Finishing A Development Branch logo

Finishing A Development Branch

CommunityPopular
obra
finishing-a-development-branch

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

Overview

Publisherobra
Repositorysuperpowers
Skill namefinishing-a-development-branch
Stars
288.1K
Forks
25.8K
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Finishing A Development Branch 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.
  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/obra/superpowers.git /tmp/superpowers
mkdir -p .claude/skills
cp -r /tmp/superpowers/skills/finishing-a-development-branch .claude/skills/finishing-a-development-branch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finishing A Development Branch 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 Finishing A Development Branch 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 Finishing A Development Branch 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.

Finishing a Development Branch

Overview

Core principle: Verify tests → Detect environment → Present options → Execute choice → Clean up.

Announce at start: "I'm using the finishing-a-development-branch skill to complete this work."

Step 1: Verify Tests

Run the project's full test suite (npm test / cargo test / pytest / go test ./...).

If tests fail, report the failures and stop — the menu comes after a green suite:

Tests failing (<N> failures). Must fix before completing:

[Show failures]

If tests pass: continue to Step 2.

Step 2: Detect Environment

bash
GIT_DIR=$(cd "$(git rev-parse --git-dir)" 2>/dev/null && pwd -P)
GIT_COMMON=$(cd "$(git rev-parse --git-common-dir)" 2>/dev/null && pwd -P)
# Capture now, while still inside the workspace — Step 5 changes directory
# before cleanup (Step 6) needs this value
WORKTREE_PATH=$(git rev-parse --show-toplevel)

This determines which menu to show and how cleanup works:

StateMenuCleanup
GIT_DIR == GIT_COMMON (normal repo)Standard 3 optionsNo worktree to clean up
GIT_DIR != GIT_COMMON, named branchStandard 3 optionsProvenance-based (see Step 6)
GIT_DIR != GIT_COMMON, detached HEADReduced 2 options (no merge)Externally managed — leave in place

Step 3: Determine Base Branch

The base branch is whatever this work forked from — usually named in the plan, the conversation, or the branch's upstream. If it is not already known, ask: "This branch split from - is that correct?" Confirm before merging: merging into the wrong base is expensive to undo.

Step 4: Present Options

Normal repo and named-branch worktree — present exactly these 3 options:

Implementation complete. What would you like to do?

1. Merge back to <base-branch> locally
2. Push and create a Pull Request
3. Keep the branch as-is (I'll handle it later)

Which option?

Detached HEAD — present exactly these 2 options:

Implementation complete. You're on a detached HEAD (externally managed workspace).

1. Push as new branch and create a Pull Request
2. Keep as-is (I'll handle it later)

Which option?

Present the menu exactly as written — concise, with every option coming from the list above. Discarding the work happens only in response to your human partner explicitly asking for it (see "If your human partner asks to discard the work" below). Wait for their answer; the integration decision is theirs.

Step 5: Execute Choice

Option 1: Merge Locally

bash
# Get main repo root for CWD safety
MAIN_ROOT=$(git -C "$(git rev-parse --git-common-dir)/.." rev-parse --show-toplevel)
cd "$MAIN_ROOT"

# Merge first — verify success before removing anything
git checkout <base-branch>
git pull
git merge <feature-branch>

# Verify tests on merged result
<test command>

If tests fail on the merged result: stop, leave the worktree and branch in place, and investigate — nothing has been pushed, so the merge is local and recoverable.

Once the merged result is green: clean up the worktree (Step 6), then delete the branch:

bash
git branch -d <feature-branch>

Option 2: Push and Create PR

bash
git push -u origin <feature-branch>
# From a detached HEAD, name the new branch on the remote:
# git push origin HEAD:refs/heads/<new-branch>

Then create the pull/merge request against with the forge's tooling — its CLI if one is available, or the creation URL most forges print when you push — following the repo's PR template and conventions if present, and report the URL to your human partner.

Keep the worktree — your human partner iterates on PR feedback there.

Option 3: Keep As-Is

Report: "Keeping branch . Worktree preserved at ."

If your human partner asks to discard the work

This path exists only as a response to an explicit request to throw the work away. Confirm first:

This will permanently delete:
- Branch <name>
- All commits: <commit-list>
- Worktree at <path>

Type 'discard' to confirm.

Wait for that exact confirmation. When it arrives:

bash
MAIN_ROOT=$(git -C "$(git rev-parse --git-common-dir)/.." rev-parse --show-toplevel)
cd "$MAIN_ROOT"

Then clean up the worktree (Step 6) and force-delete the branch:

bash
git branch -D <feature-branch>

Step 6: Cleanup Workspace

Runs for Option 1 and confirmed discards. Options 2 and 3 always preserve the worktree. Both callers have already changed directory to the main repo root — worktree removal must run from outside the worktree — and use the GIT_DIR/GIT_COMMON/WORKTREE_PATH values captured in Step 2, from before that directory change.

If GIT_DIR == GIT_COMMON: Normal repo, no worktree to clean up. Done.

If WORKTREE_PATH is under .worktrees/ or worktrees/: Superpowers created this worktree — we own cleanup:

bash
git worktree remove "$WORKTREE_PATH"
git worktree prune  # Self-healing: clean up any stale registrations

If removal is refused (contains modified or untracked files): the worktree holds files that exist nowhere else — uncommitted plans, notes, or scratch work. Never --force on your own initiative. Show your human partner what is at stake and ask:

bash
git -C "$WORKTREE_PATH" status --porcelain -uall
Worktree removal refused — these files were never committed:

<file list>

1. Commit them to <branch> before cleanup
2. Move them into <main repo root>
3. Delete them (unrecoverable)

Which?

Carry out the choice, then remove the worktree.

Otherwise: The host environment owns this workspace — leave it in place. If your platform provides a workspace-exit tool, use it.

Quick Reference

OptionMergePushKeep WorktreeCleanup Branch
1. Merge locallyyes--yes
2. Create PR-yesyes-
3. Keep as-is--yes-
Discard (explicit request only)---yes (force)

Common Rationalizations

ExcuseReality
"Tests passed earlier this session"Run the suite on the tree you are about to integrate. A green run only proves the tree it ran on.
"They obviously want it merged"Integration is your human partner's decision. Present the menu and wait.
"They seem done with this feature — I'll offer to discard it"The menu is complete as written. Discard happens only when your human partner asks for it in so many words.
"'Yeah, get rid of it' counts as confirmation"Only the typed word discard authorizes deletion.
"The PR is up, so the worktree is clutter now"PR feedback gets fixed in that worktree. It stays until the work lands.
"This other worktree looks stale — I'll clean it too"Clean up only worktrees under .worktrees/ or worktrees/. Everything else belongs to the host.
"Removal refused — --force is just finishing the cleanup"The refusal means files exist only in that worktree. --force destroys them permanently. Show your human partner and ask.
"The merged-result failure is probably flaky"A failing merged result stops everything. Branch and worktree stay put while you investigate.
"The base branch is obviously main"Confirm the fork point or ask. Merging into the wrong base is expensive to undo.
"The push was rejected — force-push will fix it"A rejected push means the remote moved. Investigate; force-push only on your human partner's explicit request.

Frequently asked questions

What does the Finishing A Development Branch AI skill do?

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

Why use Finishing A Development Branch on TypingMind?

Because you install it once and use it with any model. Finishing A Development Branch 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 Finishing A Development Branch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/obra/superpowers/tree/main/skills/finishing-a-development-branch. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finishing A Development Branch?

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 Finishing A Development Branch?

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

Is the Finishing A Development Branch AI skill free?

Yes. It is published on GitHub by obra under the MIT license. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