Tools: I built Spotify Wrapped for Git using GitHub Copilot CLI

What I Built ## The Full Feature Set ## Why I Built This ## Try it yourself ## Tech Stack ## My Experience with GitHub Copilot CLI ## 1. Understanding git log format ## 2. Calculating commit streaks ## 3. Grouping commits by hour of day ## 4. Rendering the terminal heatmap ## The Impact ## Challenges & Discoveries ## What Makes This Special GitHub Copilot CLI Challenge Submission This is a submission for the GitHub Copilot CLI Challenge git-wrapped — Spotify Wrapped, but for your Git history. Every December, Spotify tells you what kind of listener you are. I wanted the same thing for coding. What if your terminal could tell you that you're a Night Owl who codes at 2 AM, or a Code Surgeon who deletes more than they write? git-wrapped is a Python CLI tool that analyzes any Git repository and generates a beautiful, colorful terminal report. No browser, no dashboard, no account — just run a command and see your year in code. I spend a lot of time in the terminal. I wanted something that celebrates the work developers do — not just the PRs that get merged, but the late-night debugging sessions, the weekend side projects, the 14-day commit streaks that nobody notices. git-wrapped makes that visible. And it's fun. GitHub repo: stackmasteraliza/git-wrapped One external dependency. That's it. GitHub Copilot CLI was my pair programmer for this entire project. Instead of tab-switching between Stack Overflow, Git docs, and Rich documentation, I stayed in my terminal and asked Copilot directly. Here's exactly how I used it at each stage of development: The first challenge was figuring out how to extract structured data from git log. I asked Copilot CLI to explain the --format flags, and it gave me a clear breakdown of every placeholder — %H for hash, %an for author name, %aI for ISO date, %s for subject line — plus how --numstat adds per-file insertion/deletion stats and how >>> delimiters can separate commits for reliable parsing. This saved me from trial-and-error with Git's dense documentation. Computing the longest daily commit streak was trickier than expected. I needed to handle gaps, deduplicate dates, and track both the longest historical streak and the current active one. I asked Copilot CLI for approaches and it gave me multiple solutions — a Python version using datetime.timedelta to detect consecutive days, and even a Bash one-liner. I adapted the Python approach directly into my analyzer: For the "When You Code" charts, I needed to count commits grouped by hour. Copilot CLI gave me multiple shell approaches — from simple cut pipelines to awk loops — which helped me understand the data shape before writing the Python version. Seeing the shell approach first made the Python implementation obvious. This was the hardest visual to get right. A GitHub-style contribution heatmap, but in the terminal, using colored Unicode blocks. I asked Copilot CLI how to render a colored heatmap using Python's Rich library, and it showed me two approaches: I ended up combining both — using Rich's Console for color management and block characters for the grid cells: Without Copilot CLI, each of these problems would have meant opening a browser, scanning docs, finding a relevant example, adapting it, and testing. That cycle breaks flow. With Copilot CLI, the cycle was: ask a question → get a working answer → adapt and move on. I stayed in my terminal the entire time. The cognitive load dropped and I could focus on making the output feel delightful instead of fighting implementation details. It felt less like using a tool and more like pair programming with someone who has every man page memorized. The heatmap was the hardest part. Terminals don't have pixels — you're working with a character grid where each "cell" is a text character. Getting the heatmap to look like GitHub's contribution graph required careful math: mapping 52 weeks × 7 days into a grid, choosing the right Unicode block characters, and picking green shades that look good across different terminal themes. Personality detection was surprisingly fun. The "Coder DNA" feature assigns you a personality based on your commit patterns. Calibrating the thresholds (e.g., "30% of commits after 10 PM = Night Owl") required running it against a bunch of real repos to make sure the labels felt accurate and fun, not arbitrary. Rich is incredible. Python's Rich library does the heavy lifting for all the terminal visuals — panels, tables, progress bars, colored text. It's the reason git-wrapped looks polished without needing a web UI. Built with Python, Rich, and a lot of late-night commits. Powered by GitHub Copilot CLI. GitHub Repository: stackmasteraliza/git-wrapped Templates let you quickly answer FAQs or store snippets for re-use. 