designing-surveys▌
refoundai/lenny-skills · updated Apr 8, 2026
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Survey design framework grounded in product research best practices from nine leaders.
- ›Prioritize CSAT over NPS for better statistical properties and business outcome correlation; use 5-7 item scales
- ›Design single-variable questions only; avoid double-barreled phrasing that conflates multiple concepts
- ›Target customers 3-6 months post-signup when memory of the \"before\" state is fresh and relevant
- ›Use MaxDiff (Most/Least) methodology for feature prioritization rather than unlimite
Designing Surveys
Help the user design effective surveys using frameworks from 9 product leaders who have built rigorous research and feedback systems.
How to Help
When the user asks for help with surveys:
- Clarify the goal - Determine if they're measuring satisfaction, identifying problems, or prioritizing features
- Choose the right metric - Help them select between NPS, CSAT, PMF survey, or custom approaches
- Design clean questions - Ensure each question measures one thing precisely
- Target the right respondents - Help them reach users with fresh, relevant experience
Core Principles
NPS is scientifically flawed
Judd Antin: "NPS is the best example of the marketing industry marketing itself. The consensus in the survey science community is that NPS makes all the mistakes. Customer satisfaction, a simple CSAT metric, is better. It has better data properties, it is more precise, it is more correlated to business outcomes." Use CSAT with 5-7 item scales instead.
Force prioritization with constraints
Nicole Forsgren: "Let them pick three, just three. Of those three, how often does this affect you? Is this hourly? Is this daily? Is this weekly?" Limit respondents to their top barriers to keep data clean, then measure frequency to weight impact.
Survey your best customers at the right time
Gia Laudi: "Very importantly, they signed up for your product recently enough that they remember what life was like before. Generally, we say that's in the three to six-month range." Target customers who have been using the product 3-6 months so their memory of the 'before' state is fresh.
Onboarding surveys improve conversion
Laura Schaffer: "We just asked for forgiveness and put these questions into the signup flow. An improved conversion by like 5%, just improved signups." Adding 'good friction' in the form of targeted questions can increase conversion by reassuring users they're in the right place.
Avoid double-barreled questions
Nicole Forsgren: "You're asking four different questions there. If someone answers yes, was it the build? Was it the test? Was it slow or was it flaky?" Ensure each survey question only asks about one specific variable.
Use MaxDiff for feature prioritization
Madhavan Ramanujam: "Identify the most important for you, and the least important. If you do this a few times, you will be able to prioritize the entire feature set in a relative fashion." MaxDiff (Most/Least) surveys are superior to simple ranking for identifying value drivers.
Questions to Help Users
- "What specific decision will this survey inform?"
- "Are you asking about one thing per question, or multiple things?"
- "Who are your 'best' customers and when did they sign up?"
- "Are all scale options visible on mobile without scrolling?"
- "How will you force respondents to prioritize rather than rate everything high?"
Common Mistakes to Flag
- Double-barreled questions - Asking about speed AND complexity in one question
- Too many options - Allowing respondents to select unlimited items instead of forcing prioritization
- Wrong timing - Surveying customers who are too new (no experience) or too old (forgot the 'before')
- NPS worship - Relying on a metric with known scientific flaws over simpler, better alternatives
- Hidden scale options - Mobile surveys where users can't see all options create response bias
Deep Dive
For all 10 insights from 9 guests, see references/guest-insights.md
Related Skills
- Writing North Star Metrics
- Defining Product Vision
- Prioritizing Roadmap
- Setting OKRs & Goals
How to use designing-surveys 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 designing-surveys
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches designing-surveys 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 designing-surveys. Access the skill through slash commands (e.g., /designing-surveys) 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
4.5★★★★★58 reviews- ★★★★★James Verma· Dec 28, 2024
We added designing-surveys from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Alexander Bhatia· Dec 24, 2024
Useful defaults in designing-surveys — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Tariq Reddy· Dec 20, 2024
I recommend designing-surveys for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Charlotte Ndlovu· Dec 12, 2024
designing-surveys fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Advait Li· Dec 4, 2024
Registry listing for designing-surveys matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Alexander Abbas· Nov 23, 2024
Keeps context tight: designing-surveys is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Charlotte Gonzalez· Nov 15, 2024
designing-surveys has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★James Tandon· Nov 11, 2024
Solid pick for teams standardizing on skills: designing-surveys is focused, and the summary matches what you get after install.
- ★★★★★Alexander Rahman· Oct 26, 2024
We added designing-surveys from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Charlotte Perez· Oct 14, 2024
designing-surveys is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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