Business Analytics Reporter

Name: Business Analytics Reporter
Author: ailabs-393

ailabs-393/ai-labs-claude-skills

2.1k installs
432 repo stars
Updated November 11, 2025
ailabs-393/ai-labs-claude-skills

business-analytics-reporter is an agent skill that This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate.

About

Generate comprehensive business performance reports that analyze sales and revenue data identify areas where the business is lacking interpret what the statistics indicate and provide actionable improvement strategies The skill uses data driven analysis to detect weak areas and recommends specific strategies backed by business frameworks Invoke this skill when users request Analyze my business data and tell me where we re lacking Generate a report on what areas need improvement What do these sales numbers tell us about our business performance Create a business analysis report with improvement strategies Identify weak areas in our revenue data What strategies should we use to improve our business metrics The skill expects CSV files containing business data sales revenue transactions with columns like dates amounts categories or products Start by understanding the data structure and what the user wants to analyze Ask clarifying questions if needed What specific metrics or areas should the analysis focus on Are there particular time periods or categories of interest Should the report include visualizations or focus

description: This skill should be used when analyzing business sales and revenue data from CSV files to identify weak ar
Generate comprehensive business performance reports that analyze sales and revenue data, identify areas where the busine
Invoke this skill when users request:
Follow business-analytics-reporter SKILL.md steps and documented constraints.
Follow business-analytics-reporter SKILL.md steps and documented constraints.

Business Analytics Reporter by the numbers

2,100 all-time installs (skills.sh)
+19 installs in the week ending Aug 2, 2026 (Skillselion tracking)
Ranked #558 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
Security screen: MEDIUM risk (skills.sh audit)
Data as of Aug 3, 2026 (Skillselion catalog sync)

At a glance

business-analytics-reporter capabilities & compatibility

Capabilities: description: this skill should be used when anal · generate comprehensive business performance repo · invoke this skill when users request: · follow business analytics reporter skill.md step
Use cases: orchestration

From the docs

What business-analytics-reporter says it does

description: This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate statistical insights, and provide strategic improvement recommenda

SKILL.md

Generate comprehensive business performance reports that analyze sales and revenue data, identify areas where the business is lacking, interpret what the statistics indicate, and provide actionable im

SKILL.md

Invoke this skill when users request:

SKILL.md

npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill business-analytics-reporter

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Installs	2.1k
repo stars	★ 432
Security audit	2 / 3 scanners passed
Last updated	November 11, 2025
Repository	ailabs-393/ai-labs-claude-skills ↗

When should an agent use business-analytics-reporter and what problem does it solve?

This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate statistical insights, and provide strategic improvement recommendations. Use wh

Who is it for?

Developers invoking business-analytics-reporter as documented in the skill source.

Skip if: Skip when requirements fall outside business-analytics-reporter documented scope.

When should I use this skill?

What you get

Outputs aligned with the business-analytics-reporter SKILL.md workflow and stated deliverables.

HTML or Markdown performance report
JSON metrics summary
optional Plotly charts

By the numbers

1797 installs listed on skills.sh
Outputs 3 report formats: JSON, Markdown, and HTML

Files

SKILL.mdMarkdownGitHub ↗

Business Analytics Reporter

Overview

Generate comprehensive business performance reports that analyze sales and revenue data, identify areas where the business is lacking, interpret what the statistics indicate, and provide actionable improvement strategies. The skill uses data-driven analysis to detect weak areas and recommends specific strategies backed by business frameworks.

When to Use This Skill

Invoke this skill when users request:

"Analyze my business data and tell me where we're lacking"
"Generate a report on what areas need improvement"
"What do these sales numbers tell us about our business performance?"
"Create a business analysis report with improvement strategies"
"Identify weak areas in our revenue data"
"What strategies should we use to improve our business metrics?"

The skill expects CSV files containing business data (sales, revenue, transactions) with columns like dates, amounts, categories, or products.

Core Workflow

Step 1: Data Loading and Exploration

Start by understanding the data structure and what the user wants to analyze.

Ask clarifying questions if needed:

What specific metrics or areas should the analysis focus on?
Are there particular time periods or categories of interest?
Should the report include visualizations or focus on written analysis?

Load and explore the data:

import pandas as pd

# Load the CSV file
df = pd.read_csv('business_data.csv')

# Display basic information
print(f"Data shape: {df.shape}")
print(f"Columns: {df.columns.tolist()}")
print(f"Date range: {df['date'].min()} to {df['date'].max()}")
print(df.head())

Step 2: Run Automated Analysis

Use the bundled analysis script to generate comprehensive insights:

python scripts/analyze_business_data.py path/to/business_data.csv output_report.json

The script will: 1. Automatically detect data structure (revenue columns, date columns, categories) 2. Calculate statistical metrics (mean, median, growth rates, volatility) 3. Identify trends and patterns 4. Detect weak areas and underperforming segments 5. Generate improvement strategies based on findings 6. Output a structured JSON report

Output structure:

{
  "metadata": {...},
  "findings": {
    "basic_statistics": {...},
    "trend_analysis": {...},
    "category_analysis": {...},
    "variability": {...}
  },
  "weak_areas": [...],
  "improvement_strategies": [...]
}

Step 3: Interpret the Analysis Results

Read the generated JSON report and interpret the findings for the user in plain language.

Focus on: 1. Current State: What the data shows about business performance 2. Weak Areas: Specific problems identified with severity levels 3. Root Causes: Why these issues exist (use business frameworks from references/) 4. Impact: What these weaknesses mean for the business

Example interpretation:

Based on the analysis of your sales data from January to December 2024:

Current State:
- Total revenue: $1.2M with average monthly revenue of $100K
- Average growth rate: -3.5% indicating declining performance
- Revenue stability: High volatility (CV: 58%) suggesting inconsistent performance

Weak Areas Identified:
1. Revenue Growth (High Severity): Negative average growth rate of -3.5%
2. Performance Consistency (Medium Severity): 45% of periods show declining performance
3. Category Performance (Medium Severity): 4 underperforming categories identified

Step 4: Generate Detailed Recommendations

Consult the business frameworks reference to provide strategic recommendations:

Load business frameworks for context: Refer to references/business_frameworks.md for:

Revenue growth strategies (market penetration, product development, etc.)
Operational excellence frameworks
Customer-centric strategies
Pricing strategy frameworks
Common weak area solutions

