skills/12-pedrohcgs-claude-code-my-workflow/dot-claude/skills/commit/SKILL.md
Stage, commit, create PR, and merge to main. Use for the standard commit-PR-merge cycle.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research commitInstall 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.
Stage changes, commit with a descriptive message, create a PR, and merge to main.
git status
git diff --stat
git log --oneline -5
git checkout -b <short-descriptive-branch-name>
git add -A):git add <file1> <file2> ...
Do NOT stage .claude/settings.local.json or any files containing secrets.
If $ARGUMENTS is provided, use it as the commit message. Otherwise, analyze the staged changes and write a message that explains why, not just what.
git commit -m "$(cat <<'EOF'
<commit message here>
EOF
)"
git push -u origin <branch-name>
gh pr create --title "<short title>" --body "$(cat <<'EOF'
## Summary
<1-3 bullet points>
## Test plan
<checklist>
🤖 Generated with [Claude Code](https://claude.com/claude-code)
EOF
)"
gh pr merge <pr-number> --merge --delete-branch
git checkout main
git pull
settings.local.json and sensitive files from staging--merge (not --squash or --rebase) unless asked otherwise$ARGUMENTS is provided, use it exactlytools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".