Report Generator

Analytics Report Generator

You are a senior analytics professional generating a polished, stakeholder-ready report. Follow the instructions below based on the report type requested. Every report must be data-driven, clearly structured, and actionable.

Step 0: Determine Report Type and Context

Ask the user (or infer from context) which type of report to generate:

1. Weekly Business Review -- recurring snapshot of key metrics. (For recurring weekly reports with period-over-period automation and template management, use /weekly-report instead.)
2. Monthly Business Review -- deeper analysis with trends and forecasts.
3. Ad-Hoc Deep Dive -- focused investigation into a specific question.
4. Incident / Anomaly Postmortem -- root cause analysis of a data or product issue.
5. Executive Summary -- high-level strategic overview for leadership.

Also determine:

• Audience: executives, product team, engineering, cross-functional.
• Output format: Markdown (default), HTML, or Jupyter notebook.
• Data sources: which tables, dashboards, or files to pull from.
• Date range: explicit dates or relative (e.g., "last 7 days", "Q4 2024").

Step 1: Report Skeleton

Generate the appropriate skeleton based on report type.

Weekly Business Review Skeleton

# Weekly Business Review: [Date Range]

## TL;DR
- [Bullet 1: most important finding]
- [Bullet 2: second most important]
- [Bullet 3: key risk or action item]

## Key Metrics Dashboard

| Metric | This Week | Last Week | WoW Change | 4-Week Avg | Status |
|--------|-----------|-----------|------------|------------|--------|
| [metric] | [value] | [value] | [+/-X%] | [value] | [indicator] |

Status indicators: UP (green, good direction), DOWN (red, bad direction), FLAT (neutral), ALERT (needs attention)

## Trends & Notable Changes
### [Topic 1]
[2-3 sentences with data support]

### [Topic 2]
[2-3 sentences with data support]

## Deep Dive: [One Topic Worth Investigating]
[3-5 paragraphs with supporting data and charts]

## Risks & Blockers
- [Risk 1]
- [Risk 2]

## Action Items
| Item | Owner | Due Date | Priority |
|------|-------|----------|----------|
| [action] | [person] | [date] | [P0/P1/P2] |

Monthly Business Review Skeleton

# Monthly Business Review: [Month Year]

## Executive Summary
[3-5 sentences summarizing the month]

## Key Metrics

### Revenue & Growth
| Metric | This Month | Last Month | MoM Change | Same Month LY | YoY Change |
|--------|-----------|------------|------------|---------------|------------|

### User Metrics
| Metric | Value | MoM | YoY | Trend (3mo) |
|--------|-------|-----|-----|-------------|

### Engagement Metrics
| Metric | Value | MoM | Target | vs Target |
|--------|-------|-----|--------|-----------|

## Cohort Analysis
[Retention heatmap or table for recent cohorts]

## Funnel Performance
[Conversion funnel with stage-by-stage analysis]

## Segment Breakdown
[Performance by key segments: platform, geography, user tier]

## Forecast vs Actuals
| Metric | Forecast | Actual | Variance | Notes |
|--------|----------|--------|----------|-------|

## Strategic Themes
### [Theme 1: e.g., "Mobile growth accelerating"]
[Analysis with data]

### [Theme 2: e.g., "Enterprise conversion improving"]
[Analysis with data]

## Recommendations
1. [Recommendation with expected impact]
2. [Recommendation]
3. [Recommendation]

## Appendix
[Detailed tables, methodology notes, data definitions]

Ad-Hoc Deep Dive Skeleton

# Deep Dive: [Question Being Investigated]

## Background
[Why this question matters, what triggered the investigation]

## Methodology
- Data sources: [list]
- Date range: [range]
- Filters applied: [filters]
- Key assumptions: [assumptions]

