Iterate Pr

scripts/fetch_pr_feedback.py

Fetches and categorizes PR review feedback using the LOGAF scale.

uv run ${CLAUDE_SKILL_ROOT}/scripts/fetch_pr_feedback.py [--pr NUMBER]

Returns JSON with feedback categorized as:

• high - Must address before merge (h:, blocker, changes requested)
• medium - Should address (m:, standard feedback)
• low - Optional (l:, nit, style, suggestion)
• bot - Informational automated comments (Codecov, Dependabot, etc.)
• resolved - Already resolved threads

Review bot feedback (from Sentry, Warden, Cursor, Bugbot, CodeQL, etc.) appears in high/medium/low with review_bot: true — it is NOT placed in the bot bucket.

Each feedback item may also include:

• thread_id - GraphQL node ID for inline review comments (used for replies)

Workflow

1. Identify PR

gh pr view --json number,url,headRefName

Stop if no PR exists for the current branch.

2. Gather Review Feedback

Run ${CLAUDE_SKILL_ROOT}/scripts/fetch_pr_feedback.py to get categorized feedback already posted on the PR.

3. Handle Feedback by LOGAF Priority

Auto-fix (no prompt):

• high - must address (blockers, security, changes requested)
• medium - should address (standard feedback)

When fixing feedback:

• Understand the root cause, not just the surface symptom
• Check for similar issues in nearby code or related files
• Fix all instances, not just the one mentioned

This includes review bot feedback (items with review_bot: true). Treat it the same as human feedback:

• Real issue found → fix it
• False positive → skip, but explain why in a brief comment
• Never silently ignore review bot feedback — always verify the finding

Prompt user for selection:

• low - present numbered list and ask which to address:

Found 3 low-priority suggestions:
1. [l] "Consider renaming this variable" - @reviewer in api.py:42
2. [nit] "Could use a list comprehension" - @reviewer in utils.py:18
3. [style] "Add a docstring" - @reviewer in models.py:55

Which would you like to address? (e.g., "1,3" or "all" or "none")

Skip silently:

• resolved threads
• bot comments (informational only — Codecov, Dependabot, etc.)

Replying to Comments

After processing each inline review comment, reply on the PR thread to acknowledge the action taken. Only reply to items with a thread_id (inline review comments).

When to reply:

• high and medium items — whether fixed or determined to be false positives
• low items — whether fixed or declined by the user

How to reply: Use the addPullRequestReviewThreadReply GraphQL mutation with pullRequestReviewThreadId and body inputs.

Reply format:

• 1-2 sentences: what was changed, why it's not an issue, or acknowledgment of declined items
• End every reply with \n\n*— Claude Code*
• Before replying, check if the thread already has a reply ending with *- Claude Code* or *— Claude Code* to avoid duplicates on re-loops
• If the gh api call fails, log and continue — do not block the workflow

4. Check CI Status

Run ${CLAUDE_SKILL_ROOT}/scripts/fetch_pr_checks.py to get structured failure data.

Wait if pending: If review bot checks (sentry, warden, cursor, bugbot, seer, codeql) are still running, wait before proceeding—they post actionable feedback that must be evaluated. Informational bots (codecov) are not worth waiting for.

5. Fix CI Failures

For each failure in the script output:

1. Read the log_snippet and trace backwards from the error to understand WHY it failed — not just what failed
2. Read the relevant code and check for related issues (e.g., if a type error in one call site, check other call sites)
3. Fix the root cause with minimal, targeted changes
4. Find existing tests for the affected code and run them. If the fix introduces behavior not covered by existing tests, extend them to cover it (add a test case, not a whole new test file)

Do NOT assume what failed based on check name alone—always read the logs. Do NOT "quick fix and hope" — understand the failure thoroughly before changing code.

6. Verify Locally, Then Commit and Push

Before committing, verify your fixes locally:

• If you fixed a test failure: re-run that specific test locally
• If you fixed a lint/type error: re-run the linter or type checker on affected files
• For any code fix: run existing tests covering the changed code

If local verification fails, fix before proceeding — do not push known-broken code.

git add <files>
git commit -m "fix: <descriptive message>"
git push

7. Monitor CI and Address Feedback

Poll CI status and review feedback in a loop instead of blocking:

1. Run uv run ${CLAUDE_SKILL_ROOT}/scripts/fetch_pr_checks.py to get current CI status
2. If all checks passed → proceed to exit conditions
3. If any checks failed (none pending) → return to step 5
4. If checks are still pending: a. Run uv run ${CLAUDE_SKILL_ROOT}/scripts/fetch_pr_feedback.py for new review feedback b. Address any new high/medium feedback immediately (same as step 3) c. If changes were needed, commit and push (this restarts CI), then continue polling d. Sleep 30 seconds, then repeat from sub-step 1
5. After all checks pass, do a final feedback check: sleep 10, then run uv run ${CLAUDE_SKILL_ROOT}/scripts/fetch_pr_feedback.py. Address any new high/medium feedback — if changes are needed, return to step 6.

8. Repeat

If step 7 required code changes (from new feedback after CI passed), return to step 2 for a fresh cycle. CI failures during monitoring are already handled within step 7's polling loop.

Exit Conditions

Success: All checks pass, post-CI feedback re-check is clean (no new unaddressed high/medium feedback including review bot findings), user has decided on low-priority items.

Ask for help: Same failure after 2 attempts, feedback needs clarification, infrastructure issues.

Stop: No PR exists, branch needs rebase.

Fallback

If scripts fail, use gh CLI directly:

• gh pr checks name,state,bucket,link
• gh run view <run-id> --log-failed
• gh api repos/{owner}/{repo}/pulls/{number}/comments

When to Use

Use this skill when tackling tasks related to its primary domain or functionality as described above.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.