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Trustworthy Experiments

Checked
wdavidturner·vlatest·other·MIT

This skill generates verifiable, reproducible experimental results, enhancing trust and transparency in AI model development & validation.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add wdavidturner/product-skills --skill trustworthy-experiments -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

wdavidturner

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 wdavidturner/product-skills at 103d6540d3.

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

Commit
103d6540d3
Manifest
skills/trustworthy-experiments/SKILL.md
Scanned
20 files, 1,509 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 enables AI agents to design and execute trustworthy experiments. It focuses on ensuring experimental designs are robust, reproducible, and ethically sound. The agent can generate hypotheses, define metrics, and outline procedures to minimize bias and maximize the reliability of results.

When to use it

  • Product Validation: Before launching a new feature, use this skill to design an experiment that tests its impact on key user behaviors.
  • A/B Testing Optimization: Refine existing A/B tests by ensuring proper randomization, control groups, and statistical significance.
  • Research & Development: Structure internal research projects with rigorous experimental protocols to validate new ideas and approaches.
  • Ethical AI Evaluation: Design experiments to assess the fairness and potential biases of AI models before deployment.

Key capabilities

  • Hypothesis generation
  • Metric definition
  • Experimental procedure outlining
  • Bias mitigation strategies
  • Reproducibility planning

Example prompts

  • "Design an experiment to test if a new onboarding flow increases user activation."
  • "Outline the steps for an A/B test comparing two different pricing models."
  • "Create a plan to evaluate whether our AI-powered recommendation engine exhibits gender bias."

Tips & gotchas

This skill requires a good understanding of experimental design principles. While it can generate plans, users should review and validate them with domain expertise to ensure they are appropriate for the specific context.