performance-review

name	performance-review
description	Perform focused performance reviews of code changes, pull requests, branches, or workspace diffs for algorithmic complexity, database query patterns, N+1 risks, pagination, caching, batching, memory use, synchronous I/O, latency, and load behavior. Use when Codex is asked for a performance review, scalability review, profiling plan, benchmark plan, or latency-risk assessment.

Performance Review

Quick Start

• Run scripts/performance_signal_scan.py <path> when a repository or changed-file path is available.
• Read references/performance-review-checklist.md before finalizing findings.
• Follow shared safety, validation, and scope rules in ../_shared/references/skill-operating-rules.md.
• Focus on hot paths, data size, request rate, and operational cost.
• Stay read-only unless the user explicitly asks for fixes.

Review Workflow

1. Establish expected scale: records, requests per second, concurrency, payload size, memory budget, latency budget.
2. Identify hot paths and expensive dependencies: database, network, filesystem, CPU, serialization, rendering, queues.
3. Run signal scan:

python3 scripts/performance_signal_scan.py /path/to/repo-or-file

4. Trace loops, queries, API calls, and allocations through realistic worst-case inputs.
5. Recommend validation: targeted tests, profiling, explain plans, benchmarks, load tests, or production metrics.

Output Rules

• Findings must include scale assumptions and the cost mechanism.
• Distinguish measured issues from plausible risks.
• Prefer concrete mitigations: add pagination, batch calls, add index, cache with invalidation, stream data, avoid repeated work.
• If no findings are found, state residual risk and whether profiling or benchmarks were skipped.