Report Generation

Report Generation

Generate professional HTML reports with charts, tables, and dashboards using neo-brutal design aesthetic.

Quick Start

Generate a report:

python scripts/generate_html_report.py data.json --template daily-summary --output report.html

Create charts:

python scripts/create_charts.py metrics.json --type bar --output chart.png

Design Principles

Neo-Brutal Aesthetic

All reports follow these design rules:

1. Bold Typography

   • Strong, thick fonts (Arial Black, system sans-serif)
   • Clear hierarchy with size contrast

2. High Contrast

   • Black text on white backgrounds
   • Bold borders (3-4px solid black)
   • No gradients or shadows (except hard box-shadow)

3. Geometric Layouts

   • Grid-based layouts
   • Sharp corners, no border-radius
   • Consistent spacing

4. Flat Colors

   • Solid colors only
   • Color palette:
     • Primary: #000 (black)
     • Background: #fff (white)
     • Accent: #f5f5f5 (light gray)
     • Success: #4ade80 (green)
     • Warning: #fbbf24 (yellow)
     • Error: #f87171 (red)
     • Info: #60a5fa (blue)

Report Templates

1. Daily Summary

Use for: GitHub activity, Fieldy coaching, general daily reports

Sections:

• Header with title and date
• Summary statistics (grid of metric cards)
• Timeline of events
• Top items (tables)
• Footer with timestamp

Example:

python scripts/generate_html_report.py daily_data.json \
    --template daily-summary \
    --title "GitHub Daily Activity" \
    --output report.html

2. Analytics Dashboard

Use for: Multi-metric dashboards, KPI tracking

Sections:

• KPI cards grid
• Charts (bar, line, pie)
• Comparison tables
• Trends over time

Example:

python scripts/generate_html_report.py analytics.json \
    --template dashboard \
    --period week \
    --output dashboard.html

3. Comparison Report

Use for: Before/after, A/B testing, performance comparisons

Sections:

• Side-by-side comparison tables
• Delta calculations
• Visual indicators (↑↓)
• Summary of changes

Example:

python scripts/generate_html_report.py comparison.json \
    --template comparison \
    --output comparison.html

4. Leaderboard

Use for: Contributor rankings, performance rankings

Sections:

• Ranked table with badges
• Top 3 highlight
• Detailed metrics per entry
• Trends

Example:

python scripts/generate_html_report.py leaderboard.json \
    --template leaderboard \
    --output leaderboard.html

5. Timeline Report

Use for: Event sequences, activity logs, chronological data

Sections:

• Chronological event list
• Time markers
• Event categories
• Activity heatmap

Example:

python scripts/generate_html_report.py events.json \
    --template timeline \
    --output timeline.html

Data Input Format

Generic Report Data

{
  "title": "Report Title",
  "subtitle": "Report subtitle or date",
  "summary": {
    "metrics": [
      {
        "label": "Total Items",
        "value": "42",
        "change": "+5",
        "trend": "up"
      }
    ]
  },
  "sections": [
    {
      "title": "Section Title",
      "type": "table|list|chart|text",
      "data": {}
    }
  ],
  "footer": "Custom footer text"
}

Chart Data

{
  "title": "Chart Title",
  "type": "bar|line|pie|area",
  "data": {
    "labels": ["Jan", "Feb", "Mar"],
    "datasets": [
      {
        "label": "Series 1",
        "values": [10, 20, 15],
        "color": "#4ade80"
      }
    ]
  }
}

HTML Components

Metric Card

<div class="metric-card">
    <div class="metric-value">42</div>
    <div class="metric-label">Total Commits</div>
    <div class="metric-change positive">+5 ↑</div>
</div>

Table with Styling

<table class="data-table">
    <thead>
        <tr>
            <th>Column 1</th>
            <th>Column 2</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>Data 1</td>
            <td>Data 2</td>
        </tr>
    </tbody>
</table>

Status Badge

<span class="badge badge-success">Active</span>
<span class="badge badge-warning">Pending</span>
<span class="badge badge-error">Failed</span>

Section with Border

<div class="section">
    <div class="section-title">Section Title</div>
    <div class="section-content">
        <!-- Content here -->
    </div>
</div>

Charting

Chart Types

1. Bar Chart

   • Compare discrete values
   • Horizontal or vertical bars
   • Good for rankings, comparisons

2. Line Chart

   • Show trends over time
   • Multiple series support
   • Good for time series data

3. Pie Chart

   • Show proportions
   • Good for composition breakdown
   • Limited to 8 segments max

4. Area Chart

   • Stacked values over time
   • Good for cumulative metrics
   • Shows magnitude and trend

