pr-reviewer-benchmark

name	pr-reviewer-benchmark
description	Evaluate AI code reviewers on a single GitHub PR using a fixed rubric and produce a consistent results table. Use when benchmarking PR review bots, comparing false positives, or updating pr-expectations reports.

PR Reviewer Benchmark

Purpose

Score each reviewer on a PR with a repeatable process, then append a viewer-friendly table to pr-expectations/pr-NN-*.md.

Workflow

1. Confirm the target PR number and target expectation file.
2. Collect evidence from GitHub:
   • PR review comments (/pulls/{n}/comments) - top-level inline comments
   • PR review summaries (/pulls/{n}/reviews) - get each review's id and body
   • Per-review inline comments (/pulls/{n}/reviews/{review_id}/comments) - CRITICAL: some tools (notably Copilot) only surface inline comments here, not in the top-level comments endpoint. Always enumerate review IDs and fetch comments for each.
   • Check-runs (/commits/{sha}/check-runs)
3. Evaluate each reviewer using separate fresh sub-agents (no resume) so each tool is judged independently.
4. Collate findings in a single table with fixed columns.
5. Add a short takeaway section with 3-5 bullets.

Required Table Format

Use this exact column set:

Tool	Responded	Primary Finding	Verdict	Evidence

Definitions:

• Responded: Yes, No, or Check only
• Primary Finding: one-line summary of the main actionable signal
• Verdict: one of:
  • Pass
  • Fail - false positive
  • Mixed
  • Neutral
  • N/A
• Evidence: short quote or artifact type, for example inline comment on convex/overview.ts or check run only

Scoring Rubric (Convex-focused)

• Do not treat nested ctx.db calls in one Convex query as classic network N+1 by default.
• Legitimate concerns are still allowed (for example unbounded reads, real security leakage, schema mismatch).
• Check-only security bots can be Neutral when they do not provide code-level review.
• If a tool gives one false positive and one valid concern, mark Mixed.

Sub-agent Instructions Template

For each reviewer, launch a fresh sub-agent and provide:

• PR number
• reviewer/tool name
• only that tool's comments/check output
• rubric above

Ask sub-agent to return strict JSON:

{
  "tool": "ToolName",
  "responded": "Yes|No|Check only",
  "primaryFinding": "one line",
  "verdict": "Pass|Fail - false positive|Mixed|Neutral|N/A",
  "evidence": "short evidence string"
}

Output Location

• Append/update the report section in pr-expectations/pr-NN-*.md.
• Keep wording concise for video readability.
• Keep table order consistent across PRs:
  1. CodeRabbit
  2. Greptile
  3. Macroscope
  4. Cubic
  5. Graphite
  6. Qodo
  7. CodeAnt AI
  8. Sourcery
  9. Copilot