Structure recommendations as:

For each identified weak area, provide: 1. Strategic Initiative Name: Clear, actionable program name 2. Objective: What this strategy aims to achieve 3. Key Actions: 3-5 specific, prioritized steps 4. Expected Impact: High/Medium/Low 5. Timeline: Realistic implementation timeframe 6. Success Metrics: How to measure improvement

Example recommendation:

Strategy: Revenue Acceleration Program
Area: Revenue Growth
Objective: Reverse negative growth trend and achieve 10%+ monthly growth

Key Actions:
1. Implement aggressive customer acquisition campaigns
2. Review and optimize pricing strategy
3. Launch upselling and cross-selling initiatives
4. Expand into new market segments or geographies
5. Accelerate product development and innovation

Expected Impact: High
Timeline: 3-6 months
Success Metrics: Monthly revenue growth rate, new customer acquisition, ARPU increase

Step 5: Create Visualizations (Optional)

If requested, create interactive visualizations using Plotly to illustrate findings:

Consult visualization guide: Refer to references/visualization_guide.md for:

Recommended chart types for different analyses
Code examples for creating charts
Best practices for business dashboards

Common visualizations to create: 1. Revenue Trend Chart: Line chart showing revenue over time with growth rate overlay 2. Category Performance: Bar chart sorted by revenue contribution 3. Volatility Analysis: Box plot or standard deviation visualization 4. Weak Areas Heatmap: Visual representation of severity and impact

Example code for revenue trend:

import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add revenue line
fig.add_trace(
    go.Scatter(x=df['date'], y=df['revenue'], name="Revenue",
               line=dict(color='blue', width=3)),
    secondary_y=False
)

# Add growth rate line
fig.add_trace(
    go.Scatter(x=df['date'], y=df['growth_rate'], name="Growth Rate",
               line=dict(color='green', dash='dash')),
    secondary_y=True
)

fig.update_layout(title_text="Revenue Performance & Growth Rate")
fig.show()

Step 6: Generate Final Report

Compile findings into a comprehensive report format.

Option A: Generate HTML Report

Use the report template from assets/report_template.html:

# Read the template
with open('assets/report_template.html', 'r') as f:
    template = f.read()

# Load analysis results
with open('output_report.json', 'r') as f:
    analysis = json.load(f)

# Populate the template with actual data
# Replace placeholders with real values from analysis
# Add Plotly charts as JavaScript
# Save as final HTML report

with open('business_report.html', 'w') as f:
    f.write(populated_template)

The HTML template includes:

Executive summary with key metrics
Interactive charts for trends and categories
Styled weak area cards with severity indicators
Strategic recommendations with action items
Professional styling and print-ready format

Option B: Generate Markdown Report

Create a structured markdown document:

# Business Performance Analysis Report

**Generated:** [Date]
**Data Period:** [Period]

## Executive Summary

[Brief overview of findings]

## Key Metrics

- Total Revenue: $X
- Average Growth Rate: X%
- Revenue Stability: [Assessment]
- Weak Areas Identified: X

## Performance Trends

[Insert chart or describe trends]

## Areas of Weakness

### 1. [Weak Area Name] (Severity)
**Finding:** [Description]
**Impact:** [Business impact]

### 2. [Next weak area...]

## Strategic Recommendations

### Strategy 1: [Name]
**Objective:** [Goal]
**Actions:**
- [Action 1]
- [Action 2]
...

**Expected Impact:** High/Medium/Low
**Timeline:** X months

Key Analysis Metrics

The analysis script calculates the following metrics automatically:

Growth Analysis

Average Growth Rate: Period-over-period revenue change percentage
Declining Period Count: Number of periods with negative growth
Trend Direction: Overall trajectory (growing, declining, stable)

Stability Analysis

Coefficient of Variation (CV): Measures revenue volatility
CV < 25%: Stable performance
CV 25-50%: Moderate volatility
CV > 50%: High volatility (flag as weak area)

Category Performance

Revenue Contribution: Percentage breakdown by category
Underperforming Categories: Bottom 25% by average performance
Top/Bottom Performers: Best and worst performing categories

Statistical Indicators

Mean, Median, Standard Deviation for all numeric columns
Min/Max values and ranges
Total aggregates

Business Frameworks Reference

When generating recommendations, leverage the frameworks documented in references/business_frameworks.md:

1. Revenue Growth Strategies: Market penetration, product development, market development, diversification 2. Operational Excellence: Process optimization, resource allocation, quality management 3. Customer-Centric Strategies: Retention programs, CLV optimization, segmentation 4. Pricing Strategies: Value-based, dynamic, competitive pricing 5. Data-Driven Decision Making: Analytics maturity model, KPI frameworks

Match identified weak areas with appropriate strategic frameworks to provide contextually relevant recommendations.

Tips for Effective Reports

1. Start with the Big Picture: Lead with overall performance and key findings 2. Prioritize by Severity: Focus on high-severity issues first 3. Be Specific: Provide concrete numbers and percentages, not vague assessments 4. Action-Oriented: Every weak area should have actionable recommendations 5. Context Matters: Consider industry benchmarks and business context 6. Visual Communication: Use charts to make trends immediately clear 7. Executive-Friendly: Structure for quick scanning with clear headers and summaries

Common Weak Areas and Detection

The analysis automatically detects these common business problems:

Weak Area	Detection Criteria	Typical Root Causes
Revenue Growth	Negative average growth rate	Market saturation, increased competition, poor positioning
Performance Consistency	>40% declining periods	Lack of recurring revenue, seasonal dependency
Revenue Stability	CV > 50%	Customer concentration, volatile demand
Category Performance	Categories in bottom 25%	Poor product-market fit, pricing issues, low awareness

Example Usage

User request: "Analyze my Q4 sales data and tell me where we're weak and how to improve"

Workflow: 1. Load the CSV: df = pd.read_csv('q4_sales.csv') 2. Run analysis: python scripts/analyze_business_data.py q4_sales.csv q4_report.json 3. Read results: with open('q4_report.json') as f: report = json.load(f) 4. Interpret findings for the user in natural language 5. Create visualizations using Plotly (refer to references/visualization_guide.md) 6. Generate HTML report using assets/report_template.html 7. Provide strategic recommendations using references/business_frameworks.md

Expected output:

Clear explanation of current business performance
3-5 identified weak areas with severity levels
4-6 strategic initiatives with specific action plans
Interactive visualizations (if requested)
Professional HTML or markdown report

