PL Report Generator

Generate automated financial and business reports with PDF output, chart creation, and distribution. Use when: (1) producing recurring financial reports (P&L, balance sheet, cash flow), (2) generating client-ready performance summaries, (3) creating board/exec dashboards with charts, (4) automating scheduled report distribution via email or messaging, (5) converting raw financial data into formatted deliverables. NOT for: one-off ad hoc data queries (use direct analysis), real-time dashboards requiring live data push (use dedicated BI tools), or compliance filings with regulatory signatures required (those need human review and approval).

Report Generator Skill

Automates the full report lifecycle: data extraction → formatting → chart generation → PDF rendering → distribution.

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Core Capabilities

1. Financial Report Types

Report	Frequency	Primary Audience
Profit & Loss (Income Statement)	Monthly / Quarterly	CEO, Board
Balance Sheet	Monthly / Quarterly	CEO, Investors
Cash Flow Statement	Weekly / Monthly	CFO, Ops
AR/AP Aging Summary	Weekly	AR Team, Controller
Budget vs Actual Variance	Monthly	Department Heads
KPI Dashboard	Weekly / Monthly	All Executives
Client Profitability Report	Monthly / Quarterly	Partners
Payroll Summary	Per-payroll-run	HR, Finance

2. Business Reports

• Operations Report — headcount, utilization, productivity metrics
• Sales Pipeline Report — funnel stages, conversion rates, projected revenue
• Expense Analysis — category breakdowns, trend lines, anomaly flagging
• Vendor Spend Report — top vendors, spend trends, contract compliance
• Project Profitability — budget vs actuals per engagement

---

Workflow

Step 1: Data Collection

Identify the source system and extract the raw data:

bash

# QuickBooks export (CSV)
# Pull via QBO skill or manual export from client portal
# Source: reports/raw/2026-03-pl-raw.csv

# Google Sheets source
gog sheets read --id SHEET_ID --range "P&L!A1:Z100" > reports/raw/pl-data.json

# SQL/database source
# sqlite3 db.sqlite "SELECT * FROM transactions WHERE period='2026-02'" > raw.csv

Step 2: Data Processing

python

# scripts/process_pl.py
import csv, json
from collections import defaultdict

def process_pl(input_csv, period):
    """Process P&L raw data into structured format."""
    categories = defaultdict(float)
    
    with open(input_csv) as f:
        reader = csv.DictReader(f)
        for row in reader:
            categories[row['Category']] += float(row['Amount'] or 0)
    
    revenue = sum(v for k, v in categories.items() if 'Revenue' in k or 'Income' in k)
    cogs = sum(v for k, v in categories.items() if 'COGS' in k or 'Cost of' in k)
    gross_profit = revenue - cogs
    expenses = sum(v for k, v in categories.items() if k not in ['Revenue', 'COGS'])
    net_income = gross_profit - expenses
    
    return {
        'period': period,
        'revenue': revenue,
        'cogs': cogs,
        'gross_profit': gross_profit,
        'gross_margin': (gross_profit / revenue * 100) if revenue else 0,
        'expenses': expenses,
        'net_income': net_income,
        'net_margin': (net_income / revenue * 100) if revenue else 0,
        'categories': dict(categories)
    }

Step 3: Chart Generation

python

# scripts/generate_charts.py
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import numpy as np

def revenue_trend_chart(data_points, output_path):
    """Generate revenue trend line chart."""
    periods = [d['period'] for d in data_points]
    revenues = [d['revenue'] for d in data_points]
    
