aiperf-code-review

name	aiperf-code-review
description	Review the current branch against origin/main, capture findings in artifacts/code-review.md as a living document, validate every finding against the actual code, reproduce confirmed issues with the aiperf CLI against the in-repo mock server, and draft inline GitHub PR review comments anchored to specific files and lines. Use when the user asks for a branch or PR code review.

Review the current branch against origin/main, then carry the whole task through end-to-end without stopping at analysis.

Goals:

1. Collect the review findings for the branch relative to origin/main.
2. Write them into artifacts/code-review.md as a living document.
3. Validate every finding against the actual current code.
4. Assign practical severity to each issue.
5. Reproduce the confirmed issues with the real aiperf CLI against the in-repo mock server.
6. Keep runtime receipts under artifacts/.
7. Update the living document with both source-level and runtime evidence.
8. Draft inline GitHub PR review comments anchored to the exact file and line of each finding, plus a short top-level summary comment.

Requirements:

• Treat artifacts/code-review.md as a living document. Update it in place if it already exists.
• CLI docs check: If the diff adds or removes any CLI parameter (i.e., any Annotated[..., CLIParameter(...)] field or --flag name in src/aiperf/config/flags/cli_config.py), verify that docs/cli-options.md was regenerated to reflect those changes. The file is auto-generated by make generate-cli-docs; a missing regeneration is a blocking finding. The description field inside Field(description=...) is the source of truth for what appears in the docs — changes there also require a regen.
• For each finding, record:
  • status: Confirmed, Partially confirmed, or Not confirmed
  • source-level evidence with exact file paths and line references
  • practical severity and impact
  • runtime reproduction result, if reproduced
  • receipt paths
  • conclusion
• Use the real codebase, not assumptions.
• If a finding is not valid, say so explicitly and explain why.
• If a finding is only partially valid, narrow it precisely.
• Reproduce with the real aiperf binary and the in-repo mock server on a random localhost port.
• Run outside the sandbox when needed and ask for approval through the normal tool flow.
• Save all receipts under a dedicated directory such as artifacts/repro-runtime-YYYYMMDD/.
• Keep logs, command outputs, relevant generated files, and small summaries that make the proof easy to inspect.
• If MLflow reproduction is needed, use a local SQLite MLflow backend so unrelated MLflow filesystem-store issues do not pollute the validation.
• Do not overwrite unrelated user changes.
• Do not stop after gathering evidence; finish by updating the document, then present the planned GitHub comments to the user for confirmation before posting.

GitHub deliverable:

• Post inline review comments using the GitHub PR review API (gh api repos/{owner}/{repo}/pulls/{number}/reviews).
• Each confirmed finding gets its own inline comment anchored to the relevant file path and diff line number.
• Include a short top-level summary in the review body covering: fix order, overall assessment, and what is working well.
• IMPORTANT: Before posting anything to GitHub, show the user the full set of planned comments (inline + summary) and ask for explicit confirmation. Only post after the user approves.
• To determine the correct diff line position for each inline comment, run gh api repos/{owner}/{repo}/pulls/{number}/files to get the patch hunks, then count lines within the patch to find the position value.
• After posting, return the PR review URL.

Final response to me:

• Keep it concise.
• Tell me where the living document is.
• Tell me where the receipts are.
• Tell me the GitHub review URL.
• Mention any caveats encountered during reproduction.