Resources

scripts/

analyze_business_data.py: Automated analysis engine that detects data structure, calculates metrics, identifies weak areas, and generates improvement strategies

references/

business_frameworks.md: Comprehensive guide to business strategy frameworks, common weak areas, and solution templates
visualization_guide.md: Chart type recommendations, Plotly code examples, and dashboard design best practices

assets/

report_template.html: Professional HTML template with interactive visualizations, styled cards for weak areas and strategies, and print-ready formatting

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Business Performance Analysis Report</title>
    <script src="https://cdn.plot.ly/plotly-2.27.0.min.js"></script>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        body {
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
            line-height: 1.6;
            color: #333;
            background: #f5f7fa;
        }

        .container {
            max-width: 1400px;
            margin: 0 auto;
            padding: 20px;
        }

        .header {
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            padding: 40px;
            border-radius: 10px;
            margin-bottom: 30px;
            box-shadow: 0 4px 6px rgba(0,0,0,0.1);
        }

        .header h1 {
            font-size: 2.5em;
            margin-bottom: 10px;
        }

        .header .subtitle {
            font-size: 1.1em;
            opacity: 0.9;
        }

        .metrics-grid {
            display: grid;
            grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
            gap: 20px;
            margin-bottom: 30px;
        }

        .metric-card {
            background: white;
            padding: 25px;
            border-radius: 10px;
            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
            transition: transform 0.2s, box-shadow 0.2s;
        }

        .metric-card:hover {
            transform: translateY(-5px);
            box-shadow: 0 4px 12px rgba(0,0,0,0.15);
        }

        .metric-label {
            font-size: 0.9em;
            color: #666;
            text-transform: uppercase;
            letter-spacing: 0.5px;
            margin-bottom: 10px;
        }

        .metric-value {
            font-size: 2.2em;
            font-weight: bold;
            color: #2c3e50;
            margin-bottom: 5px;
        }

        .metric-change {
            font-size: 0.9em;
            padding: 4px 8px;
            border-radius: 4px;
            display: inline-block;
        }

        .metric-change.positive {
            background: #d4edda;
            color: #155724;
        }

        .metric-change.negative {
            background: #f8d7da;
            color: #721c24;
        }

        .metric-change.neutral {
            background: #e2e3e5;
            color: #383d41;
        }

        .section {
            background: white;
            padding: 30px;
            border-radius: 10px;
            margin-bottom: 30px;
            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
        }

        .section h2 {
            color: #2c3e50;
            font-size: 1.8em;
            margin-bottom: 20px;
            padding-bottom: 10px;
            border-bottom: 3px solid #667eea;
        }

        .chart-container {
            margin: 20px 0;
            min-height: 400px;
        }

        .weak-areas-grid {
            display: grid;
            gap: 20px;
            margin-top: 20px;
        }

        .weak-area-card {
            background: #fff5f5;
            border-left: 5px solid #e53e3e;
            padding: 20px;
            border-radius: 5px;
            transition: transform 0.2s;
        }

        .weak-area-card:hover {
            transform: translateX(5px);
        }

        .weak-area-header {
            display: flex;
            justify-content: space-between;
            align-items: center;
            margin-bottom: 10px;
        }

        .weak-area-title {
            font-size: 1.3em;
            font-weight: bold;
            color: #c53030;
        }

        .severity-badge {
            padding: 5px 12px;
            border-radius: 20px;
            font-size: 0.85em;
            font-weight: bold;
            text-transform: uppercase;
        }

        .severity-high {
            background: #fc8181;
            color: #742a2a;
        }

        .severity-medium {
            background: #fbd38d;
            color: #7c2d12;
        }

        .severity-low {
            background: #fed7d7;
            color: #9c4221;
        }

        .weak-area-finding {
            color: #555;
            margin: 10px 0;
            line-height: 1.8;
        }

        .strategies-grid {
            display: grid;
            gap: 25px;
            margin-top: 20px;
        }

        .strategy-card {
            background: #f0f9ff;
            border-left: 5px solid #3182ce;
            padding: 25px;
            border-radius: 5px;
        }

        .strategy-header {
            margin-bottom: 15px;
        }

        .strategy-title {
            font-size: 1.4em;
            font-weight: bold;
            color: #2c5282;
            margin-bottom: 5px;
        }

        .strategy-area {
            color: #4299e1;
            font-weight: 600;
        }

        .strategy-actions {
            margin: 15px 0;
        }

        .strategy-actions ul {
            list-style: none;
            padding-left: 0;
        }

        .strategy-actions li {
            padding: 8px 0;
            padding-left: 25px;
            position: relative;
            color: #2d3748;
        }

        .strategy-actions li:before {
            content: "→";
            position: absolute;
            left: 0;
            color: #3182ce;
            font-weight: bold;
        }

        .strategy-meta {
            display: flex;
            gap: 20px;
            margin-top: 15px;
            padding-top: 15px;
            border-top: 1px solid #bee3f8;
            font-size: 0.9em;
        }

        .strategy-meta-item {
            display: flex;
            align-items: center;
            gap: 5px;
        }

        .strategy-meta-label {
            font-weight: 600;
            color: #2c5282;
        }

        .impact-high {
            color: #38a169;
            font-weight: bold;
        }

        .impact-medium {
            color: #d69e2e;
            font-weight: bold;
        }

        .footer {
            text-align: center;
            padding: 30px;
            color: #666;
            font-size: 0.9em;
        }

        @media print {
            .header {
                background: #667eea;
                -webkit-print-color-adjust: exact;
                print-color-adjust: exact;
            }
            .metric-card, .section {
                page-break-inside: avoid;
            }
        }
    </style>
</head>
<body>
    <div class="container">
        <!-- Header -->
        <div class="header">
            <h1>Business Performance Analysis Report</h1>
            <div class="subtitle">
                <span id="report-date">Generated: [DATE]</span> |
                <span id="data-period">Data Period: [PERIOD]</span>
            </div>
        </div>

        <!-- Key Metrics -->
        <div class="metrics-grid">
            <div class="metric-card">
                <div class="metric-label">Total Revenue</div>
                <div class="metric-value" id="total-revenue">[VALUE]</div>
                <span class="metric-change positive" id="revenue-change">[CHANGE]</span>
            </div>

