How the Hiring Agent Classifies Open Source vs. Self-Projects: Repository Detection Logic

The Hiring Agent distinguishes open-source contributions from self-projects by inspecting GitHub repository visibility in github.py, where public repositories increment the open-source counter and private or missing repositories increment the self-project tally.

The interviewstreet/hiring-agent repository automates candidate screening by parsing project portfolios and categorizing each entry. When analyzing submissions, the system applies a binary classification algorithm that separates collaborative open-source work from personal self-projects based on repository metadata and accessibility.

Classification Logic in github.py

The core classification engine resides in github.py, specifically around line 291, where the agent aggregates the two categories and reports the counts:

# github.py (≈ line 291)

📊 Project classification: {open_source_count} open source, {self_project_count} self projects

Repository Visibility as the Primary Signal

The agent determines project category by evaluating GitHub URL accessibility. Public repositories automatically qualify as open-source contributions, while private repositories or entries without GitHub URLs fall into the self-project category. The implementation queries the GitHub API or inspects URL patterns to verify visibility status before incrementing the respective counters.

License Detection (Secondary Validation)

When a repository is public, the agent optionally scans for a LICENSE file or recognized open-source license identifiers. While visibility remains the primary criterion, the presence of a standard license reinforces the open-source classification in the internal tally.

How Self-Projects Are Identified

The Hiring Agent applies specific rules to flag self-projects:

• Missing GitHub URLs: Projects submitted without repository links (descriptions only or personal websites) automatically classify as self-projects.
• Private Repository Access: Entries with GitHub URLs that resolve to private or inaccessible repositories increment the self_project_count.
• No License File: While not definitive, the absence of licensing metadata in conjunction with private visibility confirms the self-project designation.

Code Implementation and Examples

The classification logic is encapsulated in the ProjectClassifier class within github.py. Below is a practical implementation demonstrating how to invoke the categorization logic:

from hiring_agent.github import ProjectClassifier

# Example list of projects supplied by a candidate

projects = [
    {"name": "FastAPI-Demo", "repo_url": "https://github.com/alice/fastapi-demo"},
    {"name": "Internal Dashboard", "repo_url": None},
]

classifier = ProjectClassifier()
open_src, self_proj = classifier.categorise(projects)

print(f"Open-source: {open_src}, Self-projects: {self_proj}")

# Output: Open-source: 1, Self-projects: 1

Accessing Raw Classification Counts

To inspect the classification results directly, reference the internal counters updated by ProjectClassifier. These values populate the summary log line at line 291 in github.py, displaying the final open_source_count and self_project_count to reviewers.

Integration with Output Reporting

After classification, the counts are formatted for display. The prompt.py module constructs the final user-facing message that incorporates the classification line, presenting the open-source versus self-project breakdown in the agent's output summary.

Summary

• The Hiring Agent in interviewstreet/hiring-agent classifies projects by checking GitHub repository visibility in github.py.
• Public repositories are categorized as open-source; private or missing repositories are labeled as self-projects.
• Classification counts (open_source_count and self_project_count) are tallied around line 291 and displayed via the logging mechanism.
• The ProjectClassifier class provides the programmatic interface for categorizing project lists.
• Output formatting occurs in prompt.py, which renders the final classification summary for reviewers.

Frequently Asked Questions

How does the Hiring Agent determine if a project is open source?

The agent checks whether the candidate provided a public GitHub repository URL. If the repository is accessible and public, it increments the open_source_count. Private repositories or entries without GitHub links are classified as self-projects and increment the self_project_count instead.

What qualifies as a self-project in the Hiring Agent system?

Any project entry without a GitHub URL, or with a URL pointing to a private or inaccessible repository, is automatically classified as a self-project. This category also includes personal projects hosted on private infrastructure or submitted as plain text descriptions without repository links.

Where is the classification logic implemented in the codebase?

The core classification algorithm resides in github.py around line 291, within the ProjectClassifier class. This module handles repository validation, visibility checks, and maintains the running counters for both project types according to the interviewstreet/hiring-agent source code.

Does the Hiring Agent check for open-source licenses when classifying projects?

The agent optionally inspects public repositories for LICENSE files or recognized license identifiers. While repository visibility remains the primary classification criterion, the presence of a standard open-source license reinforces the open-source categorization in the internal tally.

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    "instagit": {
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}