problem-definition▌
refoundai/lenny-skills · updated Apr 8, 2026
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Help users articulate problems clearly before pursuing solutions, using frameworks from 91 product leaders.
- ›Guides users through understanding current framing, identifying struggling moments, separating problems from solutions, and validating problem urgency
- ›Covers eight core principles including avoiding the shiny object trap, prototyping to define problems, and spending more time on problem understanding than solution design
- ›Includes diagnostic questions to uncover user context, wo
Problem Definition
Help the user define problems clearly before jumping to solutions using frameworks from 91 product leaders.
How to Help
When the user asks for help with problem definition:
- Understand the current framing - Ask how they're currently thinking about the problem
- Dig into the struggling moment - Help them articulate the specific context where users feel stuck
- Separate problem from solution - Ensure they haven't conflated a desired feature with the underlying need
- Validate the problem matters - Help them confirm the problem is urgent and widespread enough to solve
Core Principles
Digitizing analog isn't enough
Bret Taylor: "Why use this instead of the Yellow Pages? It was a digital version of something that had come before." Simply digitizing an analog predecessor often fails because it lacks a native reason to exist on the new platform. Ask "why should a customer give this the time of day?"
Prototype to define the problem
Jake Knapp + John Zeratsky: "This idea of getting unstuck and turning maybe some abstract ideas or some concepts that you've been discussing, turning that into a concrete prototype, something that you can look at and you can click around." Moving from abstract concepts to concrete prototypes is the fastest way to define and solve a problem.
Avoid the shiny object trap
Marily Nika: "There is something called the shiny object trap, and I'm always telling people, 'Hey, don't do AI for the sake of doing AI.' Make sure there is a problem there." Ensure AI (or any new technology) is used to solve a specific, validated user pain point rather than for its own sake.
Struggling moments cause demand
Bob Moesta: "A struggling moment causes demand. And you start to realize that in some cases that struggling moment exists and can exist for a long time and nobody solved it." Demand is created by a specific "struggling moment" in a user's life, not by the product itself. Study the context that makes users' behavior rational.
See the end from the beginning
Ryan Singer: "We are not going to start something unless we can see the end from the beginning. We're not going to take a big concept and then say, 'What's the estimate for this thing?'" Ensure the team can visualize the completed feature before committing resources. Avoid starting with fuzzy concepts.
The solution is what customers buy
Marty Cagan: "People don't buy the problem, they buy your solution. Obviously they don't buy it if it's not solving something they care about, but there are many products that are solving what they care about." While understanding the problem is necessary, competitive advantage comes from the quality of the solution. Don't over-rotate on problem validation if the problem is well-understood.
Spend more time on the problem
Christopher Lochhead: "Spend more time on the problem than the solution." Deeply understanding the customer's perspective of the problem is more valuable than internal product brainstorming. Listen to hear their perspective rather than just pitching your solution.
Qualify the problem type
Christopher Miller: "I don't know that we even talk about problems without a qualifier. Are we talking about a business problem? Are we talking about a customer problem?" Effective problem definition requires distinguishing between business needs and customer pain points to avoid "customer-hostile" solutions.
Questions to Help Users
- "What is the user doing right before they encounter this problem? What are they trying to accomplish?"
- "Why hasn't this problem been solved already? What makes it hard?"
- "Is this a business problem or a customer problem? Are they the same?"
- "If you solved this problem perfectly, how would the user's life be different?"
- "Can you describe a specific person experiencing this problem in a specific moment?"
- "What are people doing today to work around this problem?"
Common Mistakes to Flag
- Solution-first thinking - Starting with a feature idea and working backward to justify it
- Technology-first thinking - Wanting to use AI/blockchain/etc. and looking for problems to apply it to
- Abstract problem statements - Defining problems so broadly they don't point to any specific solution
- Conflating business and customer problems - Solving for internal metrics without addressing user needs
- Skipping the struggling moment - Not understanding the specific context where pain occurs
Deep Dive
For all 123 insights from 91 guests, see references/guest-insights.md
Related Skills
- positioning-messaging
- prioritizing-roadmap
- scoping-cutting
- product-taste-intuition
How to use problem-definition 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 problem-definition
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches problem-definition 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 problem-definition. Access the skill through slash commands (e.g., /problem-definition) 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▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★50 reviews- ★★★★★Dhruvi Jain· Dec 20, 2024
Useful defaults in problem-definition — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aditi Sanchez· Dec 20, 2024
Useful defaults in problem-definition — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Li Gupta· Dec 20, 2024
Solid pick for teams standardizing on skills: problem-definition is focused, and the summary matches what you get after install.
- ★★★★★Emma Khan· Dec 8, 2024
problem-definition has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aisha Abebe· Nov 27, 2024
problem-definition fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hassan Agarwal· Nov 27, 2024
Useful defaults in problem-definition — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Oshnikdeep· Nov 11, 2024
problem-definition has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Hassan Li· Nov 11, 2024
problem-definition has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Tariq Choi· Nov 11, 2024
I recommend problem-definition for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aisha Yang· Oct 18, 2024
We added problem-definition from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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