            <div class="metric-card">
                <div class="metric-label">Average Growth Rate</div>
                <div class="metric-value" id="growth-rate">[VALUE]</div>
                <span class="metric-change neutral" id="growth-status">[STATUS]</span>
            </div>

            <div class="metric-card">
                <div class="metric-label">Revenue Stability</div>
                <div class="metric-value" id="stability">[VALUE]</div>
                <span class="metric-change neutral" id="stability-status">[STATUS]</span>
            </div>

            <div class="metric-card">
                <div class="metric-label">Weak Areas Identified</div>
                <div class="metric-value" id="weak-areas-count">[COUNT]</div>
                <span class="metric-change negative" id="areas-status">Requires Attention</span>
            </div>
        </div>

        <!-- Performance Trends Section -->
        <div class="section">
            <h2>📈 Performance Trends</h2>
            <div class="chart-container" id="trend-chart"></div>
        </div>

        <!-- Category Performance Section -->
        <div class="section">
            <h2>📊 Category Performance</h2>
            <div class="chart-container" id="category-chart"></div>
        </div>

        <!-- Weak Areas Section -->
        <div class="section">
            <h2>⚠️ Areas of Weakness</h2>
            <p style="color: #666; margin-bottom: 20px;">
                The following areas have been identified as requiring immediate attention based on data analysis:
            </p>
            <div class="weak-areas-grid" id="weak-areas-container">
                <!-- Weak areas will be inserted here -->
            </div>
        </div>

        <!-- Improvement Strategies Section -->
        <div class="section">
            <h2>💡 Improvement Strategies</h2>
            <p style="color: #666; margin-bottom: 20px;">
                Recommended strategic initiatives to address identified weaknesses and drive business growth:
            </p>
            <div class="strategies-grid" id="strategies-container">
                <!-- Strategies will be inserted here -->
            </div>
        </div>

        <!-- Data Insights Section -->
        <div class="section">
            <h2>🔍 Statistical Insights</h2>
            <div id="insights-content">
                <!-- Statistical insights will be inserted here -->
            </div>
        </div>

        <!-- Footer -->
        <div class="footer">
            <p>This report was generated using automated business intelligence analysis.</p>
            <p>For questions or clarifications, please contact your analytics team.</p>
        </div>
    </div>

    <script>
        // Placeholder JavaScript - This will be populated with actual data from the analysis

        // Example: How to populate weak areas dynamically
        function renderWeakAreas(weakAreas) {
            const container = document.getElementById('weak-areas-container');
            container.innerHTML = weakAreas.map(area => `
                <div class="weak-area-card">
                    <div class="weak-area-header">
                        <div class="weak-area-title">${area.area}</div>
                        <span class="severity-badge severity-${area.severity.toLowerCase()}">${area.severity}</span>
                    </div>
                    <div class="weak-area-finding">${area.finding}</div>
                    ${area.details ? `<div style="margin-top: 10px; font-size: 0.9em; color: #666;">
                        <strong>Affected areas:</strong> ${area.details.join(', ')}
                    </div>` : ''}
                </div>
            `).join('');
        }

        // Example: How to populate strategies dynamically
        function renderStrategies(strategies) {
            const container = document.getElementById('strategies-container');
            container.innerHTML = strategies.map(strategy => `
                <div class="strategy-card">
                    <div class="strategy-header">
                        <div class="strategy-area">${strategy.area}</div>
                        <div class="strategy-title">${strategy.strategy}</div>
                    </div>
                    <div class="strategy-actions">
                        <strong>Key Actions:</strong>
                        <ul>
                            ${strategy.actions.map(action => `<li>${action}</li>`).join('')}
                        </ul>
                    </div>
                    <div class="strategy-meta">
                        <div class="strategy-meta-item">
                            <span class="strategy-meta-label">Expected Impact:</span>
                            <span class="impact-${strategy.expected_impact.toLowerCase()}">${strategy.expected_impact}</span>
                        </div>
                        <div class="strategy-meta-item">
                            <span class="strategy-meta-label">Timeline:</span>
                            <span>${strategy.timeline}</span>
                        </div>
                    </div>
                </div>
            `).join('');
        }

        // Example: Creating a simple trend chart with Plotly
        function createTrendChart(dates, values) {
            const trace = {
                x: dates,
                y: values,
                type: 'scatter',
                mode: 'lines+markers',
                line: {color: '#667eea', width: 3},
                marker: {size: 8}
            };

            const layout = {
                title: 'Revenue Trend Over Time',
                xaxis: {title: 'Date'},
                yaxis: {title: 'Revenue ($)'},
                hovermode: 'closest'
            };

            Plotly.newPlot('trend-chart', [trace], layout, {responsive: true});
        }

        // Initialize with sample data (replace with actual data from analysis)
        document.addEventListener('DOMContentLoaded', function() {
            // Set report date
            document.getElementById('report-date').textContent = 'Generated: ' + new Date().toLocaleDateString();

            // This is where you would load actual analysis results
            // For example, from a JSON file generated by analyze_business_data.py
        });
    </script>
</body>
</html>

export default async function business_analytics_reporter(input) {
  console.log("🧠 Running skill: business-analytics-reporter");
  
  // TODO: implement actual logic for this skill
  return {
    message: "Skill 'business-analytics-reporter' executed successfully!",
    input
  };
}

{
  "name": "@ai-labs-claude-skills/business-analytics-reporter",
  "version": "1.0.0",
  "description": "Claude AI skill: business-analytics-reporter",
  "main": "index.js",
  "files": [
    "."
  ],
  "license": "MIT",
  "author": "AI Labs"
}

Business Analysis Frameworks and Metrics

Key Business Metrics for Sales & Revenue Analysis

Revenue Metrics

Total Revenue: Sum of all sales over a period
Average Revenue Per Unit (ARPU): Total revenue / Number of customers or units
Revenue Growth Rate: ((Current Period Revenue - Previous Period Revenue) / Previous Period Revenue) × 100
Month-over-Month (MoM) Growth: Short-term growth trend
Year-over-Year (YoY) Growth: Long-term growth comparison

Performance Indicators

Sales Velocity: Rate at which deals move through the pipeline
Conversion Rate: (Successful conversions / Total opportunities) × 100
Average Order Value (AOV): Total revenue / Number of orders
Revenue Per Category: Performance breakdown by product/service line