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Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse COMMAND_BLOCK: # Install from PyPI pip install git-wrapped # Or install from source git clone https://github.com/stackmasteraliza/git-wrapped.git cd git-wrapped pip install -e . # Run on any repo git-wrapped # Analyze a specific year git-wrapped --year 2025 # Compare two years git-wrapped --compare 2024 2025 # Generate a share card git-wrapped --share Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: # Install from PyPI pip install git-wrapped # Or install from source git clone https://github.com/stackmasteraliza/git-wrapped.git cd git-wrapped pip install -e . # Run on any repo git-wrapped # Analyze a specific year git-wrapped --year 2025 # Compare two years git-wrapped --compare 2024 2025 # Generate a share card git-wrapped --share COMMAND_BLOCK: # Install from PyPI pip install git-wrapped # Or install from source git clone https://github.com/stackmasteraliza/git-wrapped.git cd git-wrapped pip install -e . # Run on any repo git-wrapped # Analyze a specific year git-wrapped --year 2025 # Compare two years git-wrapped --compare 2024 2025 # Generate a share card git-wrapped --share COMMAND_BLOCK: # Copilot helped me arrive at this pattern sorted_dates = sorted(set(commit_dates)) streak, max_streak = 1, 1 for i in range(1, len(sorted_dates)): if (sorted_dates[i] - sorted_dates[i-1]).days == 1: streak += 1 max_streak = max(max_streak, streak) else: streak = 1 Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: # Copilot helped me arrive at this pattern sorted_dates = sorted(set(commit_dates)) streak, max_streak = 1, 1 for i in range(1, len(sorted_dates)): if (sorted_dates[i] - sorted_dates[i-1]).days == 1: streak += 1 max_streak = max(max_streak, streak) else: streak = 1 COMMAND_BLOCK: # Copilot helped me arrive at this pattern sorted_dates = sorted(set(commit_dates)) streak, max_streak = 1, 1 for i in range(1, len(sorted_dates)): if (sorted_dates[i] - sorted_dates[i-1]).days == 1: streak += 1 max_streak = max(max_streak, streak) else: streak = 1 CODE_BLOCK: colors = ["bright_black", "dark_green", "green", "bright_green"] char = "█" for week in range(weeks): for day in range(7): intensity = get_intensity(data[week][day]) output += f"[{colors[intensity]}]{char}[/]" Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: colors = ["bright_black", "dark_green", "green", "bright_green"] char = "█" for week in range(weeks): for day in range(7): intensity = get_intensity(data[week][day]) output += f"[{colors[intensity]}]{char}[/]" CODE_BLOCK: colors = ["bright_black", "dark_green", "green", "bright_green"] char = "█" for week in range(weeks): for day in range(7): intensity = get_intensity(data[week][day]) output += f"[{colors[intensity]}]{char}[/]" - The Numbers — Total commits, files changed, lines added/deleted, net line impact, active days - Activity Heatmap — A GitHub-style contribution calendar rendered entirely in your terminal using Unicode block characters - When You Code — Hour-of-day and day-of-week bar charts showing your coding rhythm - Top Files — Leaderboard of your most frequently modified files - Languages — Programming language breakdown with percentage bars - Streaks & Records — Longest streak, current streak, busiest single day, best month - Coder DNA — A personality assessment based on your commit patterns: Night Owl, Early Bird, Weekend Warrior, Streak Master, Feature Machine, Code Surgeon, or Balanced Builder - Fun Facts — Holiday commits, longest/shortest commit messages, contributor count, productivity stats - Compare Mode — Year-over-year comparison (--compare 2024 2025) with delta percentages and personality shift tracking - Share Card — A copy-pasteable plain text card for sharing on social media (--share) - JSON Export — Dump raw stats as JSON for further analysis or integrations - Python 3.8+ — Core language - Rich — Terminal formatting (panels, tables, progress bars, styled text) - Git log parsing — Custom parser using --numstat and --format for file-level stats - Zero external API calls — Everything runs locally, your data never leaves your machine - A Table-based approach using Rich's Table with colored cells - A block character approach mapping values to █ characters with ANSI colors - See your coding habits visualized in seconds - Discover patterns you didn't know you had (turns out I code most on Thursdays) - A fun conversation starter in team standups - Run it on shared repos to see team-wide patterns - Compare year-over-year to track how the team's rhythm evolves - Fully open source — fork it, extend it, make it yours - Single dependency — easy to install anywhere - Privacy-first — everything runs locally, no data leaves your machine