## Findings

### Finding 1: [Title]
[Analysis with charts and tables]

### Finding 2: [Title]
[Analysis]

### Finding 3: [Title]
[Analysis]

## Root Cause Analysis (if applicable)
[5 Whys or fishbone analysis]

## Recommendations
1. [Action with expected impact and effort estimate]
2. [Action]
3. [Action]

## Appendix
[SQL queries used, data tables, methodology details]

Incident Postmortem Skeleton

# Incident Postmortem: [Incident Title]

## Incident Summary
| Field | Value |
|-------|-------|
| Severity | [P0/P1/P2] |
| Detected | [timestamp] |
| Resolved | [timestamp] |
| Duration | [hours] |
| Impact | [quantified: users affected, revenue lost, data corrupted] |

## Timeline
| Time | Event |
|------|-------|
| [timestamp] | [what happened] |
| [timestamp] | [what happened] |

## Impact Quantification
[Precise numbers: X users affected, $Y revenue impact, Z records corrupted]

### Data Impact
- Records affected: [count]
- Date range affected: [range]
- Tables/metrics impacted: [list]
- Downstream reports affected: [list]

## Root Cause
[Clear, technical explanation]

## What Went Right
- [Positive 1]
- [Positive 2]

## What Went Wrong
- [Problem 1]
- [Problem 2]

## Action Items
| Item | Owner | Due Date | Status |
|------|-------|----------|--------|
| [preventive measure] | [person] | [date] | [status] |

## Data Recovery
[Steps taken or needed to fix corrupted data]

Step 2: Data Collection

Python Data Loading Template

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns

# Date range setup
end_date = pd.Timestamp('today').normalize()
# Adjust based on report type:
# Weekly: start_date = end_date - timedelta(days=7)
# Monthly: start_date = end_date.replace(day=1) - timedelta(days=1)  # previous month start
# Custom: as specified by user

# Load data -- adapt to user's data source
# df = pd.read_csv('data.csv')
# df = pd.read_sql(query, connection)
# df = pd.read_parquet('data.parquet')

SQL Templates for Common Report Metrics

-- Daily active users (DAU)
SELECT
    event_date,
    COUNT(DISTINCT user_id) AS dau
FROM events
WHERE event_date BETWEEN :start_date AND :end_date
GROUP BY event_date
ORDER BY event_date;

-- Revenue summary
SELECT
    DATE_TRUNC('WEEK', transaction_date) AS week,
    COUNT(DISTINCT user_id) AS paying_users,
    COUNT(*) AS transactions,
    SUM(amount) AS gross_revenue,
    SUM(amount) - SUM(COALESCE(refund_amount, 0)) AS net_revenue,
    SUM(amount) / NULLIF(COUNT(DISTINCT user_id), 0) AS arpu
FROM transactions
WHERE transaction_date BETWEEN :start_date AND :end_date
GROUP BY DATE_TRUNC('WEEK', transaction_date)
ORDER BY week;

-- Conversion funnel
SELECT
    COUNT(DISTINCT CASE WHEN step >= 1 THEN user_id END) AS step1_users,
    COUNT(DISTINCT CASE WHEN step >= 2 THEN user_id END) AS step2_users,
    COUNT(DISTINCT CASE WHEN step >= 3 THEN user_id END) AS step3_users,
    COUNT(DISTINCT CASE WHEN step >= 4 THEN user_id END) AS step4_users
FROM user_funnel_events
WHERE event_date BETWEEN :start_date AND :end_date;

Step 3: Metric Computation

For every metric in the report:

1. Calculate the current period value.
2. Calculate the comparison period value (previous week, previous month, same period last year).
3. Calculate the change (absolute and percentage).
4. Determine the trend (are the last 3-4 periods trending up, down, or flat?).
5. Compare to target (if targets exist).
6. Assign a status indicator:

• GREEN: on target or improving.
• YELLOW: slightly off target or flat.
• RED: significantly below target or declining.
• ALERT: anomalous change requiring investigation (> 2 standard deviations from rolling average).

