review-pr
This Claude Code skill runs an automated code review workflow against a GitHub pull request by executing the `ralph.reviewer.yml` orchestration loop, which checks out the PR in an isolated environment, runs tests, analyzes the diff, and generates a structured review report. Use it when you need to perform code review automation, verify PR quality, or generate comprehensive review documentation for a specific pull request number or URL.
git clone --depth 1 https://github.com/mikeyobrien/ralph-orchestrator /tmp/review-pr && cp -r /tmp/review-pr/.claude/skills/review-pr ~/.claude/skills/review-prSKILL.md
# Review PR Run the `ralph.reviewer.yml` orchestration loop against a pull request. The loop checks out the PR in an isolated worktree, runs tests, reviews the diff, and produces a structured report. ## Usage ``` /review-pr <PR number or URL> ``` Accepts: `207`, `#207`, or `https://github.com/.../pull/207` ## Execution ### 1. Parse the PR argument Extract the PR number from the argument. Strip `#` prefix or extract from URL path. ### 2. Run the reviewer loop ```bash ralph run -H ralph.reviewer.yml -p "Review PR #<N>" ``` **Bash tool settings:** - `timeout: 600000` (10 minutes) - `run_in_background: true` Use `TaskOutput` with `block: true` to wait for completion. ### 3. Display the report Read and print `.ralph/REVIEW-REPORT.md` to the conversation. If the report file doesn't exist (loop failed before the synthesizer hat), check for and display whatever intermediate files exist: 1. `.ralph/review-scope.md` — what was scoped 2. `.ralph/review-verification.md` — test results 3. `.ralph/review-findings.md` — review findings ### 4. Verify cleanup Check that the review worktree was removed: ```bash ls -d .worktrees/review-<N> 2>/dev/null ``` - If gone: cleanup succeeded, no action needed. - If still present: **warn the user** but do NOT force-remove. Say what's there and let them decide. Also note the presence of intermediate files (`.ralph/review-scope.md`, etc.) — they're useful for debugging but the user may want to clean them up later. ## Error Handling | Situation | Action | |-----------|--------| | Ralph exits non-zero | Display error output. Suggest re-running with `RALPH_DIAGNOSTICS=1` | | Report file missing | Display intermediate files that do exist (scope, verification, findings) | | PR argument missing | Ask the user for the PR number | | PR argument unparseable | Ask the user to provide a bare number, `#N`, or full GitHub URL | ## What This Skill Does NOT Do - Does NOT validate the PR exists (the scoper hat handles that) - Does NOT modify any source code (read-only review) - Does NOT post comments to GitHub - Does NOT own worktree cleanup (verifies only) - Does NOT enable diagnostics by default
Guides implementation of code tasks using test-driven development in an Explore, Plan, Code, Commit workflow. Acts as a Technical Implementation Partner and TDD Coach — following existing patterns, avoiding over-engineering, and producing idiomatic, modern code.
Generates structured .code-task.md files from descriptions or PDD implementation plans. Auto-detects input type, creates properly formatted tasks with Given-When-Then acceptance criteria.
Use when testing Ralph's hat collection presets, validating preset configurations, or auditing the preset library for bugs and UX issues.
Lists all code tasks in the repository with their status, dates, and metadata. Useful for getting an overview of pending work or finding specific tasks.
Transforms a rough idea into a detailed design document with implementation plan. Follows Prompt-Driven Development — iterative requirements clarification, research, design, and planning.
Browser automation via Playwriter (remorses) using persistent Chrome sessions and the full Playwright Page API.
Use when creating animated demos (GIFs) for pull requests or documentation. Covers terminal recording with asciinema and conversion to GIF/SVG for GitHub embedding.
Introspect, explain, and improve Ralph Orchestrator using its published llms.txt doc map. Use this skill whenever the user asks questions about Ralph's behavior, wants to understand how a Ralph internal works (event loop, hats, memories, tasks, backends, presets), debug an unfamiliar failure mode, or propose a code change to the ralph-orchestrator repo. The skill teaches the agent to discover authoritative answers from the live docs via llms.txt before guessing, and to scope improvements through the published architecture rather than the local checkout alone.