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(periods, revenues, 'b-o', linewidth=2, markersize=8)
    ax.fill_between(periods, revenues, alpha=0.1)
    ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'${x:,.0f}'))
    ax.set_title('Revenue Trend', fontsize=14, fontweight='bold')
    ax.set_xlabel('Period')
    ax.set_ylabel('Revenue')
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(output_path, dpi=150, bbox_inches='tight')
    plt.close()
    return output_path

def expense_breakdown_chart(categories, output_path):
    """Generate expense category pie chart."""
    expense_cats = {k: v for k, v in categories.items() 
                    if v > 0 and 'Revenue' not in k and 'Income' not in k}
    
    labels = list(expense_cats.keys())
    values = list(expense_cats.values())
    
    fig, ax = plt.subplots(figsize=(8, 8))
    wedges, texts, autotexts = ax.pie(
        values, labels=labels, autopct='%1.1f%%',
        startangle=90, pctdistance=0.85
    )
    ax.set_title('Expense Breakdown', fontsize=14, fontweight='bold')
    plt.tight_layout()
    plt.savefig(output_path, dpi=150, bbox_inches='tight')
    plt.close()
    return output_path

def variance_bar_chart(budget_vs_actual, output_path):
    """Generate budget vs actual variance chart."""
    categories = list(budget_vs_actual.keys())
    budgets = [budget_vs_actual[c]['budget'] for c in categories]
    actuals = [budget_vs_actual[c]['actual'] for c in categories]
    
    x = np.arange(len(categories))
    width = 0.35
    
    fig, ax = plt.subplots(figsize=(12, 6))
    bars1 = ax.bar(x - width/2, budgets, width, label='Budget', color='steelblue')
    bars2 = ax.bar(x + width/2, actuals, width, label='Actual', color='coral')
    
    ax.set_title('Budget vs Actual', fontsize=14, fontweight='bold')
    ax.set_xticks(x)
    ax.set_xticklabels(categories, rotation=45, ha='right')
    ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'${x:,.0f}'))
    ax.legend()
    ax.grid(True, alpha=0.3, axis='y')
    plt.tight_layout()
    plt.savefig(output_path, dpi=150, bbox_inches='tight')
    plt.close()
    return output_path

Step 4: PDF Generation

python

# scripts/generate_pdf.py
# Requires: pip install reportlab pillow

from reportlab.lib import colors
from reportlab.lib.pagesizes import letter
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image, PageBreak
)
from reportlab.lib.enums import TA_CENTER, TA_RIGHT
from datetime import datetime

def generate_pl_report(data, chart_paths, output_path, company_name="PrecisionLedger Client"):
    """Generate a complete P&L PDF report."""
    doc = SimpleDocTemplate(
        output_path,
        pagesize=letter,
        rightMargin=0.75*inch,
        leftMargin=0.75*inch,
        topMargin=0.75*inch,
        bottomMargin=0.75*inch
    )
    
    styles = getSampleStyleSheet()
    
    # Custom styles
    title_style = ParagraphStyle(
        'Title', parent=styles['Title'],
        fontSize=20, textColor=colors.HexColor('#1a1a2e'),
        spaceAfter=6
    )
    subtitle_style = ParagraphStyle(
        'Subtitle', parent=styles['Normal'],
        fontSize=11, textColor=colors.HexColor('#666666'),
        spaceAfter=20, alignment=TA_CENTER
    )
    section_style = ParagraphStyle(
        'Section', parent=styles['Heading2'],
        fontSize=13, textColor=colors.HexColor('#1a1a2e'),
        spaceBefore=16, spaceAfter=8,
        borderPad=4
    )
    
    story = []
    
    # Header
    story.append(Paragraph(company_name, title_style))
    story.append(Paragraph(
        f"Profit & Loss Statement — {data['period']}", subtitle_style
    ))
    story.append(Paragraph(
        f"Generated: {datetime.now().strftime('%B %d, %Y')}", 
        ParagraphStyle('gen_date', parent=styles['Normal'], 
                       fontSize=9, textColor=colors.grey, alignment=TA_CENTER)
    ))
    story.append(Spacer(1, 0.25*inch))
    
