What Is the Main Entry Point for the Hiring Agent Pipeline?

The main entry point for the Hiring Agent pipeline is the main(pdf_path) function in score.py, which orchestrates the complete workflow from PDF ingestion to fairness-aware evaluation when invoked via the command line or imported programmatically.

The interviewstreet/hiring-agent repository implements an automated resume evaluation system. Understanding the main entry point for the Hiring Agent pipeline is essential for developers integrating this tool into CI workflows or extending its capabilities. The entire process is coordinated through a single orchestrator module that handles document conversion, LLM-based extraction, GitHub profile enrichment, and structured scoring.

The Orchestrator Module: score.py

The definitive entry point resides in the score.py module at the repository root. This file defines the main(pdf_path) function that drives the entire evaluation pipeline, along with a __main__ block that enables direct script execution.

When score.py is run as a script, the __main__ block parses command-line arguments and passes the PDF path to main(pdf_path), triggering the full workflow. This design provides both a CLI interface for end-users and a clean programmatic API for integration into larger systems.

Step-by-Step Pipeline Execution

The main(pdf_path) function coordinates five distinct stages implemented across specialized modules:

1. PDF to Markdown Conversion The pipeline begins by converting the input PDF into section-wise Markdown. This process utilizes pymupdf_rag.py for low-level page extraction and pdf.py for section segmentation and structuring.

2. LLM-Based Resume Extraction The pdf.py module invokes the language model using Jinja templates stored in prompts/templates/ to parse the Markdown content. This produces a structured JSONResume object defined in models.py, standardizing candidate information for downstream processing.

3. GitHub Profile Enrichment The github.py module fetches the candidate’s GitHub profile and repository data. It then prompts the LLM to identify and select the top seven most relevant projects, enriching the resume data with verifiable technical contributions.

4. Fairness-Aware Evaluation The evaluator.py module applies fairness-aware scoring rules to the enriched resume data. This component ensures consistent, unbiased assessment of candidate qualifications against predefined criteria.

5. Orchestration and Output Finally, score.py aggregates results from all stages, prints a human-readable summary to stdout, and manages output persistence. When DEVELOPMENT_MODE=True is configured in config.py, the system also writes results to resume_evaluations.csv in the project root.

How to Invoke the Pipeline

Command-Line Execution

For direct command-line usage, execute the score.py script with a PDF path argument:

python score.py path/to/resume.pdf

The __main__ block automatically handles argument parsing and invokes main(pdf_path) to process the document.

Programmatic Integration

For integration into Python applications or test suites, import the main function directly:

from score import main

pdf_file = "resume.pdf"
evaluation = main(pdf_file)  # Returns the structured evaluation object

# Note: Human-readable summary is printed automatically by main()

This approach returns the evaluation object while preserving the console output generated by the orchestrator.

Development Mode and Artifact Generation

When operating in development environments, set the environment variable before execution:

export DEVELOPMENT_MODE=True
python score.py resume.pdf

With DEVELOPMENT_MODE enabled, the pipeline appends evaluation results to resume_evaluations.csv in the project root, facilitating batch analysis, debugging, and result caching across multiple candidate assessments.

Summary

• The main(pdf_path) function in score.py serves as the canonical main entry point for the Hiring Agent pipeline.
• Execution can be triggered via CLI (python score.py <pdf_path>) or by importing and calling main() directly in Python code.
• The pipeline processes resumes through Markdown conversion, LLM-based JSONResume extraction, GitHub enrichment (top 7 projects), and fairness-aware evaluation via evaluator.py.
• Setting DEVELOPMENT_MODE=True enables CSV output to resume_evaluations.csv for development and analysis workflows.

Frequently Asked Questions

What file contains the main entry point for the Hiring Agent pipeline?

The score.py module at the repository root contains the main entry point. It defines the main(pdf_path) function and the __main__ CLI block that parses command-line arguments and initiates the complete evaluation workflow, coordinating calls to pdf.py, github.py, and evaluator.py.

How do I run the Hiring Agent pipeline from the command line?

Execute python score.py path/to/resume.pdf from the project root directory. The __main__ block in score.py handles argument parsing and validates the file path before calling main(pdf_path) to begin processing.

Can I import the Hiring Agent pipeline as a Python module?

Yes. Import the main function from score.py and pass a PDF path string: from score import main; result = main("resume.pdf"). This returns the structured evaluation object while the orchestrator automatically prints the human-readable summary to stdout.

What happens when DEVELOPMENT_MODE is enabled?

When DEVELOPMENT_MODE=True is set in the environment or config.py, the pipeline writes evaluation results to resume_evaluations.csv in the project root. This enables batch processing analysis, result caching across multiple runs, and debugging of the fairness-aware scoring implemented in evaluator.py.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}