iterate-pr

安装量: 235
排名: #3727

安装

npx skills add https://github.com/getsentry/skills --skill iterate-pr
Iterate on PR Until CI Passes
Continuously iterate on the current branch until all CI checks pass and review feedback is addressed.
Requires
GitHub CLI (
gh
) authenticated.
Important
All scripts must be run from the repository root directory (where .git is located), not from the skill directory. Use the full path to the script via ${CLAUDE_SKILL_ROOT} . Bundled Scripts scripts/fetch_pr_checks.py Fetches CI check status and extracts failure snippets from logs. uv run ${CLAUDE_SKILL_ROOT} /scripts/fetch_pr_checks.py [ --pr NUMBER ] Returns JSON: { "pr" : { "number" : 123 , "branch" : "feat/foo" } , "summary" : { "total" : 5 , "passed" : 3 , "failed" : 2 , "pending" : 0 } , "checks" : [ { "name" : "tests" , "status" : "fail" , "log_snippet" : "..." , "run_id" : 123 } , { "name" : "lint" , "status" : "pass" } ] } 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: Read the log_snippet and trace backwards from the error to understand WHY it failed — not just what failed Read the relevant code and check for related issues (e.g., if a type error in one call site, check other call sites) Fix the root cause with minimal, targeted changes 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: " git push 7. Monitor CI and Address Feedback Poll CI status and review feedback in a loop instead of blocking: Run uv run ${CLAUDE_SKILL_ROOT}/scripts/fetch_pr_checks.py to get current CI status If all checks passed → proceed to exit conditions If any checks failed (none pending) → return to step 5 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 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 --log-failed gh api repos/{owner}/{repo}/pulls/{number}/comments

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