Chart Generation

Uses matplotlib with neo-brutal styling:

import matplotlib.pyplot as plt

# Neo-brutal chart styling
plt.style.use('seaborn-v0_8-darkgrid')
plt.rcParams['axes.edgecolor'] = '#000'
plt.rcParams['axes.linewidth'] = 3
plt.rcParams['font.weight'] = 'bold'
plt.rcParams['font.size'] = 12
plt.rcParams['axes.grid'] = False

Example:

python scripts/create_charts.py data.json \
    --type bar \
    --title "Commits per Day" \
    --output chart.png \
    --width 800 \
    --height 400

Email Integration

Reports can be embedded in emails using the email-formatting skill:

# Generate report HTML
python scripts/generate_html_report.py data.json --template daily-summary --output report.html

# Embed in email (via email-formatting skill)
python ../email-formatting/scripts/format_email.py \
    --template email-report \
    --content report.html \
    --output email.html

Scripts

generate_html_report.py

Generate complete HTML reports from JSON data.

Usage:

python scripts/generate_html_report.py input.json \
    --template [daily-summary|dashboard|comparison|leaderboard|timeline] \
    --title "Report Title" \
    --subtitle "Optional subtitle" \
    --output report.html

Options:

• --template: Report template to use
• --title: Report title
• --subtitle: Optional subtitle
• --period: Time period (day/week/month)
• --output: Output HTML file

create_charts.py

Generate chart images from data.

Usage:

python scripts/create_charts.py data.json \
    --type [bar|line|pie|area] \
    --title "Chart Title" \
    --output chart.png \
    --width 800 \
    --height 400

Options:

• --type: Chart type
• --title: Chart title
• --width: Image width (px)
• --height: Image height (px)
• --style: Color scheme (default/success/warning/error)

aggregate_data.py

Aggregate and transform data for reporting.

Usage:

python scripts/aggregate_data.py input.json \
    --group-by date \
    --metrics count,sum,avg \
    --output aggregated.json

Options:

• --group-by: Field to group by
• --metrics: Metrics to calculate
• --filter: Filter expression
• --sort: Sort field

Integration with Agents

Reporting Agent Workflow

# 1. Gather data from domain agents
github_data = task(agent="code-agent", prompt="Get yesterday's commits")
fieldy_data = task(agent="fieldy-agent", prompt="Get today's coaching data")

# 2. Aggregate data
python scripts/aggregate_data.py combined_data.json --group-by date

# 3. Generate report
python scripts/generate_html_report.py aggregated.json \
    --template daily-summary \
    --output report.html

# 4. Send via communication-agent
task(agent="communication-agent",
     prompt=f"Send report.html to dave@flycowgames.com")

Code Agent (GitHub Reports)

# Generate GitHub activity report
commits = github.get_commits(days=1)
python scripts/generate_html_report.py commits.json \
    --template daily-summary \
    --title "GitHub Daily Activity"

Fieldy Agent (Coaching Reports)

# Generate Fieldy coaching report
coaching_data = fieldy.get_today_data()
python scripts/generate_html_report.py coaching_data.json \
    --template daily-summary \
    --title "Fieldy Daily Summary"

Common Report Patterns

Daily Activity Report

1. Load yesterday's data
2. Aggregate by repository/project
3. Calculate key metrics
4. Generate timeline
5. Create HTML report
6. Email to recipients

Weekly Summary

1. Load week's data
2. Calculate trends (compare to previous week)
3. Identify top performers
4. Generate charts
5. Create dashboard HTML
6. Email with executive summary

Performance Comparison

1. Load two datasets (before/after)
2. Calculate deltas
3. Generate comparison tables
4. Add visual indicators
5. Create comparison report
6. Highlight key changes

Styling Reference

See templates/base.css for complete CSS stylesheet.

Key classes:

• .container - Main container (max-width, border, shadow)
• .header - Report header (black background)
• .section - Content section
• .metric-card - KPI card
• .data-table - Styled table
• .badge - Status badge
• .timeline - Event timeline
• .chart-container - Chart wrapper

Tips

1. Keep it Simple

   • Don't overcomplicate layouts
   • Stick to neo-brutal aesthetic
   • Focus on data clarity

2. Consistent Spacing

   • Use 20px/30px/40px spacing units
   • Maintain visual rhythm
   • Align elements to grid

3. Readable Typography

   • Minimum 14px for body text
   • Bold for emphasis
   • High contrast always

4. Data Density

   • Show top 10-20 items max
   • Summarize the rest
   • Use pagination for long lists

5. Performance

   • Inline critical CSS
   • Optimize images
   • Keep HTML size under 500KB