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Finishing A Development Branch

Community
Ibrahim-3d
finishing-a-development-branch

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Overview

PublisherIbrahim-3d
Repositoryorchestrator-supaconductor
Skill namefinishing-a-development-branch
Stars
378
Forks
38
Bundled files
Instructions only
LicenseAGPL-3.0
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 Ibrahim-3d 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/Ibrahim-3d/orchestrator-supaconductor.git /tmp/orchestrator-supaconductor
mkdir -p .claude/skills
cp -r /tmp/orchestrator-supaconductor/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

Guide completion of development work by verifying quality and completing the branch workflow.

Core principle: Behavior depends on conductor/config.json"mode":

  • "agentic": Auto-push branch and create PR — no user input needed.
  • "human-in-the-loop": Present 4 structured options and execute the user's choice.

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

The Process

Step 1: Verify Tests

Before presenting options, verify tests pass:

bash
# Run project's test suite
npm test / cargo test / pytest / go test ./...

If tests fail:

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

[Show failures]

Cannot proceed with merge/PR until tests pass.

Stop. Don't proceed to Step 2.

If tests pass: Continue to Step 2.

Step 2: Determine Base Branch

bash
# Try common base branches
git merge-base HEAD main 2>/dev/null || git merge-base HEAD master 2>/dev/null

Or ask: "This branch split from main - is that correct?"

Step 3: Complete Branch (Mode-Dependent)

"agentic" mode — Auto-Complete

Do NOT ask the user which option to choose. Proceed automatically:

  1. Push the branch to remote with -u flag
  2. Create a Pull Request using the track's spec and plan as PR description
  3. Report the PR URL to the user

Then go to Step 5 (Cleanup Worktree).

"human-in-the-loop" mode — Present Options

Present exactly these 4 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)
4. Discard this work

Which option?

Don't add explanation - keep options concise.

Step 4: Execute Choice (Human-in-the-loop mode only)

Option 1: Merge Locally
bash
# Switch to base branch
git checkout <base-branch>

# Pull latest
git pull

# Merge feature branch
git merge <feature-branch>

# Verify tests on merged result
<test command>

# If tests pass
git branch -d <feature-branch>

Then: Cleanup worktree (Step 5)

Option 2: Push and Create PR
bash
# Push branch
git push -u origin <feature-branch>

# Create PR
gh pr create --title "<title>" --body "$(cat <<'EOF'
## Summary
<2-3 bullets of what changed>

## Test Plan
- [ ] <verification steps>
EOF
)"

Then: Cleanup worktree (Step 5)

Option 3: Keep As-Is

Report: "Keeping branch . Worktree preserved at ."

Don't cleanup worktree.

Option 4: Discard

Confirm first:

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

Type 'discard' to confirm.

Wait for exact confirmation.

If confirmed:

bash
git checkout <base-branch>
git branch -D <feature-branch>

Then: Cleanup worktree (Step 5)

Step 5: Cleanup Worktree

For Options 1, 2, 4:

Check if in worktree:

bash
git worktree list | grep_search $(git branch --show-current)

If yes:

bash
git worktree remove <worktree-path>

For Option 3: Keep worktree.

Quick Reference

ModeBehavior
"agentic"Auto-push + create PR, no user input
"human-in-the-loop"Present 4 options, execute user's choice

Human-in-the-loop Option Summary

OptionMergePushKeep WorktreeCleanup Branch
1. Merge locally--
2. Create PR--
3. Keep as-is---
4. Discard---✓ (force)

Common Mistakes

Skipping test verification

  • Problem: Merge broken code, create failing PR
  • Fix: Always verify tests before proceeding

Open-ended questions (human-in-the-loop mode)

  • Problem: "What should I do next?" → ambiguous
  • Fix: Present exactly 4 structured options

Auto-completing without checking mode

  • Problem: Pushing/creating PRs when user expects to choose
  • Fix: Read conductor/config.json"mode" before Step 3

Automatic worktree cleanup

  • Problem: Remove worktree when might need it (Option 2, 3)
  • Fix: Only cleanup for Options 1 and 4

No confirmation for discard

  • Problem: Accidentally delete work
  • Fix: Require typed "discard" confirmation

Red Flags

Never:

  • Proceed with failing tests
  • Merge without verifying tests on result
  • Delete work without confirmation
  • Force-push without explicit request

Always:

  • Verify tests before completing
  • In "human-in-the-loop" mode: present exactly 4 options and get typed confirmation for Option 4
  • In "agentic" mode: auto-push and create PR, report URL to user
  • Clean up worktree for Options 1 & 4 only (human-in-the-loop) or after auto-push (agentic)

Integration

Called by:

  • subagent-driven-development (Step 7) - After all tasks complete
  • executing-plans (Step 5) - After all batches complete

Pairs with:

  • using-git-worktrees - Cleans up worktree created by that skill

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 - guides completion of development work by presenting structured options for merge, PR, or cleanup

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/Ibrahim-3d/orchestrator-supaconductor/tree/master/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 Ibrahim-3d under the AGPL-3.0 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.

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