Stability & Risk Metrics

Coefficient of Variation (CV): (Standard Deviation / Mean) × 100
CV < 25%: Stable performance
CV 25-50%: Moderate volatility
CV > 50%: High volatility (risk area)
Revenue Concentration: Percentage of revenue from top customers/categories
Seasonality Index: Measure of seasonal patterns

Business Improvement Frameworks

1. Revenue Growth Strategies

Market Penetration

Increase market share in existing markets
Aggressive marketing and sales campaigns
Competitive pricing strategies
Customer acquisition programs

Product Development

Launch new products/services
Enhance existing offerings
Innovation and R&D investment
Feature expansion based on customer feedback

Market Development

Enter new geographic markets
Target new customer segments
Expand distribution channels
Strategic partnerships

Diversification

Add complementary products/services
Enter related industries
Vertical or horizontal integration
Risk mitigation through variety

2. Operational Excellence Framework

Process Optimization

Streamline sales processes
Reduce cycle times
Automate repetitive tasks
Eliminate bottlenecks

Resource Allocation

Focus on high-performing categories
Divest or restructure underperformers
Optimize inventory management
Strategic capacity planning

Quality Management

Improve product/service quality
Reduce defect rates
Customer satisfaction initiatives
Continuous improvement culture

3. Customer-Centric Strategies

Retention & Loyalty

Customer loyalty programs
Personalized experiences
Proactive customer service
Regular engagement initiatives

Customer Lifetime Value (CLV) Optimization

Increase purchase frequency
Upselling and cross-selling
Premium tier development
Reduce churn rate

Customer Segmentation

High-value customer focus
Targeted marketing campaigns
Customized offerings per segment
Resource prioritization

4. Pricing Strategy Framework

Value-Based Pricing

Price based on customer perceived value
Premium positioning for quality
Bundle offerings for value perception

Dynamic Pricing

Adjust prices based on demand
Seasonal pricing strategies
Promotional pricing tactics

Competitive Pricing

Market rate alignment
Penetration pricing for new markets
Price leadership strategy

5. Data-Driven Decision Making

Analytics Maturity Model

1. Descriptive: What happened? (Historical reporting) 2. Diagnostic: Why did it happen? (Root cause analysis) 3. Predictive: What will happen? (Forecasting) 4. Prescriptive: What should we do? (Recommendations)

Key Performance Indicators (KPI) Framework

Leading Indicators: Predict future performance (pipeline, leads)
Lagging Indicators: Measure past results (revenue, profit)
Balanced Scorecard: Financial, customer, internal process, learning & growth

Common Business Weak Areas and Solutions

Area: Declining Revenue

Symptoms: Negative growth rate, decreasing order volume Root Causes: Market saturation, increased competition, product obsolescence, poor positioning Solutions:

Conduct market research to identify opportunities
Refresh product offerings
Enhance value proposition
Improve marketing effectiveness
Explore new markets or segments

Area: High Volatility

Symptoms: Inconsistent revenue, unpredictable performance Root Causes: Lack of recurring revenue, seasonal dependency, customer concentration Solutions:

Develop subscription or contract-based models
Diversify customer base
Create counter-seasonal products
Build strategic partnerships
Implement demand forecasting

Area: Category Underperformance

Symptoms: Low contribution from certain products/categories Root Causes: Poor product-market fit, pricing issues, low awareness, quality problems Solutions:

Conduct category review analysis
Test price adjustments
Invest in targeted marketing
Consider product discontinuation
Reallocate resources to winners

Area: Low Profit Margins

Symptoms: High revenue but low profitability Root Causes: High costs, pricing pressure, inefficient operations Solutions:

Cost optimization initiatives
Negotiate better supplier terms
Improve operational efficiency
Value-based pricing strategy
Focus on high-margin segments

Area: Customer Churn

Symptoms: High customer turnover rate Root Causes: Poor customer experience, lack of engagement, competitive alternatives Solutions:

Implement customer success programs
Regular customer feedback loops
Loyalty and retention programs
Improve product quality and support
Personalized customer engagement

Strategic Recommendation Template

For Each Weak Area Identified:

1. Current State Assessment

What the data shows
Severity and business impact
Trend direction

2. Root Cause Analysis

Why this weakness exists
Contributing factors
Internal vs. external causes

3. Strategic Initiative

Named strategy or program
Clear objective
Expected outcomes

4. Tactical Actions

Specific, actionable steps
Prioritized by impact
Resource requirements

5. Success Metrics

How to measure improvement
Target benchmarks
Timeline for results

6. Implementation Roadmap

Phase 1: Quick wins (0-3 months)
Phase 2: Medium-term initiatives (3-6 months)
Phase 3: Long-term transformation (6-12+ months)

Data Visualization Guide for Business Reports

Recommended Chart Types for Business Analysis

Revenue & Sales Analysis

Line Charts

Use for: Trends over time, growth analysis Best for: Revenue trends, sales performance, growth rates Example: Monthly revenue over the past year

Bar Charts

Use for: Category comparisons, rankings Best for: Revenue by product, sales by region, performance rankings Example: Top 10 products by revenue

Stacked Bar Charts

Use for: Part-to-whole comparisons over categories Best for: Revenue composition, segment contribution Example: Revenue breakdown by product category per quarter

Performance Analysis

Area Charts

Use for: Cumulative trends, volume over time Best for: Cumulative revenue, market share evolution Example: Year-to-date revenue accumulation

Waterfall Charts

Use for: Sequential changes, breakdowns Best for: Revenue bridges, variance analysis Example: How revenue changed from Q1 to Q2

Scatter Plots

Use for: Correlation analysis, outlier detection Best for: Price vs. volume, cost vs. revenue relationships Example: Product performance matrix (volume vs. margin)

Composition & Distribution

Pie/Donut Charts

Use for: Part-to-whole relationships (limit to 5-7 segments) Best for: Market share, revenue composition Example: Percentage contribution by product line

Treemap

Use for: Hierarchical part-to-whole with many categories Best for: Revenue contribution with subcategories Example: Revenue by region, then by product

Box Plots

Use for: Distribution analysis, variability Best for: Revenue distribution, outlier identification Example: Monthly sales distribution over a year

Comparison Analysis

Grouped Bar Charts

Use for: Multiple metrics across categories Best for: Year-over-year comparisons, before/after analysis Example: 2024 vs 2025 revenue by quarter

