Documents the results of a completed experiment or A/B test with statistical analysis, learnings, and recommendations. Use after experiments conclude to communicate findings, inform decisions, and build organizational knowledge.
SkillScore95Maintained
Automatic
◆Product Management pack
Last verified 8/18/2026
What it changes
Passed 21 of 22 test cases with the skill, and 17 of 22 without it.
Without skill77%
With skill95%
Checks it passed that the model alone missed
Includes a Visualization section in the output structure
Includes a confidence interval for the conversion rate difference
Includes a segment-level breakdown to check if subgroup differences exist
and 2 more in the full report
5 fixed16 passed either way0 still failing1 broke
Pass-rate lift
+18 pp
Tokens
+61%
Agent turns
0%
Response time
+49%
Model gemini-3.6-flashMeasured 8/18/2026v121 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
Our checkout button color test finished with Control at 3.2% conversion and Treatment at 3.8% across 50,000 users per arm. The p-value was 0.01. Please write up the primary results section for our team update.
What was checked, without and with the skill
Checked
Without skill
With skill
Reports sample sizes for both Control and Treatment groups
Passed
Passed
Includes a confidence interval for the conversion rate difference
Missed
Passed
case-02 · gemini-3.6-flash · 8/18/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md67 lines3 files
Experiment Results
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.
When to Use
After an A/B test or experiment reaches statistical significance
When an experiment is ended early (for any reason)
To communicate findings to stakeholders who weren't involved
During decision-making about whether to ship, iterate, or kill a feature
To build a repository of learnings that inform future experiments
When NOT to Use
The experiment is not designed or run yet -> use measure-experiment-design
The results demand a direction decision -> use iterate-pivot-decision; this skill reports the evidence, that one decides
You want the transferable learning banked for the organization -> follow up with iterate-lessons-log
Your data is survey responses, not a controlled experiment -> use measure-survey-analysis
Instructions
When asked to document experiment results, follow these steps:
Summarize the Experiment
Provide context: what was tested, when it ran, how much traffic it received. Link to the original experiment design document if one exists.
Restate the Hypothesis
Remind readers what you believed would happen and why. This frames the results interpretation.
Present Primary Results
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.
Analyze Secondary Metrics
Present guardrail metrics that ensure you didn't cause unintended harm. Note any secondary metrics that moved unexpectedly.both positive and negative.
Segment the Data
Look for differential effects across user segments (platform, tenure, plan type, etc.). Sometimes overall results mask important segment-level insights.