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experiment-writeup

Skill1 starsChanged 7 months agoMITView on GitHub

Help PMs write up experiment results. Use when documenting A/B test outcomes, feature experiment learnings, or sharing results with stakeholders.

1 other project uses a version of this.

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The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    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:

    When asked "help me write up the results"

    line 16
  2. Distinctive:

    Use plain language; avoid jargon

    line 58
  3. Distinctive:

    Call out statistical significance explicitly

    line 60

≈ “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

Experiment Writeup

Help PMs document experiment results in a structured format. Turns raw data into clear narratives with hypothesis, methodology, results, learnings, and decision.

When to Use

  • Documenting A/B test outcomes
  • Writing up feature experiment results
  • Sharing experiment learnings with stakeholders
  • Creating a record for future reference
  • When asked "help me write up the results"

Process / Template

1. Gather the Data

  • Hypothesis (original statement)
  • Experiment design (variants, duration, sample size)
  • Primary, secondary, and guardrail metric results
  • Statistical significance (p-values, confidence intervals)
  • Any qualitative feedback or observations

2. Structure the Writeup

Hypothesis

  • Restate the original hypothesis
  • Brief context on why we ran this

Methodology

  • Variants tested (control vs. treatment)
  • Duration and sample size
  • Target segment
  • Any caveats (traffic issues, external events)

Results

  • Primary metric — direction, magnitude, significance
  • Secondary metrics — supporting or conflicting signals
  • Guardrail metrics — did anything regress?

Learnings

  • What did we learn? (beyond the numbers)
  • Surprises or unexpected findings
  • Implications for future work

Decision

  • Ship / Iterate / Kill
  • Rationale for the decision
  • Next steps (if iterating)

3. Write Clearly

  • Lead with the decision and key takeaway
  • Use plain language; avoid jargon
  • Include numbers with context (e.g., "+12% vs. control")
  • Call out statistical significance explicitly

Output

A structured Experiment Writeup suitable for:

  • Stakeholder sharing (Slack, email)
  • Internal documentation
  • Experimentation platform notes
  • Retrospectives and planning

Format: concise, scannable, decision-oriented. Typically 1–2 pages or equivalent in markdown.

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