fieldy-analysis

name	fieldy-analysis
description	Analyze Field Labs coaching transcription data, calculate session metrics, and generate daily summaries. Use for processing Fieldy voice transcriptions and creating coaching reports.

Fieldy Analysis

Process and analyze Field Labs coaching transcription data to generate daily email summaries.

Quick Start

Analyze today's Fieldy data:

python scripts/analyze_fieldy.py data/fieldy/fieldy_2026-01-04.json

Generate daily summary:

python scripts/generate_fieldy_summary.py data/fieldy/fieldy_2026-01-04.json --output summary.json

Data Structure

Input Format (fieldy_YYYY-MM-DD.json)

{
  "date": "2026-01-04",
  "transcriptions": [
    {
      "timestamp": "2026-01-04T10:30:00.000000+00:00",
      "transcription": "Full text of the transcription...",
      "transcriptions": [
        {
          "text": "Individual sentence or phrase",
          "speaker": "Unknown",
          "start": 0.704,
          "end": 7.184,
          "duration": null
        }
      ],
      "metadata": {
        "ip_address": "192.168.1.1",
        "user_agent": "python-httpx/0.28.1",
        "received_at": "2026-01-04T10:30:00.000000"
      }
    }
  ]
}

Metrics to Calculate

Session Metrics

1. Total Sessions

   • Count of transcription entries for the day
   • Each entry = one coaching session/call

2. Total Duration

   • Calculate from start/end times of transcription segments
   • Sum of all segment durations

3. Average Session Length

   • Total duration / number of sessions
   • Measured in minutes

4. Session Timeline

   • Timestamps of each session
   • Distribution throughout the day

Content Metrics

1. Total Words

   • Word count across all transcriptions
   • Indicates session depth/engagement

2. Average Words per Session

   • Total words / number of sessions

3. Speakers

   • Identify unique speakers (if available)
   • Track multi-speaker sessions

4. Topics/Keywords

   • Extract common topics from transcriptions
   • Identify coaching themes

Analysis Scripts

analyze_fieldy.py

Analyze Fieldy transcription data and calculate metrics.

Usage:

python scripts/analyze_fieldy.py input.json --output analysis.json

Output:

{
  "date": "2026-01-04",
  "summary": {
    "total_sessions": 12,
    "total_duration_minutes": 145.3,
    "avg_session_minutes": 12.1,
    "total_words": 5234,
    "avg_words_per_session": 436,
    "first_session": "2026-01-04T08:15:00",
    "last_session": "2026-01-04T17:45:00"
  },
  "sessions": [
    {
      "timestamp": "2026-01-04T08:15:00",
      "duration_minutes": 8.5,
      "word_count": 342,
      "segment_count": 15
    }
  

generate_fieldy_summary.py

Generate formatted summary for daily email report.

Usage:

python scripts/generate_fieldy_summary.py input.json \
    --output summary.json \
    --format report

Output (report format):

{
  "title": "Fieldy Daily Summary",
  "subtitle": "Friday, January 4, 2026",
  "metrics": [
    {
      "label": "Total Sessions",
      "value": "12",
      "trend": "up",
      "change": "+2"
    },
    {
      "label": "Total Duration",
      "value": "145 min",
      "trend": "up",
      "change": "+15 min"
    },
    {
      "label": "Avg Session Length",
      "value": "12.1 min",
       
    
  
   
    
       
       
       
         ...
      
    
  

extract_insights.py

Extract key topics and insights from transcriptions using text analysis.

Usage:

python scripts/extract_insights.py input.json --top-keywords 10

Features:

• Keyword frequency analysis
• Topic clustering
• Sentiment indicators
• Common phrases

Report Templates

Daily Email Summary

Standard format for daily Fieldy email:

Subject: Fieldy Daily Summary - [Date]

Content:

• Total sessions count
• Total duration in minutes
• Average session length
• Session timeline (first/last session times)
• Notable keywords or topics (if available)

Weekly Rollup

Aggregate metrics for the week:

• Sessions per day
• Trend analysis (increasing/decreasing)
• Busiest day
• Total weekly duration

Integration with Agents

Fieldy Agent Workflow

# 1. Read today's Fieldy data
fieldy_file = f"data/fieldy/fieldy_{today}.json"

# 2. Analyze with fieldy-analysis skill
python scripts/analyze_fieldy.py {fieldy_file} --output analysis.json

# 3. Generate summary for report
python scripts/generate_fieldy_summary.py analysis.json --format report

# 4. Use report-generation skill to create HTML
python ../report-generation/scripts/generate_html_report.py \
    summary.json --template daily-summary --output fieldy_report.html

# 5. Delegate to communication-agent to send email
task(agent="communication-agent",
     prompt=f"Send fieldy_report.html to [e-mail]")

Reporting Agent Integration

For weekly reports combining Fieldy with other metrics:

# Aggregate week's Fieldy data
fieldy_weekly = aggregate_fieldy_week(start_date, end_date)

# Combine with GitHub, Skillz, etc.
combined_report = {
    "fieldy": fieldy_weekly,
    "github": github_weekly,
    "skillz": skillz_weekly
}

# Generate comprehensive weekly report
python ../report-generation/scripts/generate_html_report.py \
    combined_report.json --template weekly-summary

Data Quality

Validation Checks

1. Missing Data

   • Check for empty transcriptions
   • Validate timestamp format
   • Ensure duration calculations are positive

2. Outliers

   • Flag sessions > 60 minutes (likely errors)
   • Flag sessions < 1 minute (incomplete)
   • Check for duplicate timestamps

3. Completeness

   • Verify all required fields present
   • Check transcriptions array not empty
   • Validate metadata structure

Scheduling

The Fieldy Agent runs daily at:

• Time: 11:00 AM UTC (3:00 AM PT)
• Trigger: systemd timer (daily-fieldy-email.timer)
• Recipients: [e-mail]

Tips

1. Duration Calculation

   • Use segment start/end times when available
   • Fall back to word count estimation (150 words/min)
   • Handle missing duration gracefully

2. Timezone Handling

   • Fieldy timestamps are in UTC
   • Convert to PT for email display
   • Ensure date boundaries are correct

3. Error Handling

   • Handle missing files gracefully
   • Continue processing if one session fails
   • Log errors for debugging

4. Performance

   • Cache parsed data to avoid re-parsing
   • Process sessions in parallel if many
   • Limit keyword extraction to top N

Example Daily Report

Fieldy Daily Summary
Friday, January 4, 2026

Summary:
✓ 12 total coaching sessions
✓ 145 minutes total duration
✓ 12.1 minutes average session length

Timeline:
• First session: 8:15 AM PT
• Last session: 5:45 PM PT
• Most active period: 2:00 PM - 4:00 PM

Top Keywords:
• technique (45 mentions)
• training (38 mentions)
• form (32 mentions)
• practice (28 mentions)

Reference

• Fieldy data location: data/fieldy/fieldy_YYYY-MM-DD.json
• Archive location: data/fieldy/archive/ (optional)
• Report recipients: See config/email_accounts.json