Heatmaps

Use for: Pattern identification, correlation matrices Best for: Performance by time period and category Example: Sales performance by day of week and hour

Bullet Charts

Use for: Target vs. actual performance Best for: KPI dashboards, goal tracking Example: Revenue vs. target with performance bands

Python Visualization Libraries

Plotly (Recommended for Interactive Reports)

import plotly.graph_objects as go
import plotly.express as px

# Interactive line chart
fig = px.line(df, x='date', y='revenue', title='Revenue Trend')
fig.show()

# Interactive bar chart
fig = px.bar(df, x='category', y='sales', color='region')
fig.show()

Matplotlib + Seaborn (For Static Reports)

import matplotlib.pyplot as plt
import seaborn as sns

# Simple line plot
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['revenue'])
plt.title('Revenue Over Time')
plt.xlabel('Date')
plt.ylabel('Revenue ($)')
plt.show()

# Seaborn bar plot
sns.barplot(data=df, x='category', y='sales')
plt.show()

Visualization Best Practices

Color Usage

Consistent color scheme: Use same colors for same categories across charts
Highlight important data: Use accent colors for key insights
Accessibility: Ensure color-blind friendly palettes
Professional palette: Stick to 3-5 primary colors

Recommended color schemes:

Growth/Positive: Green shades
Decline/Negative: Red shades
Neutral/Baseline: Gray or blue shades
Categories: Distinct, contrasting colors

Chart Design Principles

1. Clarity: Remove chartjunk, keep it simple 2. Context: Always label axes, add titles and legends 3. Scale: Start y-axis at zero for bar charts (unless showing variance) 4. Sorting: Order categories by value (descending) for better readability 5. Annotations: Highlight key data points with labels or callouts

Dashboard Layout

For comprehensive business reports, organize visualizations:

1. Executive Summary Section

Key metric cards (total revenue, growth rate, etc.)
Primary trend chart (overall performance)

2. Deep Dive Analysis

Category breakdown charts
Time-based analysis
Comparison charts

3. Insights & Weak Areas

Highlight charts showing problem areas
Performance distribution
Outlier identification

4. Recommendations

Goal vs. actual charts
Projected impact visualizations

Chart Creation Examples

Example 1: Revenue Trend with Growth Rate

import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add revenue line
fig.add_trace(
    go.Scatter(x=df['date'], y=df['revenue'], name="Revenue", line=dict(color='blue', width=3)),
    secondary_y=False
)

# Add growth rate line
fig.add_trace(
    go.Scatter(x=df['date'], y=df['growth_rate'], name="Growth Rate", line=dict(color='green', dash='dash')),
    secondary_y=True
)

# Update axes
fig.update_xaxes(title_text="Date")
fig.update_yaxes(title_text="Revenue ($)", secondary_y=False)
fig.update_yaxes(title_text="Growth Rate (%)", secondary_y=True)

fig.update_layout(title_text="Revenue Performance & Growth Rate")

Example 2: Category Performance Comparison

import plotly.express as px

# Sort by revenue descending
df_sorted = df.sort_values('revenue', ascending=False)

# Create bar chart
fig = px.bar(
    df_sorted,
    x='category',
    y='revenue',
    color='performance_tier',  # e.g., 'High', 'Medium', 'Low'
    title='Revenue by Category',
    labels={'revenue': 'Revenue ($)', 'category': 'Product Category'},
    color_discrete_map={'High': 'green', 'Medium': 'orange', 'Low': 'red'}
)

fig.update_layout(xaxis_tickangle=-45)

Example 3: Variance Analysis (Waterfall)

import plotly.graph_objects as go

# Example: Q1 to Q2 revenue bridge
fig = go.Figure(go.Waterfall(
    x=['Q1 Revenue', 'New Sales', 'Churn', 'Price Increase', 'Q2 Revenue'],
    y=[1000, 300, -150, 100, 1250],
    measure=['absolute', 'relative', 'relative', 'relative', 'total'],
    text=['+1000', '+300', '-150', '+100', '1250'],
    textposition='outside',
    connector={'line': {'color': 'rgb(63, 63, 63)'}},
))

fig.update_layout(title='Q1 to Q2 Revenue Bridge', yaxis_title='Revenue ($1000s)')

HTML Report Template Structure

<!DOCTYPE html>
<html>
<head>
    <title>Business Analysis Report</title>
    <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
    <style>
        body { font-family: Arial, sans-serif; max-width: 1200px; margin: 0 auto; padding: 20px; }
        .header { background: #2c3e50; color: white; padding: 20px; margin-bottom: 30px; }
        .section { margin-bottom: 40px; }
        .metric-card { display: inline-block; background: #ecf0f1; padding: 20px; margin: 10px; border-radius: 5px; }
        .weak-area { background: #ffe6e6; padding: 15px; margin: 10px 0; border-left: 4px solid #e74c3c; }
        .strategy { background: #e6f7ff; padding: 15px; margin: 10px 0; border-left: 4px solid #3498db; }
    </style>
</head>
<body>
    <div class="header">
        <h1>Business Performance Analysis Report</h1>
        <p>Generated: [Date]</p>
    </div>

    <div class="section">
        <h2>Executive Summary</h2>
        <!-- Metric cards -->
    </div>

    <div class="section">
        <h2>Performance Trends</h2>
        <div id="trend-chart"></div>
    </div>

    <div class="section">
        <h2>Areas of Weakness</h2>
        <!-- Weak area cards with charts -->
    </div>

    <div class="section">
        <h2>Improvement Strategies</h2>
        <!-- Strategy recommendations -->
    </div>

    <script>
        // Plotly chart JavaScript
    </script>
</body>
</html>

Integration with Analysis Script

When using the analyze_business_data.py script, create visualizations for:

1. Revenue Trend Chart: Line chart showing revenue over time with growth rate overlay 2. Category Performance Chart: Bar chart of revenue by category, sorted by performance 3. Volatility Chart: Box plot or standard deviation chart showing revenue stability 4. Weak Areas Heatmap: Visual representation of severity and impact 5. Strategy Impact Projection: Chart showing expected improvement trajectories