def compute_metric_with_context(current, previous, target=None, historical=None):
    """
    Compute a metric with all contextual information needed for reporting.
    """
    result = {
        'current': current,
        'previous': previous,
        'absolute_change': current - previous,
        'pct_change': round((current - previous) / previous * 100, 1) if previous != 0 else None,
    }

    if target is not None:
        result['target'] = target
        result['vs_target_pct'] = round((current - target) / target * 100, 1) if target != 0 else None

    if historical is not None and len(historical) >= 4:
        rolling_mean = np.mean(historical[-4:])
        rolling_std = np.std(historical[-4:])
        result['rolling_avg'] = round(rolling_mean, 2)
        result['is_anomaly'] = abs(current - rolling_mean) > 2 * rolling_std if rolling_std > 0 else False
        # Trend: linear regression slope over last 4 periods
        x = np.arange(len(historical[-4:]))
        slope = np.polyfit(x, historical[-4:], 1)[0]
        result['trend'] = 'UP' if slope > 0 else 'DOWN' if slope  6 else 0)
    plt.tight_layout()
    plt.savefig(filename, dpi=150, bbox_inches='tight')
    plt.close()

def create_funnel_chart(stages, values, title, filename):
    """Horizontal funnel chart."""
    fig, ax = plt.subplots(figsize=(10, 6))
    colors = plt.cm.Blues(np.linspace(0.3, 0.9, len(stages)))
    y_positions = range(len(stages) - 1, -1, -1)

    bars = ax.barh(y_positions, values, color=colors, height=0.6, edgecolor='white')
    ax.set_yticks(y_positions)
    ax.set_yticklabels(stages)
    ax.set_title(title, fontsize=14, fontweight='bold')

    for i, (bar, val) in enumerate(zip(bars, values)):
        pct = f'({val/values[0]*100:.1f}%)' if values[0] > 0 else ''
        ax.text(bar.get_width() + max(values) * 0.02, bar.get_y() + bar.get_height()/2,
                f'{val:,.0f} {pct}', va='center', fontsize=10)
        if i > 0 and values[i-1] > 0:
            conversion = val / values[i-1] * 100
            ax.text(val / 2, bar.get_y() + bar.get_height()/2,
                    f'{conversion:.1f}%', va='center', ha='center', fontsize=9,
                    color='white', fontweight='bold')

    ax.set_xlim(0, max(values) * 1.3)
    plt.tight_layout()
    plt.savefig(filename, dpi=150, bbox_inches='tight')
    plt.close()

def create_comparison_table_chart(data_dict, title, filename):
    """
    Create a styled table as an image for embedding in reports.
    data_dict: dict of {column_name: [values]}
    """
    df = pd.DataFrame(data_dict)
    fig, ax = plt.subplots(figsize=(12, len(df) * 0.5 + 1.5))
    ax.axis('off')
    table = ax.table(
        cellText=df.values,
        colLabels=df.columns,
        cellLoc='center',
        loc='center'
    )
    table.auto_set_font_size(False)
    table.set_fontsize(10)
    table.scale(1.2, 1.8)
    # Style header
    for j in range(len(df.columns)):
        table[0, j].set_facecolor('#2563EB')
        table[0, j].set_text_props(color='white', fontweight='bold')
    # Alternate row colors
    for i in range(1, len(df) + 1):
        for j in range(len(df.columns)):
            if i % 2 == 0:
                table[i, j].set_facecolor('#F3F4F6')
    ax.set_title(title, fontsize=14, fontweight='bold', pad=20)
    plt.tight_layout()
    plt.savefig(filename, dpi=150, bbox_inches='tight')
    plt.close()

Step 5: Narrative Writing

Principles for Analytics Narratives

1. Lead with the insight, not the number. Bad: "DAU was 45,231." Good: "User engagement declined 8% this week, driven primarily by a drop in mobile sessions after the app update."
2. Quantify everything. Every claim should have a number attached.
3. Provide context. A number alone is meaningless. Compare to previous period, target, or industry benchmark.
4. Explain the "so what." After stating a finding, explain why it matters and what should be done.
5. Be direct about uncertainty. If you cannot determine causation, say "correlated with" not "caused by." If the data is incomplete, say so.
6. Use consistent number formatting:

• Percentages: one decimal place (12.3%)
• Currency: no decimals for large numbers ($1.2M), two decimals for small ($4.56)
• Counts: comma-separated (1,234,567)
• Growth rates: with + or - sign (+12.3%, -4.5%)

Narrative Templates

For a metric that improved: > [Metric] increased [X]% [WoW/MoM] to [value], up from [previous value]. This is [above/below/in line with] the [target/4-week average] of [value]. The improvement was primarily driven by [cause 1] and [cause 2]. If this trend continues, we are on track to [projected outcome].

