t: height, weight, bmi, or all — default: all--data / -d: JSON file with measurement data--output / -o: Output directory for chartsCreate a JSON file with height/weight measurements (heights in meters, weights in kg):
{
"heights": [ ["2024-01-15T10:00:00", 1.05] ],
"weights": [ ["2024-01-15T10:00:00", 17.5] ]
}python3 ./scripts/growth_chart.py "Emma" "06.07.2016" --sex F --data emma_data.json --type allCombine with withings-family skill to fetch weight data automatically:
# Get Withings weight data (assuming withings-family skill is installed)
python3 ../withings-family/scripts/withings.py emma body > /tmp/withings.json
# Parse and generate charts
# (The growth chart script handles Withings JSON format if implemented, otherwise transform it)By default, charts and cache are written to:
/who-growth-charts/ /who-growth-charts/cache/ Where skills/ directory (the script finds it automatically; it also prefers the current working directory if it looks like a workspace, which makes symlinked workspaces behave correctly).
You can override workspace discovery with WHO_GROWTH_CHARTS_WORKSPACE=/path/to/workspace.
Generated Mar 1, 2026
Pediatricians use this skill to generate WHO growth charts during routine check-ups, overlaying a child's height, weight, and BMI measurements to track development against percentiles. It helps in early detection of growth issues like stunting or obesity, providing visual aids for parent consultations.
Mobile health apps integrate this skill to allow parents to input their child's measurements and automatically generate growth charts. It offers personalized insights and trends, enhancing user engagement by visualizing growth progress over time for better health management.
Schools employ this skill in annual health screenings to assess students' growth patterns and identify potential health concerns. It generates charts for large groups efficiently, supporting data-driven decisions for nutrition and wellness initiatives.
Researchers use this skill to analyze growth data in studies on nutrition, genetics, or environmental factors. It automates chart generation from datasets, facilitating comparisons across populations and supporting academic publications with standardized visualizations.
Telehealth platforms integrate this skill to enable remote growth assessments, where parents upload measurements via apps. Doctors review generated charts during virtual visits, offering timely advice without in-person appointments, improving access to care.
Offer a free basic version for individual use and a paid subscription for clinics with features like bulk chart generation, data export, and API access. Revenue comes from monthly fees per user or clinic license, targeting small to medium healthcare practices.
License the skill to health and fitness app developers as an embedded tool, charging a one-time integration fee or revenue share based on usage. This model leverages existing user bases, providing growth tracking as a value-added feature in parental apps.
Provide customized reporting services for organizations like schools or research institutions, analyzing aggregated growth data to identify trends and generate insights. Revenue is generated through project-based contracts or annual service agreements.
💬 Integration Tip
Ensure Python dependencies are installed and cache the WHO data locally to speed up chart generation; consider automating data input from health devices via JSON integration.
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