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

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.

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

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

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

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.

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.

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.

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

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

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

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.

performance-review