conducting-interviews▌
refoundai/lenny-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Behavioral interview framework drawn from 75 product leaders' hiring experience.
- ›Emphasizes behavioral-based questioning to reveal actual capability over rehearsed answers; drill six levels deep into the \"how\" to uncover technical expertise
- ›Guides role-specific competency definition, interview structure design, and question crafting that surface genuine insight rather than confident delivery
- ›Flags common mistakes: performative STAR responses, insufficient probing, hypothetical ques
Conducting Interviews
Help the user conduct effective hiring interviews using frameworks from 75 product leaders who have interviewed thousands of candidates at top companies.
How to Help
When the user asks for help with conducting interviews:
- Understand the role - Ask what position they're hiring for and what competencies matter most
- Design the structure - Help create a consistent, behavioral-based interview process
- Craft the questions - Suggest questions that reveal actual capability, not rehearsed answers
- Evaluate effectively - Guide them on separating signal from noise and avoiding common biases
Core Principles
Use behavioral-based interviewing
Bill Carr: "We created a set of objective criteria that would be used and an interview methodology that would be used in every interview, which was the objective criteria would be our leadership principles, and the methodology would be behavioral based interviewing." Ask for specific past examples, not hypotheticals.
Look past polished delivery
Jackie Bavaro: "Some people sounded really good because they'd say, 'Well, I'll tell you three things. Number one, number two, number three.' And then when I paid attention to my notes, I'd be like, 'Wait, their three ideas weren't actually good ideas.'" Evaluate substance over structure.
Drill six levels deep
Joe Hudson (on Elon's approach): "You ask them six levels down. You improved sales. How did you do that, exactly? Well, we improved the pipeline. How'd you do that, exactly?" True expertise is revealed by drilling into the technical and process-oriented 'how'.
Ask how they prepared
Austin Hay: "I like to ask people how they prepared for the interview. You're really asking how does the person think? How did they plan? How did they take things seriously or not?" Preparation style reveals planning depth and systems thinking.
End with 'anything else?'
Christopher Lochhead: "At the very end you say, 'Hey, Susan, before we wrap, is there anything else?' And often, the most important thing for that person to communicate comes out then." The formal structure ending unlocks authenticity.
Test failure and learning
Annie Pearl: "Talk me through your biggest product flop. What happened and what did you do about it?... The rawer the answer in terms of how bad it was and why, the better." Look for brutal honesty and genuine learning.
Simulate working together
Noam Lovinsky: "I generally like interview questions that allow us to kind of do some work together... getting into the details and really watching each other exercise our craft is really important." Collaborative exercises reveal true capability.
Use the PEARL framework
Jackie Bavaro: "Problem, Epiphany, Action, Result and Learning. What's the problem that you thought was worth solving? What's your epiphany? What's the insight that you had?" This structure ensures candidates demonstrate unique insight, not just activity.
Questions to Help Users
- "What competencies are most critical for this specific role?"
- "Are you testing for skills that can be rehearsed or genuine capability?"
- "How will you distinguish between confident delivery and quality thinking?"
- "What signals true ownership versus 'we' statements that hide contribution?"
- "How are you calibrating across multiple interviewers?"
Common Mistakes to Flag
- Performative interviews - Rewarding rehearsed STAR responses over actual capability
- Not probing deeply enough - Accepting surface answers without drilling into specifics
- High-volume fatigue - Scheduling back-to-back interviews that degrade judgment
- Hypothetical questions - Testing what candidates say they would do instead of what they have done
- Skipping the 'failure' question - Missing the chance to test self-awareness and growth mindset
Deep Dive
For all 91 insights from 75 guests, see references/guest-insights.md
Related Skills
- Writing Job Descriptions
- Evaluating Candidates
- Onboarding New Hires
- Building Team Culture
How to use conducting-interviews 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 conducting-interviews
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches conducting-interviews 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 conducting-interviews. Access the skill through slash commands (e.g., /conducting-interviews) 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★★★★★43 reviews- ★★★★★Ishan Mensah· Dec 20, 2024
I recommend conducting-interviews for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kiara Choi· Dec 16, 2024
Useful defaults in conducting-interviews — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noah Gonzalez· Dec 16, 2024
Registry listing for conducting-interviews matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kaira Menon· Dec 8, 2024
Keeps context tight: conducting-interviews is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Maya Sethi· Nov 27, 2024
Registry listing for conducting-interviews matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Henry Kapoor· Nov 7, 2024
Keeps context tight: conducting-interviews is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Henry Sharma· Oct 26, 2024
conducting-interviews is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ama Liu· Oct 18, 2024
conducting-interviews reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Carlos Sharma· Sep 25, 2024
Keeps context tight: conducting-interviews is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Piyush G· Sep 21, 2024
conducting-interviews is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
showing 1-10 of 43