LEARN_ADVANCED_GIT_WORKFLOWS Expanded Projects

This folder contains expanded project guides generated from LEARN_ADVANCED_GIT_WORKFLOWS.md.

Projects

  1. Project 1: “Git Object Explorer” — Parse and Display the Object Database
    • Build outcome: A tool that explores the .git directory, decompresses and parses Git objects (blobs, trees, commits, tags), and displays their contents in human-readable format with SHA verification.
    • Why it matters: Before you can master advanced workflows, you need to see what Git actually stores. This project forces you to understand that commits are just text files with parent pointers, branches are just files containing SHAs, and the entire history is a content-addressable database.
  2. Project 2: “Commit Graph Visualizer” — Render the DAG Structure
    • Build outcome: A tool that reads a Git repository and generates a visual graph showing commits, branches, merges, and tags—revealing the true DAG structure that underlies Git history.
    • Why it matters: When you visualize the DAG, you finally understand why rebase “rewrites history” (it creates new commits with different parents), why merge creates a commit with two parents, and how branches are just movable pointers.
  3. Project 3: “Interactive Rebase Simulator” — Understand History Rewriting
    • Build outcome: A tool that simulates git rebase -i without actually modifying the repository, showing exactly what commits would be created, dropped, or modified—and explaining why.
    • Why it matters: Interactive rebase is the most powerful and most misunderstood Git command. By simulating it step-by-step, you’ll understand that rebase doesn’t “move” commits—it creates new commits with the same changes but different parents (and therefore different SHAs).
  4. Project 4: “Three-Way Merge Engine” — Implement Git’s Core Merge Algorithm
    • Build outcome: A three-way merge tool that takes a base version, “ours” version, and “theirs” version of a file and produces either a merged result or conflict markers—exactly like Git does.
    • Why it matters: Every merge, rebase, and cherry-pick uses three-way merge internally. Understanding this algorithm explains why certain changes conflict and others don’t, and why merge is generally safer than manual patching.
  5. Project 5: “Git Hooks Framework” — Build Your Own Husky
    • Build outcome: A Git hooks management system (like Husky, but from scratch) that allows configuring pre-commit, pre-push, and commit-msg hooks via a config file, with support for running multiple scripts per hook.
    • Why it matters: Hooks are how teams enforce code quality—running linters, tests, and format checks before code is committed or pushed. Understanding hooks is essential for implementing trunk-based development and code review workflows.
  6. Project 6: “Trunk-Based Development Pipeline” — Implement Feature Flags and CI
    • Build outcome: A complete trunk-based development pipeline with feature flags, automated testing on every commit, and a CLI tool that manages short-lived branches and enforces trunk-based discipline.
    • Why it matters: Trunk-based development is how high-performing teams work—Google, Facebook, and Netflix all use variations of it. By implementing the tooling yourself, you’ll understand why long-lived branches cause merge pain and how feature flags enable shipping incomplete code safely.
  7. Project 7: “Code Review Bot” — Automate PR Quality Checks
    • Build outcome: An automated code review bot that comments on pull requests with actionable feedback—detecting large diffs, missing tests, style violations, and common anti-patterns.
    • Why it matters: Professional code review is the gatekeeper of code quality. By building a bot that performs automated reviews, you’ll understand what makes PRs easy or hard to review, why smaller PRs get approved faster, and how to structure changes for maximum reviewability.
  8. Project 8: “Monorepo Task Runner” — Build a Mini Turborepo
    • Build outcome: A monorepo task runner (like a mini Turborepo or Nx) that detects which packages changed, runs only affected tests, and caches results to avoid redundant work.
    • Why it matters: Monorepos are how Google, Microsoft, and many large companies organize code. The challenges—scale, affected detection, incremental builds—teach you how Git can be used as more than version control; it becomes the source of truth for what changed.
  9. Project 9: “Git Bisect Automator” — Debug Regressions with Binary Search
    • Build outcome: A tool that wraps git bisect to automatically find the commit that introduced a bug by running a test script, with support for skip detection, performance optimizations, and detailed reporting.
    • Why it matters: Bisect is Git’s killer debugging feature—it uses binary search to find the exact commit that broke something. By building a wrapper, you’ll understand how bisect works internally and how to leverage it effectively in real debugging scenarios.
  10. Project 10: “Stacked PRs Manager” — Handle Dependent Pull Requests
    • Build outcome: A tool for managing stacked (dependent) pull requests—where PR 2 depends on PR 1, PR 3 depends on PR 2, etc. The tool handles rebasing the stack when upstream changes, updating PR descriptions with dependency info, and orchestrating merges in order.
    • Why it matters: Stacked PRs are essential for large features that need to be reviewed incrementally. Managing them manually is error-prone; this project teaches you the rebase mechanics and the orchestration needed to keep a chain of branches synchronized.
  11. Project 11: “Conventional Commits Enforcer” — Automate Semantic Versioning
    • Build outcome: A commit message linter that enforces the Conventional Commits specification, generates changelogs automatically, and determines semantic version bumps based on commit types.
    • Why it matters: Conventional commits are the foundation of automated releases. By building the parser and enforcer yourself, you’ll understand how tools like semantic-release, commitlint, and changelog generators work—and why consistent commit messages enable powerful automation.
  12. Project 12: “Git Worktree Manager” — Work on Multiple Branches Simultaneously
    • Build outcome: A TUI (text UI) tool for managing Git worktrees—allowing you to have multiple branches checked out simultaneously in separate directories, with easy creation, switching, and cleanup.
    • Why it matters: Worktrees are Git’s hidden superpower for working on multiple branches simultaneously. By building a manager, you’ll understand how worktrees relate to the .git directory, when to use them vs. stash, and how they enable parallel development workflows.
  13. Project 13: “Repository Analytics Dashboard” — Mine Git History for Insights
    • Build outcome: A dashboard that analyzes repository history to show contribution patterns, code hotspots, team dynamics, and technical debt indicators—like a mini version of GitPrime or Pluralsight Flow.
    • Why it matters: Understanding how a team uses Git reveals workflow health. By mining git log data, you’ll see how commit frequency, merge patterns, and contributor activity reflect team practices—and how to improve them.
  14. Project 14: “Git Secret Scanner” — Find and Remove Leaked Credentials
    • Build outcome: A tool that scans Git history (not just current files) for accidentally committed secrets—API keys, passwords, tokens—and can optionally help remove them from history.
    • Why it matters: Security is a critical part of Git workflows. By building a scanner, you’ll understand how secrets persist in history even after deletion, how tools like git-secrets and truffleHog work, and how to use git filter-branch or BFG to rewrite history.

How to Use

  1. Read each project file in numeric order for a first pass.
  2. Use the deterministic outcome sections as acceptance criteria.
  3. Track implementation notes and postmortems per project.