#!/usr/bin/env python3
"""
Business Data Analysis and Report Generator

This script analyzes business sales and revenue data from CSV files and generates
comprehensive reports with insights on areas of weakness and improvement strategies.
"""

import pandas as pd
import numpy as np
import json
import sys
from datetime import datetime
from pathlib import Path


def load_and_validate_data(csv_path):
    """Load CSV data and perform basic validation."""
    try:
        df = pd.read_csv(csv_path)
        print(f"✓ Loaded data: {len(df)} rows, {len(df.columns)} columns")
        return df
    except Exception as e:
        print(f"✗ Error loading CSV: {e}", file=sys.stderr)
        sys.exit(1)


def detect_data_structure(df):
    """Detect what type of business data we're working with."""
    columns_lower = [col.lower() for col in df.columns]

    data_type = {
        'has_revenue': any(key in ' '.join(columns_lower) for key in ['revenue', 'sales', 'amount', 'price', 'total']),
        'has_date': any(key in ' '.join(columns_lower) for key in ['date', 'time', 'month', 'year', 'period']),
        'has_category': any(key in ' '.join(columns_lower) for key in ['category', 'product', 'region', 'department', 'type']),
        'has_quantity': any(key in ' '.join(columns_lower) for key in ['quantity', 'units', 'count', 'volume']),
        'has_customer': any(key in ' '.join(columns_lower) for key in ['customer', 'client', 'user'])
    }

    return data_type


def calculate_basic_stats(df, revenue_col=None):
    """Calculate basic statistical metrics."""
    stats = {
        'total_rows': len(df),
        'date_range': None,
        'basic_stats': {}
    }

    # Detect date column
    date_cols = [col for col in df.columns if any(x in col.lower() for x in ['date', 'time'])]
    if date_cols:
        try:
            df[date_cols[0]] = pd.to_datetime(df[date_cols[0]], errors='coerce')
            stats['date_range'] = {
                'start': df[date_cols[0]].min().strftime('%Y-%m-%d') if pd.notna(df[date_cols[0]].min()) else None,
                'end': df[date_cols[0]].max().strftime('%Y-%m-%d') if pd.notna(df[date_cols[0]].max()) else None
            }
        except:
            pass

    # Calculate stats for numeric columns
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    for col in numeric_cols:
        stats['basic_stats'][col] = {
            'mean': float(df[col].mean()),
            'median': float(df[col].median()),
            'std': float(df[col].std()),
            'min': float(df[col].min()),
            'max': float(df[col].max()),
            'total': float(df[col].sum())
        }

    return stats


def identify_revenue_column(df):
    """Automatically identify the revenue/sales column."""
    revenue_keywords = ['revenue', 'sales', 'amount', 'total', 'price', 'value']

    for col in df.columns:
        if any(keyword in col.lower() for keyword in revenue_keywords):
            if df[col].dtype in [np.float64, np.int64]:
                return col

    # Fallback: return first numeric column
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    return numeric_cols[0] if len(numeric_cols) > 0 else None


def analyze_trends(df, date_col=None, value_col=None):
    """Analyze trends over time."""
    if date_col is None or value_col is None:
        return None

    try:
        df_sorted = df.sort_values(date_col)

        # Calculate period-over-period growth
        df_sorted['prev_value'] = df_sorted[value_col].shift(1)
        df_sorted['growth_rate'] = ((df_sorted[value_col] - df_sorted['prev_value']) / df_sorted['prev_value'] * 100)

        avg_growth = df_sorted['growth_rate'].mean()

        # Identify declining periods
        declining_periods = df_sorted[df_sorted['growth_rate'] < 0]

        return {
            'average_growth_rate': float(avg_growth) if pd.notna(avg_growth) else 0,
            'declining_period_count': len(declining_periods),
            'declining_percentage': (len(declining_periods) / len(df_sorted) * 100) if len(df_sorted) > 0 else 0
        }
    except:
        return None


def analyze_categories(df, category_col=None, value_col=None):
    """Analyze performance by category."""
    if category_col is None or value_col is None:
        return None

    try:
        category_stats = df.groupby(category_col)[value_col].agg([
            ('total', 'sum'),
            ('average', 'mean'),
            ('count', 'count')
        ]).round(2)

        # Calculate percentage contribution
        category_stats['percentage'] = (category_stats['total'] / category_stats['total'].sum() * 100).round(2)

        # Identify underperforming categories (bottom 25%)
        threshold = category_stats['average'].quantile(0.25)
        underperforming = category_stats[category_stats['average'] <= threshold]

        return {
            'categories': category_stats.to_dict('index'),
            'underperforming_categories': list(underperforming.index),
            'top_category': category_stats['total'].idxmax(),
            'bottom_category': category_stats['total'].idxmin()
        }
    except:
        return None


def calculate_variability(df, value_col):
    """Calculate coefficient of variation to assess stability."""
    try:
        mean_val = df[value_col].mean()
        std_val = df[value_col].std()
        cv = (std_val / mean_val * 100) if mean_val != 0 else 0

        return {
            'coefficient_of_variation': float(cv),
            'stability_assessment': 'High volatility' if cv > 50 else 'Moderate volatility' if cv > 25 else 'Stable'
        }
    except:
        return None


def generate_analysis_report(df):
    """Generate comprehensive analysis report."""
    report = {
        'metadata': {
            'analysis_date': datetime.now().isoformat(),
            'data_shape': {'rows': len(df), 'columns': len(df.columns)},
            'columns': list(df.columns)
        },
        'data_structure': detect_data_structure(df),
        'findings': {}
    }

    # Identify key columns
    revenue_col = identify_revenue_column(df)
    date_cols = [col for col in df.columns if any(x in col.lower() for x in ['date', 'time'])]
    date_col = date_cols[0] if date_cols else None
    category_cols = [col for col in df.columns if any(x in col.lower() for x in ['category', 'product', 'region', 'type', 'department'])]
    category_col = category_cols[0] if category_cols else None

    report['key_columns'] = {
        'revenue_column': revenue_col,
        'date_column': date_col,
        'category_column': category_col
    }

    # Basic statistics
    report['findings']['basic_statistics'] = calculate_basic_stats(df, revenue_col)

    # Trend analysis
    if date_col and revenue_col:
        try:
            df[date_col] = pd.to_datetime(df[date_col], errors='coerce')
            report['findings']['trend_analysis'] = analyze_trends(df, date_col, revenue_col)
        except:
            report['findings']['trend_analysis'] = None

    # Category analysis
    if category_col and revenue_col:
        report['findings']['category_analysis'] = analyze_categories(df, category_col, revenue_col)

