claude

performance-engineering

Performance- and capacity-engineering team — agents (performance-architect, load-testing-engineer, profiling-and-capacity-engineer) for system performance and scalability: performance strategy and budgets, workload modeling, and SLO-linked NFR targets (performance-architect); load/stress/soak/spike testing with k6/Gatling/Locust/JMeter, open- vs closed-model workloads, ramping, think time, and test data (load-testing-engineer); profiling, flame graphs, USE/RED bottleneck triage, capacity and headroom via Little's law, and regression detection (profiling-and-capacity-engineer). Knowledge bank with decision trees, a Little's-law/percentiles capacity calculator, best-practices, skills, commands, templates, an advisory hook, scenarios. Distinct from frontend Core Web Vitals and observability-sre SLOs. Seams: web vitals -> frontend-engineering; SLO -> observability-sre; query tuning -> database-engineering; resilience -> backend-engineering. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.2.3MITupdated today
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Plugin performance-engineering from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.

What it installs

Skills (3)

  • load-test-design

    Design the load/stress/soak/spike test from a modeled workload: pick the open- vs closed-model executor deliberately, design ramping and think time, generate realistic owned test data, avoid coordinated omission, and assert thresholds in-script — tool-neutral across k6/Gatling/Locust/JMeter.

  • performance-test-strategy

    Set the performance strategy before testing: turn vague goals into falsifiable NFRs (percentile + threshold + load), model the real workload, link targets to the customer SLO, and choose the test type (load/stress/soak/spike) that answers the open question.

  • profiling-and-bottleneck-triage

    Localize the bottleneck and size the system: CPU/memory/IO profiling and flame graphs, USE/RED triage to name the constraining resource, capacity planning with headroom via Little's law and the measured saturation point, and regression detection against a committed baseline.

Slash commands (3)

  • /performance-engineering:design-performance-test

    Set the performance strategy: turn vague goals into falsifiable NFRs, model the workload, link to the SLO, and pick the test type (load/stress/soak/spike).

  • /performance-engineering:run-load-test

    Build and run the load/stress/soak/spike test from a modeled workload: the right open/closed model, ramping, think time, realistic data, and in-script thresholds.

  • /performance-engineering:triage-bottleneck

    Profile a slow/near-limit system, localize the bottleneck (USE/RED + flame graph), compute capacity + headroom (Little's law), and verdict any regression.

Subagents (3)

  • load-testing-engineer

    Use to build and run the performance test once the workload is modeled: load, stress, soak, and spike scenarios in k6 / Gatling / Locust / JMeter. NOT for setting the workload model (performance-architect) or profiling the bottleneck (profiling-and-capacity-engineer).

  • performance-architect

    Use this agent to set the performance strategy BEFORE any load test: NOT for writing the test script (load-testing-engineer), finding the bottleneck (profiling-and-capacity-engineer), or the SLO/error-budget (observability-sre).

  • profiling-and-capacity-engineer

    Use this agent once a test or production shows a system is slow or near its limit, to find WHERE it breaks first and HOW BIG it must be: CPU/memory/IO profiling and flame graphs to localize the hot path.

Hooks (1)

  • PreToolUse

    /usr/bin/env -u BASH_ENV -u ENV -u SHELLOPTS -u BASHOPTS -u LD_PRELOAD -u LD_LIBRARY_PATH -u LD_AUDIT -u DYLD_INSERT_LIBRARIES -u DYLD_LIBRARY_PATH -u PYTHONPATH -u PYTHONSTARTUP PATH=/usr/local/bin:/

View source on GitHub mcorbett51090.github.io

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Maintain performance-engineering?

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