xirong/my-git

xirong/my-git has 7,403 GitHub stars and 2,470 forks. Star changes are not measured yet. Last checked 2026-11-07 18:01 UTC. Last push 2026-09-09.

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Git as the control plane for AI-native software engineering | AI Native 软件工程的 Git 变更控制手册

7,403 stars 2,470 forks

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README_en.md

Original

My Git Handbook

English | 中文

A Git change-control handbook for AI-native software engineering: understand the design, verify changes, and manage collaboration.

Why learn when AI can run the commands?

AI can carry out operations. Understanding shapes the options we notice, the questions we ask, and the evidence we require before accepting a result. Without a concept in mind, we may never think to use it.

I maintain this handbook to explain enduring ideas in Git: how snapshots record state, how objects reuse content, how references organize history, and how collaboration handles divergence. These ideas support human judgment; AI helps us experiment, execute, and verify.

The emphasis shifts from execution fluency toward understanding, judgment, and verification. Practical skills still matter, especially reading critical commands and recognizing risk. Practice tests understanding.

Read the philosophy · Start learning interactively

From understanding design to managing collaboration

Use the knowledge map to identify whether you are solving a conceptual question, one engineering change, or team collaboration before entering the articles below.

What you want to develop	Start here	What you will be able to judge
Understand the design	Interactive learning → Learning path	Where changes live, how history forms, and what recovery depends on
Complete an engineering change	Accept a change end to end → Review example	Whether scope, commit revision, and test evidence agree
Manage collaboration	Team Git Workflow Guide → GitHub governance	How to isolate, review, authorize, release, and recover

Git supplies versions and history. Tests, review, permissions, artifacts, and release systems complete change control. This handbook covers both the tool’s capabilities and their limits.

Who Is This For?

Git beginners looking to build the right mental model.
Practicing developers who frequently need to handle conflicts, rollbacks, and accidental commits.
Senior developers focused on PRs, Review, CI, and release collaboration.
Technical leads who need to design team Git workflows and GitHub governance standards.
AI coding users who need to control the risks of code changes brought by tools like Codex, Claude Code, Cursor, and Cline.

Directory Navigation

Directory	Problems Solved
01-getting-started	Git basics, mental models, and the knowledge map
02-daily-workflow	Daily commands, branching and merging, rebase, stash, and worktree
03-team-collaboration	Team workflows, PRs, Code Review, and selecting collaboration models
04-github-engineering	Branch protection, Rulesets, CODEOWNERS, CI, releases, and security
05-ai-native-development	Diff review, commit splitting, worktree, and multi-agent strategies for AI coding
06-troubleshooting	Recovering from Git mishaps, conflict resolution, secret cleaning, and force push recovery
07-large-repo	Large repositories, monorepos, partial clone, sparse checkout, and Git LFS
08-templates	Templates for PRs, Issues, Reviews, Hotfixes, Releases, and AI Reviews
10-company-practices	Enterprise engineering practice cases and decision maps

Interactive Learning

Use animations, articles, and temporary-repository labs to understand Git snapshots, the object graph, references, history transformations, remotes, worktrees, and storage. The ten topics follow one sequence: 01 through 03 use the existing interactives and 04 through 10 use the added interactives. Material and scripts can be checked; reader understanding still needs prediction, reproduction, and transfer.

Open the Git Mental Model Interactive Lab

View the ten-topic article, interactive, and lab navigation

Learning Paths

Beginner Path

Why Learn Git in the AI Era
Git Mental Model
Ten-topic course navigation: each topic connects an article, interactive, and temporary-repository lab.
Git Basic Commands
Everyday Git Commands

Daily Development Path

Team Collaboration Path

GitHub Engineering Governance Path

AI Native Development Path

Troubleshooting Path

Find Answers by Problem

The problem I'm facing	Suggested reading
Just committed to the wrong branch	Committed to Wrong Branch
Don't know how to recover after reset, rebase, or force push	Git Troubleshooting Playbook
Team doesn't know whether to choose GitHub Flow, Gitflow, or Trunk-Based Development	Team Git Workflow Guide
PR is too large, review is slow	Pull Request Best Practices
AI changed too many files at once, don't know how to review	AI Change Review Examples
Large AI-generated diff needs to be split into commits or PRs	AI Commit Splitting, Stacked PR for AI-Generated Changes
Want to protect the main branch	Branch Protection
AI agents are pushing code and opening PRs, and I don't know how to govern them	AI Agent Governance
Want an agent collaboration convention for the team repository	AGENTS.md Template
Too many PRs, main branch often broken after merge	Merge Queue
Clone, status, or checkout is slow in a large repository	Large Repository Git Practices
Want to directly copy team templates	Templates

Phase 1: Must-Read Articles

Recommended Resources

New content will prioritize official documentation and industry-standard materials:

For a more complete index of materials, see Recommended Reading.

Versions and Maintenance Plan

See the roadmap for detailed plans.

The course, repairs, and collaboration material are collected in the v2.2.0 release notes and Changelog. The documentation was prepared on 2026-09-09; GitHub Releases records publication status.

Existing content will be gradually incorporated into new learning paths. See the legacy content migration for the migration list and the resources index for the materials index.

Project Governance

Original project content is licensed under the MIT License. Third-party materials, archived ebooks, external articles, linked resources, and quoted references remain under their original licenses and copyrights.

Contributing

We welcome contributions of real-world problems, team practices, incident recovery experiences, AI programming workflows, and reusable templates.

Please follow the Content Style Guide for new content.