analytics-tracking▌
kostja94/marketing-skills · updated Apr 8, 2026
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Guides analytics implementation: GA4 setup, event tracking, conversions, and data quality. Applies to web and app tracking across marketing channels.
Analytics: Tracking
Guides analytics implementation: GA4 setup, event tracking, conversions, and data quality. Applies to web and app tracking across marketing channels.
When invoking: On first use, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
User ID
- Purpose: Cross-device, cross-session user identification
- Implementation: Set
user_idwhen user is identified (e.g., login); send to GA4 - Benefit: Accurate attribution across sessions; better audience building
CTA Attribution (Article ROI)
Track CTA clicks on key articles to measure content ROI:
| Action | Purpose |
|---|---|
| Event per CTA | e.g., cta_click with content_url, content_type |
| Conversion | Mark as conversion in GA4 for attribution |
| Use | Compare high vs low performers; optimize CTA placement and copy |
See seo-monitoring for article database and benchmark context.
Infrastructure Requirements
| Component | Purpose |
|---|---|
| Data warehouse | Centralized data; BI reporting |
| Event tracking | User behavior; funnel mapping |
| Attribution | Ad pixels; attribution model; impression-to-sale tracking |
Optimization flow: Clean UTM + conversion events → attribution reports → optimize channel mix.
Scope
- GA4: Web data stream, gtag.js, configuration
- User ID: Cross-device, cross-session identification
- CTA attribution: Per-article conversion tracking for content ROI
- Events: Recommended and custom events
- Conversions: Key events, parameters
- Quality: Naming, testing, validation
GA4 Setup
Prerequisites
- Google Analytics property and web data stream
- Google tag (gtag.js) on all pages
- Measurement ID (e.g.,
G-XXXXXXXXXX)
Enhanced Measurement
Enable in Admin > Data Streams > Enhanced Measurement for automatic tracking of:
- Page views, scrolls, outbound clicks
- Site search, file downloads
- Video engagement (YouTube)
Event Tracking
Event Types
| Type | Description |
|---|---|
| Automatically collected | page_view, first_visit, session_start |
| Enhanced measurement | scroll, click, file_download, etc. |
| Recommended | purchase, sign_up, search, etc. |
| Custom | Business-specific actions |
Naming Conventions
- Length: <=40 characters (GA4 hard limit; longer names are not logged)
- Format:
snake_case, lowercase - Verb first:
download_pdf,submit_form,video_play - Context:
pricing_page_scrollvs genericscroll
gtag.js Syntax
gtag('event', '<event_name>', {
<parameter_name>: <value>,
// e.g. value: 99.99, currency: 'USD'
});
Place below the Google tag snippet. Events fire on page load or user action (e.g., button click).
Recommended Events
| Event | Use | Key Parameters |
|---|---|---|
purchase |
E-commerce | value, currency, items |
sign_up |
Registration | method |
login |
Login | method |
search |
Site search | search_term |
view_item |
Product view | items |
add_to_cart |
Add to cart | items |
Custom Events
- Focus on 15-25 meaningful events aligned with KPIs
- Add parameters for context (e.g.,
content_type,item_id) - Avoid tracking everything; prioritize quality over quantity
Conversions (Key Events)
- Mark important events as conversions in GA4 Admin
- Use for attribution, audiences, and reporting
- Typical: purchase, sign_up, lead, contact
Attribution & Conversion Optimization
Attribution models determine how conversion credit is assigned across touchpoints. Use attribution data to optimize ads and growth channels.
| Model | Use |
|---|---|
| Data-driven (GA4 default) | ML assigns credit by actual contribution; best for multi-touch journeys |
| Last-click | 100% to final touchpoint; simple but undervalues awareness/consideration |
Optimization flow: Clean UTM (source, medium, campaign) + conversion events → GA4 attribution reports → compare channels by attributed conversions → reallocate budget to ads/channels that drive results. Inconsistent UTM fragments data; multi-touch attribution requires reliable touchpoint data.
Reference: UTM.io – UTMs for Marketing Attribution, GA4 – Get started with attribution
Testing & Validation
| Tool | Use |
|---|---|
| Realtime | See events as they fire |
| DebugView | Detailed event/parameter inspection; requires debug mode |
| GA4 Debug mode | gtag('config', 'G-XXX', { 'debug_mode': true }); or GTM preview |
- Test before launch; verify parameters and naming
- Check for duplicate events, missing values
Output Format
- Event list (name, trigger, parameters)
- Implementation notes (gtag or GTM)
- Conversion mapping
- Testing checklist
Related Skills
- traffic-analysis: UTM, source attribution; attribution for channel optimization
- ai-traffic-tracking: AI traffic in GA4
- google-search-console: GSC analysis (correlate with GA4)
- seo-monitoring: Article database, benchmark, full SEO monitoring framework
How to use analytics-tracking 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 analytics-tracking
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches analytics-tracking from GitHub repository kostja94/marketing-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 analytics-tracking. Access the skill through slash commands (e.g., /analytics-tracking) 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
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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
4.6★★★★★61 reviews- ★★★★★Layla Rao· Dec 28, 2024
Keeps context tight: analytics-tracking is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Evelyn Nasser· Dec 28, 2024
Registry listing for analytics-tracking matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Benjamin Yang· Dec 28, 2024
analytics-tracking reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chaitanya Patil· Dec 16, 2024
Keeps context tight: analytics-tracking is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Arya Wang· Dec 12, 2024
analytics-tracking is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Luis Srinivasan· Dec 8, 2024
I recommend analytics-tracking for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Camila Lopez· Nov 27, 2024
Solid pick for teams standardizing on skills: analytics-tracking is focused, and the summary matches what you get after install.
- ★★★★★Kofi Nasser· Nov 19, 2024
Registry listing for analytics-tracking matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aanya Mehta· Nov 19, 2024
Keeps context tight: analytics-tracking is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Emma Johnson· Nov 19, 2024
analytics-tracking is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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