AI Skill Report Card

Analyzing Solar Panel Catalog

B+78·Feb 10, 2026·Source: Extension-page

Solar Panel Catalog Analysis

Extract key product data from HTML/text containing solar panel listings:

Python
import re from dataclasses import dataclass @dataclass class SolarPanel: brand: str model: str wattage: int price: float availability: str = "In Stock" def parse_solar_panel(product_text): # Extract brand (first word before "PANEL SOLAR") brand_match = re.search(r'(\w+)\s+PANEL SOLAR', product_text) brand = brand_match.group(1) if brand_match else "Unknown" # Extract wattage (number followed by W) wattage_match = re.search(r'(\d+)W', product_text) wattage = int(wattage_match.group(1)) if wattage_match else 0 # Extract price ($ followed by number with decimals) price_match = re.search(r'\$(\d+\.?\d*)', product_text) price = float(price_match.group(1)) if price_match else 0.0 # Extract model (alphanumeric code after brand) model_match = re.search(rf'{brand}\s+(.+?)\s+\$', product_text) model = model_match.group(1).strip() if model_match else "Unknown" return SolarPanel(brand, model, wattage, price)
Recommendation
Add more concrete input/output examples showing different catalog formats (HTML tables, CSV, JSON) and edge cases like missing data
  1. Parse Product Listings

    • Extract individual product blocks from HTML/text
    • Identify product boundaries using images or separators
  2. Extract Core Data

    • Brand name (JA SOLAR, LONGI, etc.)
    • Model number (JAM54D41, LR8-66HGD-610W, etc.)
    • Wattage rating (455W, 610W, etc.)
    • Price in USD
  3. Calculate Efficiency Metrics

    • Price per watt ($/W)
    • Power density comparisons
    • Brand performance ratios
  4. Generate Summary Report

    • Product count by brand
    • Price range analysis
    • Wattage distribution

Progress:

  • Parse raw product data
  • Extract specifications
  • Calculate metrics
  • Generate comparison table
  • Create summary statistics
Recommendation
Include a complete analysis template or framework that shows how to structure the final comparison report with actual data tables

Example 1: Input: "JA SOLAR PANEL SOLAR JA SOLAR JAM54D41 LB 455W $86.00" Output:

Brand: JA SOLAR
Model: JAM54D41 LB 455W
Wattage: 455W
Price: $86.00
Price/Watt: $0.189

Example 2: Input: "LONGI PANEL SOLAR LONGI HIMO 7 LR8-66HGD-610W $92.50" Output:

Brand: LONGI
Model: HIMO 7 LR8-66HGD-610W
Wattage: 610W
Price: $92.50
Price/Watt: $0.152
Recommendation
Expand the workflow to include data validation steps and error handling for malformed product listings
  • Always calculate price per watt for meaningful comparisons
  • Group products by brand for easier analysis
  • Flag unusual pricing (too high/low price per watt ratios)
  • Extract model numbers completely - they contain important technical info
  • Convert all prices to same currency if mixed
  • Validate wattage extraction (should be 100-1000W range for typical panels)
  • Don't confuse voltage ratings (12V, 24V) with wattage
  • Don't extract random numbers as wattage - verify "W" suffix
  • Don't ignore product variants (M, LB, HIMO suffixes matter)
  • Don't assume price format - some use commas, different currencies
  • Don't merge different product lines from same brand
0
Grade B+AI Skill Framework
Scorecard
Criteria Breakdown
Quick Start
11/15
Workflow
11/15
Examples
15/20
Completeness
15/20
Format
11/15
Conciseness
11/15