Brainstorm Experiments Existing
- 1.9k installs
- 24.9k repo stars
- Updated July 3, 2026
- phuryn/pm-skills
brainstorm-experiments-existing designs cheap validation experiments for existing-product feature assumptions.
About
The brainstorm-experiments-existing skill helps teams test feature assumptions before full build on live products. Agents clarify the idea and assumptions, suggest experiments per assumption such as first-click tests, fake doors, technical spikes, production A/B tests with mitigation, wizard-of-oz flows, and behavioral surveys. Principles demand measuring behavior not opinions, responsible testing without user or business harm, and maximum learning per effort. Each experiment specifies assumption, method, metric, and success threshold in tables or markdown. Further reading links to Product Compass experiment libraries and assumption canvases. Use when validating feature ideas cheaply on existing products or planning discovery experiments.
- Maps assumptions to prototypes, fake doors, spikes, and A/B tests.
- Requires behavioral metrics and explicit success thresholds.
- Includes production test risk mitigation guidance.
- Outputs structured experiment tables with assumption linkage.
- Focuses on existing products with shipped user bases.
Brainstorm Experiments Existing by the numbers
- 1,919 all-time installs (skills.sh)
- +78 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #258 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
brainstorm-experiments-existing capabilities & compatibility
- Capabilities
- experiment design · metric definition · risk mitigation planning
- Use cases
- research · testing · planning
What brainstorm-experiments-existing says it does
Design low-effort experiments to test product assumptions before committing to full implementation.
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| Installs | 1.9k |
|---|---|
| repo stars | ★ 24.9k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 3, 2026 |
| Repository | phuryn/pm-skills ↗ |
How do we test this feature assumption before committing to a full build?
Design low-effort experiments to validate assumptions on existing products using prototypes, fake doors, spikes, and A/B tests.
Who is it for?
Existing products validating incremental feature hypotheses.
Skip if: Brand-new venture assumption mapping without a shipped product.
When should I use this skill?
User plans product experiments, fake door tests, or assumption validation.
What you get
Experiment table with methods, metrics, thresholds, and risk mitigations per assumption.
Files
Design Experiments (Existing Product)
Design low-effort experiments to test product assumptions before committing to full implementation.
Context
You are helping a product team design experiments for $ARGUMENTS. The team has a feature idea and assumptions that need validation.
If the user provides files (PRDs, assumption lists, designs), read them first.
Instructions
The user will describe their idea and assumptions. Work through these steps:
1. Clarify the idea and assumptions: Confirm what the team wants to build and what they need to validate.
2. Suggest experiments for each assumption. Consider methods like:
- First-click testing or task completion with a prototype
- Feature stubs or fake door tests
- Technical spikes
- A/B tests on production (with risk mitigation)
- Wizard of Oz approaches
- Survey-based validation (behavioral, not opinion-based)
3. Key principles to follow:
- Measure actual behavior, not users' opinions
- Test responsibly — don't put users or the business at risk
- For production tests (e.g., A/B tests), explain risk mitigation strategies
- Aim for maximum validated learning with minimal effort
4. For each experiment, specify:
- Assumption: What do we believe?
- Experiment: What exactly will we do to validate it?
- Metric: What will be measured?
- Success threshold: The expected value if we are right
Think step by step. Present experiments in a clear table or structured format. Save as markdown if substantial.
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Further Reading
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Use for experiment design on shipped products; pair with prioritize-assumptions when the assumption backlog needs ranking first.
FAQ
What experiment types are suggested?
Prototypes, fake doors, spikes, A/B tests, wizard-of-oz, and behavioral surveys.
Should we trust user opinions?
No. Measure actual behavior with clear success thresholds.
What must each experiment include?
Assumption, method, metric, and success threshold if the belief is true.
Is Brainstorm Experiments Existing safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.