Git Workflow logo

Git Workflow

Organization
netresearch
git-workflow

Use when establishing branching strategies, implementing Conventional Commits, creating or reviewing PRs, resolving PR review comments, merging PRs (including CI verification, auto-merge queues, and post-merge cleanup), managing PR review threads, merging PRs with signed commits, handling merge conflicts, verifying a merge didn't silently drop changes, syncing a long-diverged branch (e.g. master into integration), rebasing a long-lived branch onto a moved base or splitting one into several PRs, integrating Git with CI/CD, setting up git hooks (lefthook, captainhook, husky, pre-commit), debugging hook-install failures in git worktrees, or working in a bare-repo plus per-branch-worktree layout, including consolidating a directory that holds both a plain clone and a .bare. The pull-request tooling is GitHub-only; references/pull-request-workflow.md carries the GitLab equivalents. Not for creating releases (use github-release) or diagnosing BLOCKED/won't-merge PRs (use github-project).

Overview

Publishernetresearch
Repositorygit-workflow-skill
Skill namegit-workflow
Stars
59
Forks
14
Bundled files
24
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 24 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by netresearch on GitHub. Read the source before you install it.

Installation

Install the Git Workflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/netresearch/git-workflow-skill/tree/main/skills/git-workflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/netresearch/git-workflow-skill.git /tmp/git-workflow-skill
mkdir -p .claude/skills
cp -r /tmp/git-workflow-skill/skills/git-workflow .claude/skills/git-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Git Workflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Git Workflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Git Workflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Git Workflow Skill

Not Here

Releases: github-release. BLOCKED-PR diagnosis: github-project.

GitHub only. Everything below the branching and commit sections — pr-status.sh, pr-merge.sh, the merge gate, review threads — speaks GitHub GraphQL. For a GitLab merge request the equivalent glab calls are in references/pull-request-workflow.md § GitLab: the same gate with glab.

Critical Rules (Non-Negotiable)

  1. No direct push to main — always open a PR.
  2. No merge before all threads resolved (references/pull-request-workflow.md).
  3. No squash unless asked — preserves atomic commits, signatures, bisection.
  4. No "tested/verified/working" without pasted command output — else say so.
  5. No edits to installed skill/plugin cache paths (~/.claude/skills/, ~/.claude/plugins/cache/, **/.bare/**) — always the repo worktree, verified by pwd.
  6. Force-push only with --force-with-lease — never plain --force.
  7. Commit before rebase — add → commit → fetch → rebase → push (a dirty tree aborts it).
  8. No editorializing — state what changed, not how good it is (references/no-editorializing.md).

Reference Files

ReferenceContent Triggers
references/commit-conventions.mdConventional commits, DCO sign-off
references/pull-request-workflow.mdPR merge gate, signed rebase, posting a review with inline suggestions
references/ci-cd-integration.mdCI watching, git mirrors
references/advanced-git.mdRebase (incl. non-interactive rebase -i), cherry-pick, bisect, stash, worktrees, reflog, hanging push/fetch (SSH mux), parsing git output in a script (flags that pin diff/log/status output against user config), fetching one branch in a single-branch clone (refspec)
references/github-releases.md→ github-release skill
references/git-hooks-setup.mdHook frameworks, hooks per stage
references/claude-code-hooks.mdsettings.json merge gate, cache-path rejection, auto-lint
references/code-quality-tools.mdshellcheck, shfmt, git-absorb, difftastic
references/merge-gate-watcher.mdWaiting on a gate, a watcher the harness killed, merge-driver loop, check taxonomy, stale-SHA rerun
references/spec-cleanup.mdPlanning artifacts off the base branch
references/no-editorializing.mdNo self-praise, no narrating the expected

Conventional Commits

<type>[scope]: <description>

feat MINOR, fix PATCH; full type list and DCO sign-off in references/commit-conventions.md. Breaking: ! after type, or BREAKING CHANGE: in the footer. Branches: feature/TICKET-123-description, release/1.2.0

Hook Detection

bash
ls lefthook.yml .lefthook.yml captainhook.json .pre-commit-config.yaml .husky/pre-commit 2>/dev/null || echo "No hooks"

Install commands per framework: references/git-hooks-setup.md.

PR Merge Requirements

Before merging: threads resolved, CI green (incl. annotations), rebased, signed, and reviewed on the current head — by a human, a bot, or the author through pr-merge.sh --self-reviewed. A review is mandatory; a bot review is not, and one is waited for only while it is actually in flight. Rebase-only + signed: git merge --ff-only.

Verification

bash
./scripts/verify-git-workflow.sh /path/to/repository
# Gate state, next action:
./scripts/pr-status.sh [-R owner/repo] [PR] [--json] [--watch]
# Merge; refuses when the gate is shut:
./scripts/pr-merge.sh [-R owner/repo] [PR] [--dry-run|--self-reviewed]
# Many PRs (`owner/repo N` per line) to merge; stops on any NEXT it does not handle:
./scripts/pr-sweep.sh [--interval SEC] [--timeout SEC] [--attention FILE] LISTFILE
# What a repository expects, before the first artifact:
./scripts/repo-contribution-preflight.sh [--repo <dir>] [--section docs|templates|packaging|ci|tools]
# Which copy is installed here (every script answers this):
./scripts/pr-status.sh --version

Contributing: https://github.com/netresearch/git-workflow-skill

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Git Workflow AI skill do?

Use when establishing branching strategies, implementing Conventional Commits, creating or reviewing PRs, resolving PR review comments, merging PRs (including CI verification, auto-merge queues, and post-merge cleanup), managing PR review threads, merging PRs with signed commits, handling merge conflicts, verifying a merge didn't silently drop changes, syncing a long-diverged branch (e.g. master into integration), rebasing a long-lived branch onto a moved base or splitting one into several PRs, integrating Git with CI/CD, setting up git hooks (lefthook, captainhook, husky, pre-commit), debu...

Why use Git Workflow on TypingMind?

Because you install it once and use it with any model. Git Workflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Git Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/netresearch/git-workflow-skill/tree/main/skills/git-workflow. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Git Workflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Git Workflow?

As many as you like. As long as a model supports skills, you can use Git Workflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Git Workflow AI skill free?

It is published on GitHub by netresearch. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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