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SKILL.md
npx skills add ailabs-393/ai-labs-claude-skills --skill 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 recommendations. Use when the user requests a business performance report, asks to analyze sales data, wants to identify areas of weakness, or needs recommendations on business improvement strategies.
npx skills add ailabs-393/ai-labs-claude-skills --skill business-analytics-reporter
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:
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:
Load and explore the data:
Use the bundled analysis script to generate comprehensive insights:
The script will:
Output structure:
Read the generated JSON report and interpret the findings for the user in plain language.
Focus on:
Example interpretation:
Consult the business frameworks reference to provide strategic recommendations:
Load business frameworks for context:
Refer to references/business_frameworks.md for:
Structure recommendations as:
For each identified weak area, provide:
Example recommendation:
If requested, create interactive visualizations using Plotly to illustrate findings:
Consult visualization guide:
Refer to references/visualization_guide.md for:
Common visualizations to create:
Example code for revenue trend:
Compile findings into a comprehensive report format.
Option A: Generate HTML Report
Use the report template from assets/report_template.html:
The HTML template includes:
Option B: Generate Markdown Report
Create a structured markdown document:
The analysis script calculates the following metrics automatically:
When generating recommendations, leverage the frameworks documented in references/business_frameworks.md:
Match identified weak areas with appropriate strategic frameworks to provide contextually relevant recommendations.
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 |
User request: "Analyze my Q4 sales data and tell me where we're weak and how to improve"
Workflow:
df = pd.read_csv('q4_sales.csv')python scripts/analyze_business_data.py q4_sales.csv q4_report.jsonwith open('q4_report.json') as f: report = json.load(f)references/visualization_guide.md)assets/report_template.htmlreferences/business_frameworks.mdExpected output:
analyze_business_data.py: Automated analysis engine that detects data structure, calculates metrics, identifies weak areas, and generates improvement strategiesbusiness_frameworks.md: Comprehensive guide to business strategy frameworks, common weak areas, and solution templatesvisualization_guide.md: Chart type recommendations, Plotly code examples, and dashboard design best practicesreport_template.html: Professional HTML template with interactive visualizations, styled cards for weak areas and strategies, and print-ready formattingpython scripts/analyze_business_data.py path/to/business_data.csv output_report.json{
"metadata": {...},
"findings": {
"basic_statistics": {...},
"trend_analysis": {...},
"category_analysis": {...},
"variability": {...}
},
"weak_areas": [...],
"improvement_strategies": [...]
}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 identifiedStrategy: 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 increaseimport 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()# 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)# 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