    # Variability analysis
    if revenue_col:
        report['findings']['variability'] = calculate_variability(df, revenue_col)

    return report


def identify_weak_areas(report):
    """Identify areas where the business is lacking."""
    weak_areas = []

    # Check trend analysis
    if report['findings'].get('trend_analysis'):
        trend = report['findings']['trend_analysis']
        if trend['average_growth_rate'] < 0:
            weak_areas.append({
                'area': 'Revenue Growth',
                'severity': 'High',
                'finding': f"Negative average growth rate of {trend['average_growth_rate']:.2f}%",
                'metric': trend['average_growth_rate']
            })

        if trend['declining_percentage'] > 40:
            weak_areas.append({
                'area': 'Performance Consistency',
                'severity': 'Medium',
                'finding': f"{trend['declining_percentage']:.1f}% of periods show declining performance",
                'metric': trend['declining_percentage']
            })

    # Check variability
    if report['findings'].get('variability'):
        variability = report['findings']['variability']
        if variability['coefficient_of_variation'] > 50:
            weak_areas.append({
                'area': 'Revenue Stability',
                'severity': 'Medium',
                'finding': f"High volatility detected (CV: {variability['coefficient_of_variation']:.1f}%)",
                'metric': variability['coefficient_of_variation']
            })

    # Check category performance
    if report['findings'].get('category_analysis'):
        cat_analysis = report['findings']['category_analysis']
        if cat_analysis['underperforming_categories']:
            weak_areas.append({
                'area': 'Category Performance',
                'severity': 'Medium',
                'finding': f"{len(cat_analysis['underperforming_categories'])} underperforming categories identified",
                'details': cat_analysis['underperforming_categories']
            })

    return weak_areas


def generate_improvement_strategies(weak_areas, report):
    """Generate specific improvement strategies based on weak areas."""
    strategies = []

    for weak_area in weak_areas:
        if weak_area['area'] == 'Revenue Growth':
            strategies.append({
                'area': 'Revenue Growth',
                'strategy': 'Revenue Acceleration Program',
                'actions': [
                    'Implement aggressive customer acquisition campaigns',
                    'Review and optimize pricing strategy',
                    'Launch upselling and cross-selling initiatives',
                    'Expand into new market segments or geographies',
                    'Accelerate product development and innovation'
                ],
                'expected_impact': 'High',
                'timeline': '3-6 months'
            })

        elif weak_area['area'] == 'Performance Consistency':
            strategies.append({
                'area': 'Performance Consistency',
                'strategy': 'Business Stabilization Plan',
                'actions': [
                    'Establish consistent sales processes and methodologies',
                    'Implement predictive analytics for demand forecasting',
                    'Develop recurring revenue streams (subscriptions, contracts)',
                    'Build customer loyalty programs to increase retention',
                    'Smooth out seasonal variations with counter-cyclical products'
                ],
                'expected_impact': 'Medium',
                'timeline': '4-8 months'
            })

        elif weak_area['area'] == 'Revenue Stability':
            strategies.append({
                'area': 'Revenue Stability',
                'strategy': 'Volatility Reduction Initiative',
                'actions': [
                    'Diversify revenue streams across products and customer segments',
                    'Establish minimum order requirements or retainer agreements',
                    'Implement inventory management to reduce supply chain disruptions',
                    'Create predictable pricing models (annual contracts vs. spot pricing)',
                    'Build strategic partnerships for consistent deal flow'
                ],
                'expected_impact': 'Medium',
                'timeline': '6-12 months'
            })

        elif weak_area['area'] == 'Category Performance':
            strategies.append({
                'area': 'Category Performance',
                'strategy': 'Portfolio Optimization',
                'actions': [
                    'Conduct deep-dive analysis on underperforming categories',
                    'Consider discontinuing or restructuring bottom performers',
                    'Reallocate resources to high-performing categories',
                    'Investigate pricing, positioning, or quality issues',
                    'Test targeted marketing campaigns for weak categories'
                ],
                'expected_impact': 'High',
                'timeline': '2-4 months'
            })

    # Add general recommendations
    strategies.append({
        'area': 'Data-Driven Culture',
        'strategy': 'Analytics Infrastructure Development',
        'actions': [
            'Implement real-time dashboards for key business metrics',
            'Establish regular reporting cadence (daily, weekly, monthly)',
            'Train team on data interpretation and decision-making',
            'Set up automated alerts for anomaly detection',
            'Build customer segmentation and cohort analysis capabilities'
        ],
        'expected_impact': 'High',
        'timeline': '3-6 months'
    })

    return strategies


def main():
    if len(sys.argv) < 2:
        print("Usage: python analyze_business_data.py <path_to_csv> [output_json]")
        sys.exit(1)

    csv_path = sys.argv[1]
    output_path = sys.argv[2] if len(sys.argv) > 2 else "business_analysis_report.json"

    print(f"\n📊 Business Data Analysis Report Generator")
    print(f"{'=' * 60}\n")

    # Load data
    print(f"Loading data from: {csv_path}")
    df = load_and_validate_data(csv_path)

    # Generate analysis
    print("\nAnalyzing business data...")
    report = generate_analysis_report(df)

    # Identify weak areas
    print("Identifying areas of weakness...")
    weak_areas = identify_weak_areas(report)
    report['weak_areas'] = weak_areas

    # Generate strategies
    print("Generating improvement strategies...")
    strategies = generate_improvement_strategies(weak_areas, report)
    report['improvement_strategies'] = strategies

    # Save report
    with open(output_path, 'w') as f:
        json.dump(report, f, indent=2)

    print(f"\n✓ Analysis complete! Report saved to: {output_path}")
    print(f"\nKey Findings:")
    print(f"  - {len(weak_areas)} areas of weakness identified")
    print(f"  - {len(strategies)} improvement strategies generated")

    if weak_areas:
        print(f"\nWeak Areas Summary:")
        for area in weak_areas:
            print(f"  • {area['area']} ({area['severity']} severity): {area['finding']}")


if __name__ == "__main__":
    main()

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How it compares

Choose business-analytics-reporter for one-off CSV sales deep-dives; use warehouse or BI integrations when analytics must run continuously on live data.

FAQ

What is business-analytics-reporter?

This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate statistical insights, and provide strategic improvement rec

When should I use business-analytics-reporter?

This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate statistical insights, and provide strategic improvement rec

Is business-analytics-reporter safe to install?

Review the Security Audits panel on this page before production use.

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