AI Skill Report Card
Analyzing Fitness Data
Quick Start10 / 15
Python# Extract key metrics from fitness dashboard def analyze_fitness_dashboard(dashboard_text): metrics = { 'user': extract_user_name(dashboard_text), 'weight_progress': extract_weight_evolution(dashboard_text), 'training_frequency': count_training_sessions(dashboard_text), 'total_load': extract_total_load(dashboard_text), 'progress_trend': calculate_trend(dashboard_text) } return generate_insights(metrics)
Recommendation▾
Add concrete input/output examples showing actual dashboard text snippets with corresponding structured analysis results
Workflow12 / 15
Progress:
- Parse dashboard interface elements
- Extract quantitative metrics (weight, load, frequency)
- Identify time periods and date ranges
- Calculate progress trends
- Generate actionable recommendations
- Format insights for user consumption
Recommendation▾
Include a ready-to-use template or framework for fitness data analysis rather than just function names
Examples15 / 20
Example 1: Input: Dashboard showing "Carga Total: 0 kg, Treinos: 0, Últimos 3 meses" Output:
Status: New user - no training history
Recommendation: Begin with assessment questionnaire
Next steps: Access "Meus Treinos" to start first workout
Example 2: Input: Weight evolution chart from 23/fev to 25/mai with 0.5-2kg variations Output:
Period: Feb 23 - May 25 (3 months)
Weight range: 0.5-2.0 kg progression
Trend: Gradual increase indicating strength gains
Average weekly progression: 0.2 kg
Recommendation▾
Expand completeness by covering edge cases like incomplete data, different fitness app formats, or missing metrics
Best Practices
- Focus on progression trends rather than absolute values
- Identify gaps in training consistency
- Correlate load increases with time periods
- Highlight milestone achievements
- Provide context for zero values (new user vs. inactive period)
- Extract date ranges accurately from Portuguese formats
- Map interface elements to functional areas (questionnaires, exercises, muscle groups)
Common Pitfalls
- Don't assume zero values indicate failure - may indicate new user
- Avoid comparing users without considering starting points
- Don't ignore interface navigation elements - they show available features
- Don't overlook date format differences (dd/mmm vs dd/mm)
- Avoid making recommendations without understanding user's current phase