An AI-powered system that analyzes GitHub profiles and repositories to generate structured, actionable feedback for portfolio improvement

Problem

GitHub profiles are often the primary signal for evaluating developers, but there is no structured way to assess code quality, project depth, or portfolio strength. Most candidates lack clear feedback on how their repositories are perceived by recruiters or how to improve them.

GitHub profiles are often the primary signal for evaluating developers, but there is no structured way to assess code quality, project depth, or portfolio strength. Most candidates lack clear feedback on how their repositories are perceived by recruiters or how to improve them.

Solution

This project builds an AI agent that evaluates GitHub profiles using repository-level signals and LLM-based analysis. It generates a structured audit report with actionable recommendations to improve portfolio quality, project presentation, and overall developer positioning.

Sample Report

How it works

1. GitHub Profile Input

Takes a GitHub username and retrieves public repositories using the GitHub API

2. Repository Analysis

Extracts signals such as:

- Project diversity & Code structure

- Documentation quality & Activity patterns

3. LLM-Based Evaluation

Uses LLMs to interpret repository quality and generate meaningful insights beyond raw metrics

4. Structured Feedback Generation

Produces a detailed audit including:

- Strengths & Weaknesses

- Improvement suggestions

5. Output Layer

Delivers a recruiter-style portfolio review with clear, actionable recommendations

Tech Stack

- Streamlit (UI)

- OpenAI API (LLM generation)

- GitHub API (data retrieval)

- Python (core logic)

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Repo

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