Fetches product-analyst from daffy0208/ai-dev-standards and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
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│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/product-analyst
Restart Cursor to activate product-analyst. Access via /product-analyst in your agent's command palette.
⚠
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Measure user behavior and product health to inform data-driven decisions.
Core Principle
What gets measured gets improved. Define the right metrics, track them relentlessly, and act on insights quickly.
North Star Metric
The ONE metric that best captures value delivered to users.
Your North Star should:
✅ Represent real customer value
✅ Correlate with revenue
✅ Be measurable frequently (daily/weekly)
✅ Rally the entire team around one goal
Examples by Product Type:
Communication:Slack: Messages Sent (weekly active)
Zoom: Weekly Meeting Minutes
Discord: Active Servers
Marketplace:Airbnb: Nights Booked
Uber: Completed Rides
Etsy: Gross Merchandise Value (GMV)
Media/Content:Spotify: Time Listening
Netflix: Hours Watched
Medium: Total Time Reading
SaaS/B2B:Asana: Weekly Active Teams
Notion: Collaborative Documents
Salesforce: Deals Closed (CRM value)
Social:Facebook: Daily Active Users (DAU)
Instagram: Posts Shared
Twitter: Tweets per User
How to choose your North Star:
What action represents core value?
If users do this more, do they get more value?
Does this predict revenue?
Can the entire team influence it?
Key Metrics by Category
Acquisition Metrics
Goal: Get users into the product
Traffic Sources:-Organic Search: SEO traffic
-Paid Ads: Google Ads, Facebook Ads
-Referral: Word of mouth, links
-Direct: Typed URL, bookmarked
-Social: Twitter, LinkedIn posts
Key Metrics:-Unique Visitors: Total website visitors
-Sign-ups: Users who created account
-Conversion Rate: Visitors → Sign-ups
-Cost Per Acquisition (CPA): Ad spend / sign-ups
-Source Quality: Which sources convert best?Targets:-Visitor → Sign-up: 2-5% (good), 5-10% (excellent)
-CPA: < $50 (B2C), < $200 (B2B), depends on LTV
Activation Metrics
Goal: Get users to "aha moment"
Activation Definition:- User completes onboarding
- User takes first core action
- User experiences product value
Examples:Slack: Sent 2,000 messages (team is active)
Dropbox: Added file to folder
Twitter: Followed 30 accounts
Airbnb: Completed first booking
Key Metrics:-Activation Rate: Sign-ups → Activated
-Time to Activation: How long to aha moment?-Onboarding Completion: % who finish setup
Targets:-Activation Rate:>40% (good),>60% (excellent)
-Time to Activation: <24 hours (ideal)
Engagement Metrics
Goal: Keep users coming back
Key Metrics:- Daily Active Users (DAU)
- Weekly Active Users (WAU)
- Monthly Active Users (MAU)
-DAU/MAU Ratio (Stickiness): How often users return
-Session Frequency: Times per week user logs in
-Session Duration: Time spent per visit
-Feature Adoption: % using each feature
DAU/MAU Stickiness:Excellent:>40% (Facebook, Slack)
Good: 20-40% (most SaaS)
Needs Work: <20%
Session Frequency Targets:B2C Social: 5-7 times per week
B2B Tools: 3-5 times per week
E-commerce: 1-2 times per week
Retention Metrics
Goal: Prevent churn
Cohort Retention:-Day 1: % still active 1 day after sign-up
-Day 7: % still active 7 days after
-Day 30: % still active 30 days after
Good Retention Curves:Consumer B2C:-D1: 60-80%
-D7: 40-60%
-D30: 30-50%
- Flattening curve (good!)Enterprise B2B:-D1: 80-90%
-D7: 70-80%
-D30: 60-70%
- Very flat curve
Bad Retention:-D1: 40%
-D7: 10%
-D30: 2%
- Steep drop-off = product-market fit issue
Churn Rate:-Monthly Churn: % users who stop using each month
-Target: <5% (consumer), <1% (enterprise)
- Churn = Revenue Leak
Net Retention:- (Starting Users + New - Churned) / Starting Users
-Target:>100% (growth despite churn)
Revenue Metrics
Goal: Monetize effectively
Key Metrics:-MRR (Monthly Recurring Revenue): Predictable monthly income
-ARR (Annual Recurring Revenue): MRR × 12
-ARPU (Average Revenue Per User): Revenue / # users-LTV (Lifetime Value): Total revenue from user over lifetime
-CAC (Customer Acquisition Cost): Sales + marketing / new customers
-LTV:CAC Ratio: Must be > 3:1-Payback Period: Months to recover CAC
Calculations: LTV = ARPU × Average Lifetime (months)
Average Lifetime = 1 / Churn Rate
Example:ARPU: $50/month
Churn: 5% per month
Average Lifetime: 1 / 0.05 = 20 months
LTV: $50 × 20 = $1,000CAC: $300
LTV:CAC = $1,000 / $300 = 3.3:1 (Good!)Targets:-LTV:CAC:>3:1 (minimum),>4:1 (healthy)
-Payback Period: <12 months
-MRR Growth:>10% month-over-month (early stage)
Satisfaction Metrics
Goal: Keep customers happy
NPS (Net Promoter Score):Question: "How likely are you to recommend us?" (0-10)
-Promoters: 9-10-Passives: 7-8-Detractors: 0-6 NPS = % Promoters - % Detractors
Benchmarks:Excellent:>50
Good: 30-50Needs Work: <30
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›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
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share 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