    # Key metrics summary table
    story.append(Paragraph("Executive Summary", section_style))
    
    summary_data = [
        ["Metric", "Amount", "Margin"],
        ["Total Revenue", f"${data['revenue']:,.2f}", "—"],
        ["Cost of Goods Sold", f"${data['cogs']:,.2f}", 
         f"{data['cogs']/data['revenue']*100:.1f}%" if data['revenue'] else "—"],
        ["Gross Profit", f"${data['gross_profit']:,.2f}", 
         f"{data['gross_margin']:.1f}%"],
        ["Total Expenses", f"${data['expenses']:,.2f}", 
         f"{data['expenses']/data['revenue']*100:.1f}%" if data['revenue'] else "—"],
        ["Net Income", f"${data['net_income']:,.2f}", 
         f"{data['net_margin']:.1f}%"],
    ]
    
    summary_table = Table(summary_data, colWidths=[3*inch, 2*inch, 1.5*inch])
    summary_table.setStyle(TableStyle([
        ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#1a1a2e')),
        ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
        ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
        ('FONTSIZE', (0, 0), (-1, 0), 11),
        ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.HexColor('#f8f9fa')]),
        ('FONTNAME', (0, -1), (-1, -1), 'Helvetica-Bold'),
        ('BACKGROUND', (0, -1), (-1, -1), colors.HexColor('#e8f4fd')),
        ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#dddddd')),
        ('ALIGN', (1, 0), (-1, -1), 'RIGHT'),
        ('TOPPADDING', (0, 0), (-1, -1), 8),
        ('BOTTOMPADDING', (0, 0), (-1, -1), 8),
        ('LEFTPADDING', (0, 0), (0, -1), 12),
    ]))
    story.append(summary_table)
    story.append(Spacer(1, 0.25*inch))
    
    # Charts
    if 'revenue_trend' in chart_paths:
        story.append(Paragraph("Revenue Trend", section_style))
        story.append(Image(chart_paths['revenue_trend'], width=6.5*inch, height=3.25*inch))
        story.append(Spacer(1, 0.15*inch))
    
    if 'expense_breakdown' in chart_paths:
        story.append(Paragraph("Expense Breakdown", section_style))
        story.append(Image(chart_paths['expense_breakdown'], width=4*inch, height=4*inch))
    
    # Detailed category breakdown
    story.append(PageBreak())
    story.append(Paragraph("Detailed Breakdown", section_style))
    
    cat_data = [["Category", "Amount", "% of Revenue"]]
    for cat, amount in sorted(data['categories'].items(), key=lambda x: abs(x[1]), reverse=True):
        pct = f"{amount/data['revenue']*100:.1f}%" if data['revenue'] else "—"
        cat_data.append([cat, f"${amount:,.2f}", pct])
    
    cat_table = Table(cat_data, colWidths=[3.5*inch, 2*inch, 1.5*inch])
    cat_table.setStyle(TableStyle([
        ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#1a1a2e')),
        ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
        ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
        ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.HexColor('#f8f9fa')]),
        ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#dddddd')),
        ('ALIGN', (1, 0), (-1, -1), 'RIGHT'),
        ('TOPPADDING', (0, 0), (-1, -1), 6),
        ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
        ('LEFTPADDING', (0, 0), (0, -1), 12),
    ]))
    story.append(cat_table)
    
    doc.build(story)
    return output_path

Step 5: Distribution

python

# scripts/distribute_report.py

def distribute_via_email(report_path, recipients, subject, body):
    """Distribute report via email (use gog skill for Gmail)."""
    # Use: gog send --to [e-mail] --subject "..." --attach report.pdf --body "..."
    # Or use himalaya skill for IMAP/SMTP
    # REQUIRES Irfan approval before sending to external clients
    pass

def distribute_via_telegram(report_path, chat_id):
    """Send report to Telegram channel."""
    # Use message tool: action=sendAttachment, target=chat_id, filePath=report_path
    pass

---

Quick-Start Templates

Monthly P&L Report (Full Pipeline)

bash

# 1. Set up output dirs
mkdir -p reports/{raw,charts,output}

# 2. Extract data (QBO/Sheets/CSV)
# → reports/raw/2026-02-pl.csv

# 3. Process + generate
python scripts/process_pl.py reports/raw/2026-02-pl.csv "February 2026" > reports/raw/pl-data.json

