Supporting material availability

This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files:

• <support-root>/scripts/fetch_pr_checks.py
• <support-root>/scripts/fetch_pr_feedback.py

Ask for the original supporting material or an existing installation before using a step that depends on these paths; this catalog does not identify a verified download source for them. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable.

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

…