version: "1.0.1" name: review-pr description: Analyze a GitHub pull request including diff, comments, related issues, and local code context argument-hint: <pr-url-or-number> [-- <optional instructions>]

Review Pull Request

Parse $ARGUMENTS to extract the PR identifier and optional reviewer instructions:

• Everything before the first -- (or all of it, if there is no --) is the PR identifier (URL or number). Referred to as PR_ID below.
• Everything after the first -- is reviewer instructions — additional focus areas or context from the caller. If absent, there are no special instructions.

Analyze the pull request: PR_ID

Steps

Run the following steps. Gather data in parallel where possible.

1. Set up worktree

Resolve the PR number:

gh pr view PR_ID --json number

Use the number to form the worktree name review-pr-<number>.

Invoke the /create-worktree review-pr-<number> --pr <number> skill to create an isolated worktree for this PR. Note the worktree path from the skill output — referred to as WORKTREE below.

Verify that the cwd is now inside the worktree:

pwd

If pwd does not show WORKTREE, run cd WORKTREE and verify again. Stop with an error if the cwd cannot be set. Once confirmed, subsequent Bash calls will run inside the worktree automatically.

2. Fetch PR metadata and diff

Use gh to collect all PR information in parallel:

• gh pr view PR_ID --json title,body,author,state,baseRefName,headRefName,number,url,comments,reviews,labels,milestone
• gh pr diff PR_ID

3. Check dependency changes in pyproject.toml

If the PR diff (from step 2) touches pyproject.toml, run the automated dependency checker.

First, fetch the latest devel from origin so the comparison is against the current state:

git fetch origin devel

Then run the script — always compare against origin/devel regardless of the PR's base branch:

python tools/check_dependency_changes.py origin/devel

The script finds the merge-base, simulates a three-way merge (like GitHub), and reports:

• Main dependencies ([project.dependencies]) — displayed as WARNING (these affect every user)
• Optional dependencies ([project.optional-dependencies]) — displayed as WARNING (these affect users of specific extras)
• Dev/group dependencies ([dependency-groups]) — listed for awareness (no impact on end users)
• Merge conflicts — if pyproject.toml would conflict, the script warns and falls back to showing the PR's intended changes

Include the script output verbatim in the review. If the script exits with code 1 (changes detected), flag dependency changes in the review verdict. If it exits with code 2 (conflicts), flag that pyproject.toml needs conflict resolution.

If pyproject.toml is not in the diff, skip this step.

4. Check CI lint and mypy status

Use gh pr checks PR_ID to retrieve the CI check results. Look for checks matching lint on all python versions / lint* — these run mypy, ruff, flake8, bandit, and docstring checks across Python 3.9–3.13 (defined in .github/workflows/lint.yml).

• Report the status of each lint check (pass/fail/pending).
• If any lint check has failed, fetch its logs with gh run view <run-id> --log-failed to identify the specific errors (mypy type errors, ruff violations, etc.).
• Include lint/mypy status in the final review verdict. A failing lint check should be flagged as a required fix.

5. Analyze related issues

Extract any issue references from the PR body (e.g. fixes #1234, closes #1234, #1234). For each referenced issue, fetch it with:

• gh issue view <number> --json title,body,author,state,comments,labels

Understand the original problem being solved.

6. Analyze the code

Using absolute paths under WORKTREE:

• Read the changed files in full to understand context beyond the diff.
• Look at callers/callees of modified functions.
• Check if similar patterns exist elsewhere that should also be updated.
• Review the coding style and code patterns to conform with @CLAUDE.md
• If reviewer instructions were provided, pay special attention to the areas or concerns they describe during this analysis.
• Mention only crucial finding to the user in the initial overview. Avoid code snippets except if critical bugs were found

7. Review test coverage

7a. Define the testing scope

Based on the PR changes, identify what needs to be tested:

• What are the distinct behaviors introduced or modified?
• What are the edge cases and error paths?
• What are the integration boundaries (e.g., does the change affect how components interact)?

Produce a checklist of required test scenarios before evaluating existing tests.

7b. Evaluate existing tests against the checklist

• Compare the PR's tests against the required scenarios checklist. Flag any gaps.
• Flag any tests that verify things outside the PR's scope or test obvious/trivial behavior that adds no value.

7c. Verify tests are in the right modules

• Search the test directory for where similar functionality is already tested (grep for related function names, class names, or feature keywords).
• New tests should live alongside related existing tests, not in new files unless the PR introduces an entirely new module.
• Flag tests placed in the wrong location.

7d. Check for duplication and over-testing

• Look for tests that cover nearly identical cases and could be merged or parametrized with @pytest.mark.parametrize.
• Look for repeated setup/assertion logic across tests that should be extracted into a helper or fixture.
• If multiple tests differ only in a single variable (e.g., True/False, different type values), they must be parametrized into one test function.
• Flag any copy-paste test code. Minimizing test code maintenance burden is a priority.

7e. Bug Fixes Should Have Regression Tests

If the PR fixes a bug:

• Is there a test that would have caught the bug before the fix?
• Does the test verify the specific scenario from the bug report?

8. Check documentation coverage

If the PR modifies public API (new parameters, changed behavior, new configuration options, new functions/classes exposed to users):

• Search WORKTREE/docs/website/docs/ for existing documentation covering the topic of the PR.
• If the topic is already documented and the PR changes its behavior or adds new options, verify that the PR also updates the relevant doc pages.
• If documentation updates are missing, flag this in the review as a required change.
• If the topic is not documented at all, this is not a blocker — don't require docs for previously undocumented internals.

9. Produce the review

Write a structured analysis covering:

• Summary: What the PR does and why, referencing the linked issue.
• Changes: Breakdown of changes should be concise, avoid code samples
• Dependency Changes: If pyproject.toml was modified, include the warnings and listings from step 3. Main and optional dependency changes must appear as yellow WARNING blocks. Omit this section if no dependency changes were found.
• CI Lint & Mypy: Status of lint checks across Python versions. If any failed, list the specific errors.
• Observations & Potential Issues: Bugs, edge cases, missing error handling, performance concerns, memory issues, security issues, backward compatibility.
• Breaking Changes and Public API: Follow the rules on breaking changes and Public API and report issues.
• Test Coverage: Required scenarios checklist with pass/fail status, misplaced tests, duplication issues, parametrization opportunities.
• Documentation: Whether doc updates are needed and if they were provided.
• Reviewer Instructions: If reviewer instructions were provided, include a dedicated section addressing each point raised. If no instructions were provided, omit this section.
• Verdict: Overall assessment.