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Experiment Results

Checked
product-on-purpose·v1.0.0·other·Apache-2.0

<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->

Install on your platform

Showing OpenClaw.

  1. 1

    Run this in your terminal

    terminal
    $

What we know about this skill

Publisher

product-on-purpose

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned product-on-purpose/pm-skills at 1cef1a9eae.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
1cef1a9eae
Manifest
skills/measure-experiment-results/SKILL.md
Scanned
5 files, 651 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill analyzes experiment results from Product on Purpose, a resource for product managers. It extracts key metrics like success rate, confidence intervals, and sample sizes to provide concise summaries of A/B test outcomes. The tool helps users quickly understand the statistical significance and practical implications of their experiments.

When to use it

  • Evaluate the impact of a new feature on user engagement by analyzing its A/B test results.
  • Determine if a pricing change significantly affects conversion rates based on experiment data.
  • Assess the effectiveness of different onboarding flows using metrics derived from experimentation.
  • Summarize findings for stakeholders who need to understand experiment outcomes without diving into detailed statistical reports.

Key capabilities

  • Extracts success rate from experiment results.
  • Calculates and displays confidence intervals.
  • Identifies sample sizes used in experiments.
  • Provides concise summaries of A/B test outcomes.

Example prompts

  • "Summarize the results of this Product on Purpose experiment: [paste experiment data here]"
  • "What was the success rate and confidence interval for this experiment? [paste experiment data here]"
  • "Give me a brief overview of these experiment findings, highlighting any statistically significant changes. [paste experiment data here]"

Tips & gotchas

The skill relies on structured data from Product on Purpose experiments; ensure the input adheres to their format for accurate analysis. Results should always be interpreted in context with other relevant business information and qualitative feedback.