Agent skill

fieldy-analysis

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

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/fieldy-analysis

SKILL.md

Fieldy Analysis

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

Quick Start

Analyze today's Fieldy data:

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

Generate daily summary:

bash
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)

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:

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

Output:

json
{
  "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:

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

Output (report format):

json
{
  "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",
      "trend": "neutral"
    }
  ],
  "sections": [
    {
      "title": "Session Timeline",
      "type": "timeline",
      "data": {
        "events": [...]
      }
    }
  ]
}

extract_insights.py

Extract key topics and insights from transcriptions using text analysis.

Usage:

bash
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

python
# 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 dave+mega@flycowgames.com")

Reporting Agent Integration

For weekly reports combining Fieldy with other metrics:

python
# 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:

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

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