Individual skill
product-experiments
Help users design, execute, and analyze product experiments to validate hypotheses and measure true incremental impact while avoiding common statistical pitfalls.
skills/product-experiments/SKILL.md
Source commit 13598cc54e09399bc1bc1398b0fca284110efb2f
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Instructions only
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,477
- Repository last updated
- Jul 16, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agents Repository topic
- ai-assistant Repository topic
- claude Repository topic
- claude-code Repository topic
- lenny-rachitsky Repository topic
- llm Repository topic
- pm-tools Repository topic
- product-management Repository topic
- skills Repository topic
- product-skills Repository topic
- design Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 8,053 bytesreferences/artifacts.mddoc · 34,203 bytesreferences/guest-insights.mddoc · 16,225 bytes
Read excerpt
# Product Experimentation Excellence
Drive measurable growth and mitigate risk through rigorous A/B testing and data-driven learning.
Help the user with product experimentation excellence using insights from 9 guests and posts across Lenny's Podcast and Newsletter.
## How to Help
1. **Hypothesis Definition** - Guide the user in drafting clear, falsifiable hypotheses based on user behavior theories.
2. **Experimental Design** - Help determine the right metrics, sample sizes, and guardrail metrics for a clean test.
3. **Statistical Analysis** - Support the interpretation of p-values, confidence intervals, and potential sample ratio mismatches.
4. **Strategic Evaluation** - Assist in deciding whether to ship, iterate, or kill a feature based on experiment results and long-term business impact.
## Core Principles