github-pr-review

# GitHub PR review

Resolves Pull Request review comments with severity-based prioritization, fix application, and thread replies.

## Current PR

!`gh pr view --json number,title,state,milestone -q '"PR #\(.number): \(.title) (\(.state)) | Milestone: \(.milestone.title // "none")"' 2>/dev/null`

## Core workflow

### 1. Fetch, filter, and classify comments

```bash
REPO=$(gh repo view --json nameWithOwner -q '.nameWithOwner')
PR=$(gh pr view --json number -q '.number')
LAST_PUSH=$(git log -1 --format=%cI HEAD)

# Inline review comments - filter out replies (keep only originals)
gh api repos/$REPO/pulls/$PR/comments?per_page=100 --jq '
  [.[] | select(.in_reply_to_id == null) |
   {id, path, user: .user.login, created_at, body: .body[0:200]}]
'

# PR-level reviews with non-empty body (CodeRabbit sections, Gemini, etc.)
gh api repos/$REPO/pulls/$PR/reviews?per_page=100 --jq '
  [.[] | select(.body | length > 0) |
   {id, user: .user.login, state, submitted_at, body: .body[0:500]}]
'
```

**Cross-check review-attached comments**: CodeRabbit's review body states "Actionable comments posted: N". If the general `pulls/$PR/comments` endpoint returns fewer than N new originals from that reviewer, some comments are only available via the review-specific endpoint. Fetch them and merge by comment ID:

```bash
# $REVIEW_ID from the reviews fetch above; $EXPECTED from parsing "Actionable comments posted: N"
gh api repos/$REPO/pulls/$PR/reviews/$REVIEW_ID/comments?per_page=100 --jq '
  [.[] | select(.in_reply_to_id == null) |
   {id, path, user: .user.login, created_at, body: .body[0:200]}]
'
```

Deduplicate by `id` before continuing. Comments found only via the review-specific endpoint are valid inline comments and should be treated identically (same classification, same `in_reply_to` reply mechanism).

**Filter new vs already-seen**: compare `created_at`/`submitted_at` with `$LAST_PUSH`. Comments posted after the last push are new. Mark older comments as "previous round" in the summary table.

**Parse CodeRabbit review bodies**: the initial fetch truncates bodies for classification. For reviews from CodeRabbit (`user.login` starts with `coderabbitai`), fetch the full body separately:

```bash
gh api repos/$REPO/pulls/$PR/reviews?per_page=100 --jq '
  [.[] | select(.user.login | startswith("coderabbitai")) |
   {id, submitted_at, body}]
'
```

CodeRabbit posts structured `<details>` blocks containing outside-diff, duplicate, and nitpick comments. Each block includes file path, line range, severity, and optionally a "Prompt for AI Agents" with pre-built context. See `references/coderabbit_parsing.md` for full parsing guide.

**Use CodeRabbit AI prompts when available**: if a comment (or the review body) contains a "Prompt for AI Agents" `<details>` block, use it to understand the issue and suggested approach. Always read the actual code before proposing a fix. If the review body contains a "Prompt for all review comments with AI agents" block, read it first for cross-comment context before processing individual comments.

Classify all comments by severity and process in order: CRITICAL > HIGH > MEDIUM > LOW.

| Severity | Indicators | Action |
|----------|------------|--------|
| CRITICAL | `critical.svg`, `_🔒 Security_`, `_🚨 Critical_`, `_🔴 Critical_`, "security", "vulnerability" | Must fix |
| HIGH | `high-priority.svg`, `_⚠️ Potential issue_`, `_🐛 Bug_`, `_⚡ Performance_`, `_🟠 Major_`, "High Severity" | Should fix |
| MEDIUM | `medium-priority.svg`, `_🛠️ Refactor suggestion_`, `_💡 Suggestion_`, "Medium Severity" | Recommended |
| LOW | `low-priority.svg`, `_🧹 Nitpick_`, `_🔧 Optional_`, `_🟡 Minor_`, `_🔵 Trivial_`, `_⚪ Info_`, "style", "nit" | Optional |

When a comment has both a type label and a secondary color badge (e.g., `_💡 Suggestion_ | _🟠 Major_`), the color badge is the **binding** severity and overrides the type-based default.

See `references/severity_guide.md` for full detection patterns (Gemini badges, CodeRabbit emoji, Cursor comments, keyword fallback, related comments heuristics).

### 2. Show review summary table

Before processing, display a structured overview of all comments:

```
| # | ID         | Severity | File:Line          | Type     | Status   | Summary            |
|---|------------|----------|--------------------|----------|----------|--------------------|
| 1 | 123456789  | CRITICAL | src/auth.py:45     | inline   | new      | SQL injection risk |
| 2 | 987654321  | HIGH     | src/db.py:346-350  | outside  | new      | Missing join cond  |
| 3 | 555555555  | HIGH     | src/chunk.py:188   | duplicate| previous | Stale metadata     |
| 4 | 444444444  | LOW      | tests/test_q.py:12 | nitpick  | previous | Naming convention  |
```

- **Type**: `inline`, `outside` (outside diff), `duplicate`, `minor`, `nitpick` (from CodeRabbit sections), or `review` (generic PR-level)
- **Status**: `new` (posted after last push) or `previous` (from earlier rounds)
- Group related comments (same file, same root cause, "also applies to" ranges) and note clusters
- Deduplicate: if the same issue appears both as an inline comment and in a CodeRabbit review body section (e.g., duplicate), keep one entry and note both sources

If there are **more than 10 comments**, suggest saving a review summary to Claude's memory for tracking across sessions. The summary should include: PR number, comment IDs, severity, status (new/addressed/deferred/won't fix), and brief description. This helps maintain continuity when new comments arrive after subsequent pushes.

### 3. Process each comment

For each comment, in severity order:

1. **Show context**: comment ID, severity, file:line, quote
2. **Check for AI prompt**: if CodeRabbit "Prompt for AI Agents" is available for this comment, use it to understand the issue and suggested approach
3. **Check for proposed fix**: if CodeRabbit includes a "Proposed fix" or "Suggested fix" code block, use it as a starting point (but verify correctness)
4. **R