performance-benchmark-suite
SDK performance benchmarking and regression detection
npx skills add a5c-ai/babysitter --skill performance-benchmark-suite --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Performance Benchmark Suite Skill ## Overview This skill implements comprehensive SDK performance benchmarking, tracking latency, throughput, memory usage, and detecting performance regressions across versions. ## Capabilities - Measure latency percentiles (p50, p95, p99) - Track memory usage and allocation patterns - Detect performance regressions automatically - Generate visual benchmark reports - Compare performance across SDK versions - Implement microbenchmarks for critical paths - Configure continuous benchmarking in CI - Support load testing scenarios ## Target Processes - Performance Benchmarking - SDK Testing Strategy - SDK Versioning and Release Management ## Integration Points - k6 for load testing - Artillery for HTTP benchmarking - hyperfine for CLI benchmarking - Benchmark.js for JavaScript - pytest-benchmark for Python - Continuous benchmark systems (Bencher) ## Input Requirements - Performance requirements (SLOs) - Benchmark scenarios - Baseline versions for comparison - Environment specifications - Reporting requirements ## Output Artifacts - Benchmark test suite - Performance baseline data - Regression detection rules - Visual benchmark reports - CI benchmark co
- Overview
- Capabilities
- Target Processes
- Integration Points
- Input Requirements
- Output Artifacts
- Usage Example
- Best Practices
What does the performance-benchmark-suite skill do?
SDK performance benchmarking and regression detection
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill performance-benchmark-suite --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.