measure-experiment-results — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited measure-experiment-results (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
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Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->
An experiment results document captures what happened when you tested a hypothesis, including statistical outcomes, segment analysis, learnings, and clear recommendations. Good results documentation turns individual experiments into organizational knowledge that improves future decision-making.
measure-experiment-designiterate-pivot-decision; this skill reports the evidence, that one decidesiterate-lessons-logmeasure-survey-analysisWhen asked to document experiment results, follow these steps:
Provide context: what was tested, when it ran, how much traffic it received. Link to the original experiment design document if one exists.
Remind readers what you believed would happen and why. This frames the results interpretation.
Show the primary metric outcome clearly: what were the values for control and treatment? Include statistical significance (p-value), confidence intervals, and sample sizes. Be honest about whether results are conclusive.
Present guardrail metrics that ensure you didn't cause unintended harm. Note any secondary metrics that moved unexpectedly.both positive and negative.
Look for differential effects across user segments (platform, tenure, plan type, etc.). Sometimes overall results mask important segment-level insights.
What did you learn beyond the numbers? Include surprising findings, questions raised, and implications for the product hypothesis. Negative results are valuable learnings.
Be clear: should we ship, iterate, or kill? Support the recommendation with the evidence. If the decision is nuanced, explain the trade-offs.
Specify what happens now.engineering work to ship, follow-up experiments, metrics to continue monitoring, or documentation to update.
Use the template in references/TEMPLATE.md to structure the output. A complete readout fills every template section: Summary; Hypothesis Recap; Results; Segment Analysis; Visualization; Learnings; Recommendation; Next Steps; and Appendix.
Before finalizing, verify:
See references/EXAMPLE.md for a completed example.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.