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TheOnlyFusionCubeTheOnlyFusionCube/potetos-for-everyone/skills/principle-explain-the-number/SKILL.md

principle-explain-the-number

Skill18 starsChanged 53 days agoMITView on GitHub

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.

We split this skill into the rules it gives an agent

1 rule. 1 is common, 0 are distinctive, and 0 are very specific to this project.

  • 1

    Common

    Most of the 25 edited versions we read keep these.

  • 0

    Distinctive

    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. Common:

    Keep the evidence with the number: Put the run count, the spread, and the limiter in the notes or a linked artifact.

    line 12

“Common” is measured: kept by at least 6 in 10 of the 25 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

Portable adaptation of Lauren Tan's pstack v0.15.30. Use available host capabilities and the fallbacks in PORTABILITY.md; do not claim unavailable tools, subagents, or runtime evidence.

Explain the Number

  • Ask "why not double?" Name the resource or code path that bounds the result, such as a core, a lock, the disk, the network, or the load generator itself. Get it from a profile or from system counters taken during a run, then map it to source. A guess from reading the code is not a limiter. If you cannot say why the number is not twice as good, you do not know what you measured.
  • List what else the number could be measuring, and rule out each one with evidence. The usual suspects are errors, skipped or cached work, code that never ran, an untuned side (one left on default settings), noise, and a piece too small to matter end to end.
  • Keep the evidence with the number. Put the run count, the spread, and the limiter in the notes or a linked artifact.

For a performance number, run the full procedure with the benchmark-checklist skill. For an eval result, ask the same of the trials: did every run do the task, does the gap hold across trials and models, and does the scenario matter.

You skipped this when the evidence behind a number has no run count, no spread, or no named limiter, or when the time saved is larger than the time the changed piece took.

Distinct from Prove It Works, which checks that an output is real.

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