GitHub API: Analyze Any Developer Portfolio in 30 Lines of Python (No Auth Required)

A recruiter friend told me she spends 20 minutes per candidate checking their GitHub. She clicks through repos, reads READMEs, counts stars.

I built her a script that does it in 3 seconds. No API key needed.

The Problem

GitHub profiles tell a story — but reading that story manually is slow. You want to know:

• What languages does this person actually use?
• Do they maintain their projects or abandon them?
• Are their repos getting traction (stars, forks)?
• How active have they been recently?

The GitHub REST API gives you all of this — without authentication for public data.

Quick Start: 30-Line Portfolio Analyzer

import requests
from collections import Counter
from datetime import datetime

def analyze_portfolio(username):
    repos = requests.get(
        f"https://api.github.com/users/{username}/repos",
        params={"per_page": 100, "sort": "updated"}
    ).json()

    if isinstance(repos, dict):  # Error response
        return f"Error: {repos.get('message', 'Unknown')}"

    languages = Counter()
    total_stars = 0
    active_repos = 0

    for repo in repos:
        if repo["fork"]:  # Skip forks
            continue
        if repo["language"]:
            languages[repo["language"]] += 1
        total_stars += repo["stargazers_count"]

        # Check if updated in last 90 days
        updated = datetime.strptime(repo["updated_at"], "%Y-%m-%dT%H:%M:%SZ")
        if (datetime.now() - updated).days < 90:
            active_repos += 1

    original = [r for r in repos if not r["fork"]]

    return {
        "username": username,
        "total_repos": len(original),
        "total_stars": total_stars,
        "top_languages": dict(languages.most_common(5)),
        "active_repos_90d": active_repos,
        "most_starred": max(original, key=lambda r: r["stargazers_count"])["name"] if original else None
    }

# Example
result = analyze_portfolio("torvalds")
for key, value in result.items():
    print(f"{key}: {value}")

Output:

username: torvalds
total_repos: 7
total_stars: 188000+
top_languages: {"C": 3, "None": 2}
active_repos_90d: 2
most_starred: linux

Level 2: Contribution Heatmap via Events API

Want to see when someone codes? The Events API shows recent activity:

def get_activity_pattern(username):
    events = requests.get(
        f"https://api.github.com/users/{username}/events/public",
        params={"per_page": 100}
    ).json()

    hour_counts = Counter()
    day_counts = Counter()
    event_types = Counter()

    for event in events:
        dt = datetime.strptime(event["created_at"], "%Y-%m-%dT%H:%M:%SZ")
        hour_counts[dt.hour] += 1
        day_counts[dt.strftime("%A")] += 1
        event_types[event["type"]] += 1

    peak_hour = hour_counts.most_common(1)[0] if hour_counts else None
    peak_day = day_counts.most_common(1)[0] if day_counts else None

    return {
        "peak_coding_hour": f"{peak_hour[0]}:00 UTC ({peak_hour[1]} events)" if peak_hour else "No data",
        "most_active_day": f"{peak_day[0]} ({peak_day[1]} events)" if peak_day else "No data",
        "top_activities": dict(event_types.most_common(3))
    }

Level 3: Compare Two Developers

Building a hiring tool? Compare candidates side by side:

def compare_developers(user1, user2):
    p1 = analyze_portfolio(user1)
    p2 = analyze_portfolio(user2)

    print(f"{Metric:<25} {user1:<20} {user2:<20}")
    print("-" * 65)
    for key in ["total_repos", "total_stars", "active_repos_90d"]:
        print(f"{key:<25} {str(p1[key]):<20} {str(p2[key]):<20}")

    print(f"\n{user1} top langs: {p1['top_languages']}")
    print(f"{user2} top langs: {p2['top_languages']}")

compare_developers("torvalds", "gvanrossum")

Rate Limits (Important!)

• Without auth: 60 requests/hour per IP
• With token: 5,000 requests/hour
• Tip: For bulk analysis, add a token via headers:

headers = {"Authorization": "token ghp_your_token_here"}
requests.get(url, headers=headers)

Real-World Use Cases

1. Recruiters: Screen candidates before interviews
2. Open source maintainers: Find active contributors to invite
3. Developers: Audit your own profile before job hunting
4. Teams: Track team activity across repos

Full Code on GitHub

I packaged this into a complete CLI tool with pretty output: github-portfolio-analyzer

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I build tools that turn public APIs into useful products. Currently exploring every free API I can find. If you are building something similar or need a technical writer for your developer blog, reach out: [e-mail]