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AI Review Guide
This document is for AI-assisted code review, patch review, and PR analysis in this repository.
Read AGENTS.md first. If the review touches substantive code paths, read .agents/architecture.md before reviewing.
Review Priorities
Prioritize findings in this order:
- Correctness bugs
- Regressions in observable behavior
- Public API and compatibility risk
- Sync and async parity gaps
- Packaging and optional dependency regressions
- Performance regressions in hot paths
- Missing or weak tests
- Style and nits
Repo-Specific Review Checklist
When reviewing a change, explicitly check whether it affects:
- sync and async parity
- public method signatures, defaults, or return types
- null handling, dtype behavior, timezone behavior, or result shapes
- Cython and pure Python parity
- bare install or optional dependency import behavior
- SQLAlchemy or DB-API compatibility
- compatibility expectations for Python, ClickHouse, Pandas, or SQLAlchemy versions covered in CI
For client-level behavior changes, confirm whether the tests exercise both sync and async paths using the shared integration fixtures.
What Good Review Feedback Looks Like
- Lead with findings, not summary.
- Order findings by severity.
- Use
file:linereferences. - Be explicit about impact.
- Call out what could break for real users.
- Distinguish confirmed issues from inferred risk.
If no material issues are found, say that explicitly and mention any residual testing or compatibility gaps.
Preferred Review Output
Use a structure like this:
- Findings, ordered by severity
- Open questions or assumptions
- Brief change summary, only if useful
Each finding should answer:
- what is wrong
- why it matters
- who or what it could break
- what evidence in the diff or repo context supports the concern
These points should be brief but factual and accurate.
Review Closing Checklist
Before saying a change looks good, make sure you understand:
- whether sync and async parity was considered
- whether public or user-visible behavior changed
- whether optional dependency and packaging behavior still make sense
- whether the change holds up across the compatibility axes in
.agents/architecture.md(Python versions, Cython on and off, bare install, SQLAlchemy 1.4, ClickHouse server versions) - whether tests are targeted and meaningful
- whether any important validation still has not been run