# 4. Generate charts
python scripts/generate_charts.py reports/raw/pl-data.json reports/charts/

# 5. Generate PDF
python scripts/generate_pdf.py reports/raw/pl-data.json reports/charts/ reports/output/PL-Feb2026.pdf

# 6. Review (ALWAYS before distribution)
open reports/output/PL-Feb2026.pdf

KPI Dashboard (Quick Summary)

python

# scripts/kpi_dashboard.py
# Generates a one-page KPI card PDF

KPI_DEFINITIONS = {
    'Gross Margin': {'target': 0.65, 'format': 'percent'},
    'Net Margin': {'target': 0.20, 'format': 'percent'},
    'Revenue Growth MoM': {'target': 0.05, 'format': 'percent'},
    'AR Days Outstanding': {'target': 30, 'format': 'days', 'lower_is_better': True},
    'Cash Runway': {'target': 6, 'format': 'months'},
    'Billable Utilization': {'target': 0.80, 'format': 'percent'},
}

Variance Report (Budget vs Actual)

python

# Compare budget to actuals and flag variances > threshold
VARIANCE_THRESHOLD = 0.10  # 10% triggers flag

def flag_variances(budget_vs_actual, threshold=VARIANCE_THRESHOLD):
    flags = []
    for category, values in budget_vs_actual.items():
        if values['budget'] > 0:
            variance_pct = (values['actual'] - values['budget']) / values['budget']
            if abs(variance_pct) > threshold:
                direction = 'over' if variance_pct > 0 else 'under'
                flags.append({
                    'category': category,
                    'budget': values['budget'],
                    'actual': values['actual'],
                    'variance_pct': variance_pct,
                    'direction': direction,
                    'severity': 'HIGH' if abs(variance_pct) > 0.25 else 'MEDIUM'
                })
    return sorted(flags, key=lambda x: abs(x['variance_pct']), reverse=True)

---

Report Naming Conventions

text

reports/
├── raw/           ← source data (CSV, JSON exports)
├── charts/        ← generated chart images (PNG)
├── output/        ← final PDFs
│   ├── PL-YYYY-MM-{ClientCode}.pdf
│   ├── BS-YYYY-MM-{ClientCode}.pdf
│   ├── CF-YYYY-MM-{ClientCode}.pdf
│   ├── KPI-YYYY-MM-{ClientCode}.pdf
│   └── BVA-YYYY-MM-{ClientCode}.pdf  ← Budget vs Actual
└── templates/     ← reusable layout templates

---

Dependencies

bash

# Python packages
pip install reportlab matplotlib pillow pandas numpy

# Verify
python -c "import reportlab, matplotlib, pandas; print('OK')"

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Safety & Compliance Rules

1. Never distribute to external parties without Irfan's explicit approval
2. Always review PDF before sending — no automated external distribution
3. Client data stays in reports/raw/ only — never commit to git
4. Watermark drafts — add "DRAFT" overlay until final review
5. Audit trail — log every distribution: reports/distribution-log.json
6. PII handling — redact employee SSNs, salaries from any shared reports

---

Integration Points

System	How to Connect	Direction
QuickBooks Online	QBO skill / CSV export	Read only
Google Sheets	gog sheets skill	Read / Write summary
Email (Gmail)	gog mail / himalaya skill	Send (with approval)
Telegram	message tool	Send PDF
File System	Direct path	Read/Write reports/

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When NOT to Use This Skill

• Real-time BI dashboards — Use Looker, Power BI, or Tableau instead
• Regulatory/tax filings — These require human sign-off and certified software
• Live transaction streams — This skill works on batch/period-end data
• Ad hoc data questions — Just run a direct financial analysis, don't generate a full report
• Data entry or corrections — This is output-only; never write back to source systems