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Yeachan-Heo/benchmark-checklist

Yeachan-Heo

benchmark-checklist

Vet a measured number before reporting or acting on it: name the limiter, measure the claimed work, repeat with variance, rule out the observer, label every claim. For any speedup, regression, or score you are about to report.

v1.0LATEST
NewUpdated Nov 8, 2026

Benchmark Checklist

Run this checklist before you report, commit, or act on any measured number: a speedup, a regression, a throughput, a latency, or an eval score. Its purpose is to keep you from measuring the wrong thing and believing it.

When to use

  • you ran a benchmark, profiler, timer, or eval and are about to state a number
  • a decision (merge, revert, optimize further) depends on the measurement
  • a user asks "is it faster?" and a measurement exists or will be taken

When not to use

  • no measurement is involved — do not block prose claims about unmeasured behavior on this checklist; instead label them as guesses

1. Name the limiter

State what limits the metric before you measure — CPU, lock contention, I/O, allocator, network, cold cache, or the harness itself. A change that "improves" a metric the limiter does not govern measured noise, not work. If you cannot name a plausible limiter, say so and treat the number as unexplained.

2. Measure the work you claim

  • Same inputs, same data shape, same sizes as the claim being made.
  • Warmup runs until steady state; report whether caches are cold or warm, and which claim each state supports.
  • The measured path must include the changed code, end to end. A microbenchmark that skips the integration point does not support a claim about the system.

3. Repeat and report variance

Run enough repetitions to see the spread — at least five, more in noisy environments. Report the median and the spread, not a single best run. If the effect is smaller than run-to-run variance, the honest verdict is "no measurable change".

4. Rule out the observer

  • Confirm the tool measured the work: profile output naming your function, timers around the real span, counters that actually moved.
  • A failing check must fail for the predicted reason; a passing one must rest on real state and exit codes, not truncated or swallowed output. This is the principles skill's Prove-it-works clause applied to measurement.

5. Report with labels

Every number carries its evidence and its label in the same sentence — measured, inferred, or guess. Name the machine, the build, and the data scale. When the stakes justify it, persist the raw output as an artifact under .omc/ and link it.

Verdict

Only after all five sections pass does the number enter a reply, a PR description, or a decision. If a section fails, report what you actually established instead of the claim you hoped to make.

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Overall Score

87/100

Grade

A

Excellent

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

95

Quality

85

Clarity

88

Completeness

82

Summary

A disciplined checklist for vetting measured performance claims before reporting or acting on them. The skill guides users through five critical validation steps: naming performance limiters, measuring claimed work accurately, reporting variance, ruling out measurement tool bias, and labeling findings with evidence. This is a read-only decision-making framework with no file writes, shell execution, or external network access.

Detected Capabilities

decision guidancechecklist enforcementno file operationsno shell execution

Trigger Keywords

Phrases that agents use to match this skill to user intent.

benchmark before decidingvalidate performance claimverify speedup is realcheck regression measurementmeasure before commitperformance evidence neededeval score rigor

Referenced Domains

External domains referenced in skill content, detected by static analysis.

github.com

Use Cases

  • Validate performance improvements before merging optimization PRs
  • Verify regression detection is real, not measurement artifact
  • Evaluate benchmark scores with statistical rigor
  • Prevent false claims about speedups or latency improvements
  • Establish performance-driven decisions with reproducible data

Quality Notes

  • Clear, specific checklist structure with five sequential validation steps
  • Each section explains the rationale and common failure modes (e.g., measuring noise instead of work, observer bias)
  • Practical examples of what constitutes valid evidence (profile output, timers, exit codes)
  • Addresses edge cases and common pitfalls in benchmarking (microbenchmarks skipping integration, single best run vs. median)
  • Well-scoped: describes when to use and when not to use the checklist
  • No external dependencies or tool requirements — pure reasoning framework
  • Credits original pstack work and documents lineage (ported from discipline to Claude skill)
  • Concise prose that balances completeness with readability
Model: claude-haiku-4-5-20251001Analyzed: Nov 8, 2026

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