Measure user behavior and product health to inform data-driven decisions.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionproduct-analystExecute the skills CLI command in your project's root directory to begin installation:
Fetches product-analyst from daffy0208/ai-dev-standards and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate product-analyst. Access via /product-analyst in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Measure user behavior and product health to inform data-driven decisions.
What gets measured gets improved. Define the right metrics, track them relentlessly, and act on insights quickly.
The ONE metric that best captures value delivered to users.
Your North Star should:
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:
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
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)
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
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)
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 = $1,000
CAC: $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)
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: 30-50
Needs Work: <30
✓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
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
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.6★★★★★50 reviews- AAma Smith★★★★★Dec 28, 2024
product-analyst reduced setup friction for our internal harness; good balance of opinion and flexibility.
- DDev Dixit★★★★★Dec 28, 2024
product-analyst is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- DDev Gupta★★★★★Dec 20, 2024
product-analyst fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- NNaina Ghosh★★★★★Dec 8, 2024
Registry listing for product-analyst matched our evaluation — installs cleanly and behaves as described in the markdown.
- NNaina Gupta★★★★★Dec 4, 2024
Keeps context tight: product-analyst is the kind of skill you can hand to a new teammate without a long onboarding doc.
- OOmar Bhatia★★★★★Nov 23, 2024
We added product-analyst from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- MMia Nasser★★★★★Nov 19, 2024
product-analyst fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- AAnika Mehta★★★★★Nov 11, 2024
product-analyst is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- KKwame Garcia★★★★★Oct 14, 2024
product-analyst fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- WWilliam Martin★★★★★Oct 10, 2024
We added product-analyst from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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