Prepare a separate development folder for a Git branch using a worktree. The skill copies environment files, installs Python and frontend dependencies, and generates the Prisma client so the branch has its own working setup.
Set up a new git worktree for parallel development. Creates the worktree, copies .env files, installs dependencies, and generates Prisma client. TRIGGER when user asks to set up a worktree, work on a branch in isolation, or needs a separate environment for a branch or PR.
Run score Not tested
See how to installworktree prepares environments for parallel development using Git worktrees, which give each branch a separate working folder. It supports both existing branches and new branches created from dev. It copies .env from the original working folder, falling back to .env.default when .env is absent. It then installs dependencies and generates the Prisma client.
Use it when you want to work on another fix while keeping your current work in place, or give each PR its own working folder. If you leave out the folder name, it chooses the next available AutoGPT<N>. The skill also includes instructions for removing the worktree after development is finished.
dev and set up its working environment.”The skill uses Git, Poetry, and pnpm, and assumes the directory structure under autogpt_platform. CoPilot's SDK mode does not work inside Claude Code, so testing requires CHAT_USE_CLAUDE_AGENT_SDK=false. The optional app startup instructions include forcibly terminating processes that occupy the ports it uses.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI checked the SKILL.md for operations that need care and for setup requirements. The score comes only from actually running the skill (hands-on test).
Not counted in the score. Rated by AI (OpenAI Codex) as of Oct 23, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
Derive paths from the git toplevel. If a name is provided as argument, use it. Otherwise, check git worktree list and pick the next AutoGPT<N>.
ROOT=$(git rev-parse --show-toplevel)
PARENT=$(dirname "$ROOT")
# From an existing branch
git worktree add "$PARENT/<NAME>" <branch-name>
# From a new branch off dev
git worktree add -b <new-branch> "$PARENT/<NAME>" dev
Copy .env from the root worktree. Falls back to .env.default if .env doesn't exist.
ROOT=$(git rev-parse --show-toplevel)
TARGET="$(dirname "$ROOT")/<NAME>"
for envpath in autogpt_platform/backend autogpt_platform/frontend autogpt_platform; do
if [ -f "$ROOT/$envpath/.env" ]; then
cp "$ROOT/$envpath/.env" "$TARGET/$envpath/.env"
elif [ -f "$ROOT/$envpath/.env.default" ]; then
cp "$ROOT/$envpath/.env.default" "$TARGET/$envpath/.env"
fi
done
TARGET="$(dirname "$(git rev-parse --show-toplevel)")/<NAME>"
cd "$TARGET/autogpt_platform/autogpt_libs" && poetry install
cd "$TARGET/autogpt_platform/backend" && poetry install && poetry run prisma generate
cd "$TARGET/autogpt_platform/frontend" && pnpm install
Replace <NAME> with the actual worktree name (e.g., AutoGPT7).
Backend uses ports: 8001, 8002, 8003, 8005, 8006, 8007, 8008. Free them first if needed:
TARGET="$(dirname "$(git rev-parse --show-toplevel)")/<NAME>"
for port in 8001 8002 8003 8005 8006 8007 8008; do
lsof -ti :$port | xargs kill -9 2>/dev/null || true
done
cd "$TARGET/autogpt_platform/backend" && poetry run app
SDK mode spawns a Claude subprocess — won't work inside Claude Code. Set CHAT_USE_CLAUDE_AGENT_SDK=false in backend/.env to use baseline mode.
# Replace <NAME> with the actual worktree name (e.g., AutoGPT7)
git worktree remove "$(dirname "$(git rev-parse --show-toplevel)")/<NAME>"
If branchlet is installed:
branchlet create -n <name> -s <source-branch> -b <new-branch>
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Create a pull request that follows the project's template and title conventions. Check existing behavior and test coverage, document testing limits, and coordinate self-review or bot review with follow-up on reported issues.
Coordinate multiple Claude Code agents in separate Git worktrees and tmux windows. Assign tasks, inspect progress, help stalled agents resume, and verify PR checks and review feedback before treating work as complete.
Compare performance across releases, environments, or time windows using Sentry data. Report request counts, latency percentiles, and failure rates, with statistical checks that distinguish faster or slower behavior from noise and flag insufficient data.
Work through PR review feedback by fixing code, pushing commits, replying to reviewers, and resolving addressed threads. Monitor automated checks, merge conflicts, and new comments until checks pass and the review work is complete.
Prepare a pull request for merging by repeatedly reviewing changes and addressing feedback. Verify resolved discussions, passing automated checks, and mergeability, then check twice for late comments after the checks finish.
Review a pull request, a proposed code change, for correctness, security, design, and test coverage. Check its explanation and existing feedback, then post findings on relevant code lines with severity labels.