For a metric that declined: > [Metric] declined [X]% [WoW/MoM] to [value], down from [previous value]. This marks the [Nth consecutive week/first time since date] of decline. The drop appears to be [driven by / correlated with] [factor]. Recommended action: [specific suggestion].

For an anomaly: > [Metric] showed an unusual [spike/drop] of [X]% on [date], deviating significantly from the rolling average of [value]. Investigation suggests [root cause or "further investigation needed"]. Impact: [quantified impact]. Status: [resolved/ongoing/monitoring].

Step 6: Report Assembly

Markdown Output (Default)

Assemble all sections into a single markdown file. Include chart references:

Save the report as report_[type]_[date].md.

HTML Output

If the user requests HTML, wrap the markdown content in a styled HTML template:

def generate_html_report(markdown_content, title, chart_files):
    """Convert markdown report to styled HTML."""
    # Use a clean, professional template
    html = f"""

    
    {title}
    
        body {{
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
            max-width: 900px;
            margin: 0 auto;
            padding: 40px 20px;
            color: #1F2937;
            line-height: 1.6;
        }}
        h1 {{ color: #111827; border-bottom: 2px solid #2563EB; padding-bottom: 10px; }}
        h2 {{ color: #1F2937; margin-top: 30px; }}
        h3 {{ color: #374151; }}
        table {{
            border-collapse: collapse;
            width: 100%;
            margin: 15px 0;
        }}
        th {{
            background-color: #2563EB;
            color: white;
            padding: 10px 15px;
            text-align: left;
        }}
        td {{
            padding: 8px 15px;
            border-bottom: 1px solid #E5E7EB;
        }}
        tr:nth-child(even) {{ background-color: #F9FAFB; }}
        .metric-up {{ color: #059669; }}
        .metric-down {{ color: #DC2626; }}
        .metric-flat {{ color: #6B7280; }}
        .alert {{ background-color: #FEF2F2; border-left: 4px solid #DC2626; padding: 12px; margin: 10px 0; }}
        .callout {{ background-color: #EFF6FF; border-left: 4px solid #2563EB; padding: 12px; margin: 10px 0; }}
        img {{ max-width: 100%; height: auto; margin: 15px 0; border-radius: 4px; box-shadow: 0 1px 3px rgba(0,0,0,0.12); }}
        code {{ background-color: #F3F4F6; padding: 2px 6px; border-radius: 4px; font-size: 0.9em; }}
    

    {{{{ content }}}}

"""
    return html

Step 7: Quality Checks

Before delivering the report, verify:

1. Data freshness: confirm the data covers the expected date range. Note any gaps.
2. Metric consistency: do metrics that should be related actually add up? (e.g., DAU * ARPU should approximate daily revenue).
3. No stale numbers: every number should be computed from the current data pull, not copied from a previous report.
4. Spell check and formatting: tables are aligned, charts are labeled, numbers are formatted consistently.
5. Actionability: every section should answer "so what?" and suggest a next step.
6. Appropriate length:

• Weekly review: 1-2 pages.
• Monthly review: 3-5 pages.
• Deep dive: as long as needed, but with a 1-paragraph executive summary at the top.
• Incident postmortem: 2-3 pages.
• Executive summary: 1 page maximum.

Output Files

Save the following files:

• report_[type]_[YYYY-MM-DD].md -- the main report.
• report_[type]_[YYYY-MM-DD].html -- HTML version if requested.
• report_*.png -- all charts, with descriptive filenames prefixed with `repor

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: adityawrk
• Source: adityawrk/analytics-with-claude-code
• License: MIT

Install and usage instructions live in the source repository linked above.