Generate Candidate Summary Report

Generate Candidate Summary Report

This skill generates a comprehensive markdown summary report analyzing candidate profile data with statistics on gender distribution, URR representation, and nationality diversity.

What it does:

• Reads candidate profile CSV data
• Calculates comprehensive statistics (gender, URR, nationality)
• Generates formatted markdown report with tables and insights
• Identifies URR countries represented in the candidate pool

Usage:

Basic Usage

Run the summary generation script with default settings:

python .claude/​skills/​generate_candidate_summary_skill/​generate_summary.py

This uses default paths:

• Input: /​data/​home/​xiong/​dev/​Fund_Process_Automation/​candidate_profile.csv
• Output: /​data/​home/​xiong/​dev/​Fund_Process_Automation/​summary.md

With Custom Paths

Specify custom input and output files:

python .claude/​skills/​generate_candidate_summary_skill/​generate_summary.py \
  --csv_file /​path/​to/​candidate_profile.csv \
  --output_file /​path/​to/​summary.md

Command-line Arguments:

• --csv_file: Path to input CSV file (default: candidate_profile.csv in project root)
• --output_file: Path to output markdown file (default: summary.md in project root)

Input Requirements:

Expected Input File:

• Path: /​data/​home/​xiong/​dev/​Fund_Process_Automation/​candidate_profile.csv
• Format: CSV file with the following columns:
  • Name: Candidate's full name
  • Gender: Male/​Female/​Unknown
  • Country of Nationality: Country name
  • URR: "yes" or "no"

Note: This file is typically generated by the process_resume_skill.

Output:

Generated File:

• Path: /​data/​home/​xiong/​dev/​Fund_Process_Automation/​summary.md
• Format: Markdown document

Report Contents:

1. Overview Section

   • Total number of candidates analyzed

2. Summary Statistics Tables

   • Gender distribution (Male/​Female/​Unknown) with counts and percentages
   • URR vs Non-URR distribution with percentages
   • Top 10 nationalities with counts and URR status

3. Key Insights

   • Gender balance analysis
   • URR representation percentage
   • Geographic diversity metrics
   • Most common nationality

4. URR Countries List

   • All URR countries represented in the pool
   • Candidate count per URR country

Example Output Structure:

# Candidate Profile Summary

## Overview
This analysis covers X candidate resumes...

## Summary Statistics

### Gender Distribution
| Gender | Count | Percentage |
|--------|-------|------------|
| Male   | X     | XX.X%      |
| Female | X     | XX.X%      |

### Under-Represented Region (URR) Distribution
| URR Status | Count | Percentage |
|------------|-------|------------|
| URR (Yes)  | X     | XX.X%      |

### Top Nationalities Represented
| Country | Count | URR Status |
|---------|-------|------------|
...

## Key Insights
1. Gender Balance: ...
2. URR Representation: ...
3. Geographic Diversity: ...

## URR Countries Identified
- Country: X candidate(s)
...

Dependencies:

• Python 3.x
• pandas library (pip install pandas)

Configuration:

Default file paths (can be overridden with command-line arguments):

• Input: /​data/​home/​xiong/​dev/​Fund_Process_Automation/​candidate_profile.csv
• Output: /​data/​home/​xiong/​dev/​Fund_Process_Automation/​summary.md

Error Handling:

The script includes comprehensive error handling:

• Validates input CSV file exists before processing
• Checks for required columns (Gender, URR, Country of Nationality)
• Ensures CSV is not empty
• Creates output directory if it doesn't exist
• Provides clear error messages via logging

Console Output:

When successful, displays:

==================================================
SUMMARY REPORT GENERATED
==================================================
Output file: /​path/​to/​summary.md
Total candidates: X
Male: X, Female: X, Unknown: X
URR: X, Non-URR: X
==================================================

Key Features:

• Flexible paths: Use command-line arguments to specify custom input/​output locations
• Robust validation: Checks file existence, column presence, and data integrity
• Automatic directory creation: Creates output directories if they don't exist
• Comprehensive logging: Provides detailed information about processing steps
• Dynamic date: Report includes current generation date
• Error handling: Graceful failure with informative error messages