planning-under-uncertainty▌
refoundai/lenny-skills · updated Apr 8, 2026
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Navigate product planning in uncertain environments using adaptive frameworks from 44 product leaders.
- ›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
Planning Under Uncertainty
Help the user navigate product planning when the future is unclear using adaptive planning frameworks from 44 product leaders.
How to Help
When the user asks for help with planning under uncertainty:
- Understand the uncertainty type - Ask what's driving the ambiguity: technical unknowns, market volatility, AI/ML unpredictability, or organizational change
- Assess planning horizon - Determine if they need short-term execution tactics or long-term strategic flexibility
- Match framework to context - Recommend appropriate planning approaches based on their uncertainty profile
- Build in adaptation mechanisms - Help them create checkpoints and decision criteria for pivoting
Core Principles
Embrace optionality over prediction
Amjad Masad: "Being agile, not being stuck with roadmaps, being able to just say, oh, we're just going to switch priorities right away, is going to be super important." In rapidly changing environments like AI, maintain flexibility to pivot when new capabilities emerge rather than committing to rigid long-term plans.
Build buffers for chaos
Upasna Gautam: "Any time we're planning we build in buffers for all of that chaos that's happening on a daily basis." In chaotic environments, planning must include explicit time buffers and contingency plans ranging from days to months depending on scope.
Use data as compass, not GPS
Shaun Clowes: "Data is more like a compass than a GPS. If you look at data as a way of giving you the answer, you're always wrong." Use data to validate or invalidate intuition rather than waiting for it to tell you exactly what to do.
Value learning over winning
Ramesh Johari: "Experimentation was never historically in science about winners and losers... Experimentation is always very hypothesis driven. It's about, what are you learning?" A healthy experimentation culture values learning from "failed" risky bets more than safe, incremental "wins."
Develop reproducible testing processes
Nikita Bier: "Develop a reproducible testing process, and that will actually influence the probability of your success more than anything." Success in uncertain markets is driven by the quality and speed of the testing process rather than the initial idea.
Diagnose before acting in crisis
Alex Hardimen: "There's this incredible humility that was needed to really understand and first diagnose what was actually happening on the platform." Managing through a crisis requires "wartime" humility to accurately diagnose problems before attempting solutions.
Create decision triggers, not fixed plans
Eric Ries: "Give yourself a fixed period of time to take some decisive action and see if it feels better." Build checkpoints into plans where you'll reassess based on what you've learned, not just calendar dates.
Distinguish reversible from irreversible decisions
Claire Hughes Johnson: "Type one, type two decisions. Is it high impact? Is it irreversible? Is it not?" Spend more time on one-way doors and move quickly on reversible decisions that can be adjusted later.
Questions to Help Users
- "What would need to be true for your current plan to work? Which of those assumptions are you least confident about?"
- "If this takes twice as long as expected, what would you do differently? What if it takes half as long?"
- "What's the smallest thing you could ship to learn whether your core hypothesis is correct?"
- "Is this a one-way door or a two-way door decision?"
- "What signals would tell you to pivot or kill this initiative?"
- "How much buffer have you built in for unexpected chaos?"
Common Mistakes to Flag
- Over-planning - Creating detailed long-term roadmaps that create false confidence and resist necessary pivots
- Analysis paralysis - Waiting for perfect information instead of making decisions with 70% confidence
- Ignoring leading indicators - Not tracking intermediate signals that could tell you earlier if you're on track
- Judging experiments by outcomes alone - Not valuing the learning from "failed" experiments that tested important hypotheses
- Planning theater - Spending excessive time on documents and processes that don't reduce actual uncertainty
Deep Dive
For all 52 insights from 44 guests, see references/guest-insights.md
Related Skills
- prioritizing-roadmap
- running-decision-processes
- scoping-cutting
- problem-definition
How to use planning-under-uncertainty on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add planning-under-uncertainty
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches planning-under-uncertainty from GitHub repository refoundai/lenny-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate planning-under-uncertainty. Access the skill through slash commands (e.g., /planning-under-uncertainty) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★73 reviews- ★★★★★Daniel Yang· Dec 28, 2024
We added planning-under-uncertainty from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kaira Martin· Dec 28, 2024
Useful defaults in planning-under-uncertainty — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aisha Brown· Dec 28, 2024
planning-under-uncertainty fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Harper Shah· Dec 24, 2024
Solid pick for teams standardizing on skills: planning-under-uncertainty is focused, and the summary matches what you get after install.
- ★★★★★Ishan Thompson· Dec 16, 2024
planning-under-uncertainty has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Xiao Rahman· Dec 8, 2024
planning-under-uncertainty has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Charlotte Gonzalez· Dec 4, 2024
planning-under-uncertainty reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Noor White· Nov 27, 2024
planning-under-uncertainty reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Arjun Taylor· Nov 23, 2024
planning-under-uncertainty has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Xiao Abbas· Nov 19, 2024
I recommend planning-under-uncertainty for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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