superpowers/finishing-a-development-branch/SKILL.md
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
npx skillsauth add adminlove520/xiaoxi-skills finishing-a-development-branchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Guide completion of development work by presenting clear options and handling chosen workflow.
Core principle: Verify tests → Present options → Execute choice → Clean up.
Announce at start: "I'm using the finishing-a-development-branch skill to complete this work."
Before presenting options, verify tests pass:
# 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.
# 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?"
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.
# 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)
# 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)
Report: "Keeping branch <name>. Worktree preserved at <path>."
Don't cleanup worktree.
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:
git checkout <base-branch>
git branch -D <feature-branch>
Then: Cleanup worktree (Step 5)
For Options 1, 2, 4:
Check if in worktree:
git worktree list | grep $(git branch --show-current)
If yes:
git worktree remove <worktree-path>
For Option 3: Keep worktree.
| Option | Merge | Push | Keep Worktree | Cleanup Branch | |--------|-------|------|---------------|----------------| | 1. Merge locally | ✓ | - | - | ✓ | | 2. Create PR | - | ✓ | ✓ | - | | 3. Keep as-is | - | - | ✓ | - | | 4. Discard | - | - | - | ✓ (force) |
Skipping test verification
Open-ended questions
Automatic worktree cleanup
No confirmation for discard
Never:
Always:
Called by:
Pairs with:
data-ai
Spaced-repetition flashcard system. Create cards from facts or text, chat with flashcards using free-text answers graded by the agent, generate quizzes from YouTube transcripts, review due cards with adaptive scheduling, and export/import decks as CSV.
development
Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.
development
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
devops
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.