Measure what matters and make 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-analyticsExecute the skills CLI command in your project's root directory to begin installation:
Fetches product-analytics 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-analytics. Access via /product-analytics 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.
Submit your Claude Code skill and start earning
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
0
total installs
0
this week
22
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
22
stars
Measure what matters and make data-driven decisions.
The ONE metric that represents customer value
Examples:
Slack: Weekly Active Users
Airbnb: Nights Booked
Spotify: Time Listening
Shopify: GMV
Your North Star should: ✅ Represent customer value
✅ Correlate with revenue
✅ Be measurable frequently
✅ Rally the team
North Star Metric
├── Input Metrics (drive North Star)
│ ├── Acquisition
│ ├── Activation
│ └── Retention
└── KPIs (business health)
├── Revenue
├── Churn
└── LTV
// Track user actions
analytics.track('Button Clicked', {
button_name: 'signup',
page: 'homepage',
user_id: '123'
})
// Track page views
analytics.page('Homepage', {
referrer: document.referrer,
path: window.location.pathname
})
// Identify users
analytics.identify('user-123', {
email: '[email protected]',
plan: 'pro',
created_at: '2024-01-15'
})
Sign-up Funnel:
1. Land on homepage: 10,000 (100%)
2. Click signup: 2,000 (20%)
3. Fill form: 1,000 (10%)
4. Verify email: 800 (8%)
5. Complete onboarding: 400 (4%)
Insights:
- Biggest drop: Homepage to signup (80% lost)
- Fix: Clarify value prop, add social proof
Week 1 Cohort (Jan 1-7):
- D1: 80% active
- D7: 40% active
- D30: 20% active
Week 2 Cohort (Jan 8-14):
- D1: 85% active (+5%)
- D7: 50% active (+10%)
- D30: 30% active (+10%)
Insight: Onboarding changes improved retention!
Good Retention:
- D1: 60-80%
- D7: 40-60%
- D30: 30-50%
- Flattening curve (good!)
Bad Retention:
- D1: 40%
- D7: 10%
- D30: 2%
- Steep drop-off (bad!)
## Tools
```yaml
Event Tracking:
- Mixpanel (best for products)
- Amplitude (good alternative)
- PostHog (open-source)
Session Recording:
- FullStory
- LogRocket
- Hotjar
A/B Testing:
- Optimizely
- VWO
- Google Optimize (free)
Executive Dashboard:
- North Star Metric (big number)
- Revenue (MRR/ARR)
- Key metric trends (graphs)
Product Dashboard:
- Active users (DAU/WAU/MAU)
- Feature usage
- Retention cohorts
- Funnels
Marketing Dashboard:
- Traffic sources
- Conversion rates
- Cost per acquisition
- ROI by channel
Great analytics:
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
product-analytics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
product-analytics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: product-analytics is the kind of skill you can hand to a new teammate without a long onboarding doc.
product-analytics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
product-analytics reduced setup friction for our internal harness; good balance of opinion and flexibility.
product-analytics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend product-analytics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
product-analytics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
product-analytics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added product-analytics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
showing 1-10 of 71