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performance-proof

Skill0 starsChanged 13 days agoMITView on GitHub

Find and validate a measurable performance bottleneck before optimizing. Use when latency, throughput, memory use, render speed, or compute cost is a concern.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run the same benchmark and correctness tests.

    line 15
  2. Distinctive:

    Check trade-offs such as memory, complexity, tail latency, warm-up, quality, and operational cost.

    line 16
  3. Distinctive:

    Never call an optimization successful without comparable measurements.

    line 22
  4. Distinctive:

    Do not use production-scale load, incur cloud/GPU spend, or affect live customers without authorization.

    line 23
  5. Distinctive:

    If no reliable baseline can be obtained, present the change as an unverified hypothesis.

    line 24

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Performance Proof

Procedure

  1. Define the metric and target: p50/p95 latency, throughput, memory, startup time, bundle size, GPU utilization, or cost per job.
  2. Record a baseline under representative inputs and a repeatable environment. Note warm-up, concurrency, dataset size, hardware, and variance.
  3. Profile the workload using available instrumentation before changing code. Identify the bottleneck and its evidence.
  4. Form one optimization hypothesis with a predicted measurable effect. Avoid speculative rewrites.
  5. Make a localized change, preserving behavior and interfaces. If several changes are necessary, isolate them so regressions can be attributed.
  6. Run the same benchmark and correctness tests. Compare distributions where possible, not a single favorable measurement.
  7. Check trade-offs such as memory, complexity, tail latency, warm-up, quality, and operational cost.

Result format

Metric, baseline, environment, change, new measurement, variance or caveat, correctness check, and recommendation.

Boundaries

  • Never call an optimization successful without comparable measurements.
  • Do not use production-scale load, incur cloud/GPU spend, or affect live customers without authorization.
  • If no reliable baseline can be obtained, present the change as an unverified hypothesis.

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