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lengrufeng2008lengrufeng2008/dsh-skill-library/skills/experiment-design/SKILL.md

experiment-design

Skill0 starsChanged 15 days agoMITView on GitHub

Pre-register the hypothesis, power, metric, and stopping rule.

32 other projects use a version of this.

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Used with

Projects with it also use list-quality-scorecard and positive-reply-scoring.

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.
  • creatop-gtmSkill

    list-quality-scorecard

    Check a lead list before it goes into a sequence. Gives a deliverability readout (firm) and a targeting readout (advisory). Surfaces issues, never blocks. Use before enrolling any list.

    Also in 3 projects that use this

  • creatop-gtmSkill

    positive-reply-scoring

    Classify replies and compute positive reply rate, the metric that predicts revenue. Use to tell whether a campaign is actually working, not just getting replies.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 23 edited versions we read keep these.

  • 2

    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. Distinctive:

    No fabricated sources, data, or test results.

    line 31
  2. Distinctive:

    If the task falls outside the method, say so and stop.

    line 32

“Common” is measured: kept by at least 6 in 10 of the 23 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

Experiment design

Use

Apply this when the task matches the description. Keep the output decision-useful and mark unknowns.

Method

  1. State the decision and the unit of analysis.
  2. Profile grain, coverage, missingness, and leakage.
  3. Choose a method and write its assumptions.
  4. Validate on a holdout or independent slice.

Focus: Experiment design.

Verification

  • Numbers reproduce from the written query/code.
  • Assumptions and exclusions are visible.
  • State clearly what was not verified.

Boundaries

  • This is a checklist, not proof of correctness.
  • No fabricated sources, data, or test results.
  • If the task falls outside the method, say so and stop.

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