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Yeachan-HeoYeachan-Heo/oh-my-claude-sisyphus/skills/benchmark-checklist/SKILL.md

benchmark-checklist

Skill41k starsChanged 4 days agoMITView on GitHub

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.

42 other projects use a version of this.

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Used with

Projects with it also use principle-explain-the-number, poteto-help and 4 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • ianclearySkill

    principle-explain-the-number

    Apply before you trust, report, or act on a number you measured: a speedup, a regression, a throughput, a latency, or an eval result. Find what limits it, and rule out that it measured something other than the work you think.

    Also in 28 projects that use this

  • ianclearySkill

    poteto-help

    Guides users through pstack setup, /poteto-mode, and picking the skill, playbook, or principle for a task. Type /poteto-help with a question.

    Also in 14 projects that use this

  • jaydubya818Skill

    principle-attack-the-premise

    Apply when repeated fixes fail and a shared assumption may be wrong.

    Also in 27 projects that use this

  • jaydubya818Skill

    principle-test-behavior-not-implementation

    Apply when tests mirror internals instead of asserting observable outcomes.

    Also in 28 projects that use this

  • eryzerzSkill

    make-bot-ui

    Use when building a custom UI (page, dashboard, buttons) that should wake a Grok Bot over a webhook, when the user must provide a webhook sender key, or when exposing that UI on Tailscale.

    Also in 15 projects that use this

  • +1 more from jaydubya818

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 39 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run this checklist before you report, commit, or act on any measured number: a speedup, a regression, a throughput, a latency, or an eval …

    line 17
  2. Distinctive:

    no measurement is involved — do not block prose claims about unmeasured behavior on this checklist.

    line 29
  3. Distinctive:

    Run enough repetitions to see the spread — at least five, more in noisy environments.

    line 51
  4. Very specific:

    Every number carries its evidence and its label in the same sentence — measured, inferred, or guess.

    line 66
  5. Distinctive:

    Only after all five sections pass does the number enter a reply, a PR description, or a decision.

    line 73

“Common” is measured: kept by at least 6 in 10 of the 39 edited versions of this file we could read. “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

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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