Navigate product planning in uncertain environments using adaptive frameworks from 44 product leaders.
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Emphasizes optionality over prediction: maintain flexibility to pivot when new information emerges rather than committing to rigid roadmaps, especially critical in fast-moving AI/ML contexts
Teaches decision frameworks including type one/two decisions (reversible vs. irreversible), data as compass not GPS, and explicit decision triggers tied to learning rather than calendar dates
Co
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionplanning-under-uncertaintyExecute the skills CLI command in your project's root directory to begin installation:
Package manager
npx skills add https://github.com/refoundai/lenny-skills --skill planning-under-uncertaintyFetches planning-under-uncertainty from refoundai/lenny-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate planning-under-uncertainty. Access via /planning-under-uncertaintyin your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Package manager
npx skills add https://github.com/refoundai/lenny-skills --skill planning-under-uncertaintyWorks with
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0
this week
616
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upvotes
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
shadcn/improve
mattpocock/skills
othmanadi/planning-with-files
garrytan/gstack
davila7/claude-code-templates
intellectronica/agent-skills
We added planning-under-uncertainty from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in planning-under-uncertainty — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
planning-under-uncertainty fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: planning-under-uncertainty is focused, and the summary matches what you get after install.
planning-under-uncertainty has been reliable in day-to-day use. Documentation quality is above average for community skills.
planning-under-uncertainty has been reliable in day-to-day use. Documentation quality is above average for community skills.
planning-under-uncertainty reduced setup friction for our internal harness; good balance of opinion and flexibility.
planning-under-uncertainty reduced setup friction for our internal harness; good balance of opinion and flexibility.
planning-under-uncertainty has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend planning-under-uncertainty for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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