القائمة
Run Criterion benchmarks with baseline comparison for performance optimization work
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | bench-compare |
| description | Run Criterion benchmarks with baseline comparison for performance optimization work |
Compare benchmark results against a baseline to measure optimization impact.
Invoke with optional benchmark filter:
bench-compare - Run all benchmarksbench-compare scan - Run only scan-related benchmarksbench-compare throughput - Run throughput benchmarksBefore making changes, save current benchmark results:
cargo bench --bench <name> -- --save-baseline before
Or for all benchmarks:
cargo bench -- --save-baseline before
Make the optimization changes to the codebase.
Run benchmarks against the saved baseline:
cargo bench --bench <name> -- --baseline before
Look for these patterns in Criterion output:
Performance has improved - Optimization successfulPerformance has regressed - Changes hurt performanceNo change in performance - Within noise thresholdProvide a summary table:
| Benchmark | Before | After | Change |
|---|---|---|---|
| name | X ns | Y ns | -Z% |
The project has these benchmark files in benches/:
scan.rs - Core scanning performancescanner_throughput.rs - End-to-end throughputvectorscan_overhead.rs - Vectorscan integration overheadrule_isolation.rs - Per-rule cost isolationhotspots.rs - Known hot path benchmarks (requires bench feature)validator.rs - Validation step benchmarks (requires bench feature)ring_buffer, fixed_set, fixed_vec, timing_wheel, etc.--bench <name> to run specific benchmarks for faster iterationhotspots and validator benches require: cargo bench --features benchmemory_bandwidth.rsWrite-then-verify documentation pipeline. Use when a user asks to improve comments or docs, explain algorithms or design choices, write or upgrade docstrings, or raise documentation quality for a codebase (especially Rust crates). Writes docs, then automatically verifies every claim against code reality using a fresh agent to eliminate confirmation bias.
Use when you have code review findings, PR comments, or review reports that need to be systematically addressed — especially when there are multiple findings across different files and severities
Use when creating any beads task — auto-researches the codebase, links related tasks, and produces a rich self-contained description from a structured template. Accepts minimal intent and outputs a complete task ready for agent implementation.
Use when you have code review findings, PR comments, or review reports that need to be systematically addressed — especially when there are multiple findings across different files and severities
Use when a task needs an implementation plan that is iteratively created and stress-tested through review-and-revise cycles before implementation begins — catches blind spots, incorrect codebase assumptions, unnecessary complexity, and performance pitfalls while changes are still cheap
Use when a markdown plan file exists and needs validation before implementation — catches design flaws, logic holes, footguns, unnecessary complexity, and performance concerns while changes are still cheap