assumption-mapper
Skill33 starsChanged 9 months agoMITView on GitHub
Exposes hidden risks by identifying and ranking assumptions across desirability, feasibility, and viability categories. Use when evaluating new products or features to surface the highest-risk assumption to test first.
Files
Assumption Mapper
Purpose
Expose hidden risks by identifying and ranking assumptions.
Categories
- Desirability (users want it)
- Feasibility (can we build it)
- Viability (business works)
Steps
- List assumptions in each category.
- Rate risk and uncertainty.
- Identify highest-risk assumption to test first.
Output
Key Assumptions
Highest-Risk Assumption
Validation Method
Decision Before Building
References
See worked example for a complete scenario.
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- imohammadfaraz/claude-skills-for-product-manager★ 19same name
- varunk130/ai-customer-discovery-skills★ 2same name
- hollandkevint/thinkhaven-method-kit★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository14 other files it gives its agents.
- experiment-designSkill
- feature-adoption-analyzerSkill
- jtbd-extractorSkill
- launch-readinessSkill
- outcome-definitionSkill
- post-launch-learningSkill
- prd-criticSkill
- problem-claritySkill
- retention-drop-diagnoserSkill
- roadmap-reality-checkerSkill
- stakeholder-alignment-checkerSkill
- tradeoff-articulatorSkill
- user-segment-prioritizerSkill
- value-vs-effortSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.