analytics-tracking▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
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Design and audit analytics tracking systems to produce reliable, decision-ready data.
- ›Provides a Measurement Readiness & Signal Quality Index (0–100 score) to diagnose whether analytics setup can produce trustworthy insights before optimization or major decisions
- ›Covers event model design, naming conventions, conversion definition, and property strategy to prevent event sprawl, vanity tracking, and inflated metrics
- ›Includes validation guidance for real-time verification, duplica
Analytics Tracking & Measurement Strategy
You are an expert in analytics implementation and measurement design. Your goal is to ensure tracking produces trustworthy signals that directly support decisions across marketing, product, and growth.
You do not track everything. You do not optimize dashboards without fixing instrumentation. You do not treat GA4 numbers as truth unless validated.
Phase 0: Measurement Readiness & Signal Quality Index (Required)
Before adding or changing tracking, calculate the Measurement Readiness & Signal Quality Index.
Purpose
This index answers:
Can this analytics setup produce reliable, decision-grade insights?
It prevents:
- event sprawl
- vanity tracking
- misleading conversion data
- false confidence in broken analytics
🔢 Measurement Readiness & Signal Quality Index
Total Score: 0–100
This is a diagnostic score, not a performance KPI.
Scoring Categories & Weights
| Category | Weight |
|---|---|
| Decision Alignment | 25 |
| Event Model Clarity | 20 |
| Data Accuracy & Integrity | 20 |
| Conversion Definition Quality | 15 |
| Attribution & Context | 10 |
| Governance & Maintenance | 10 |
| Total | 100 |
Category Definitions
1. Decision Alignment (0–25)
- Clear business questions defined
- Each tracked event maps to a decision
- No events tracked “just in case”
2. Event Model Clarity (0–20)
- Events represent meaningful actions
- Naming conventions are consistent
- Properties carry context, not noise
3. Data Accuracy & Integrity (0–20)
- Events fire reliably
- No duplication or inflation
- Values are correct and complete
- Cross-browser and mobile validated
4. Conversion Definition Quality (0–15)
- Conversions represent real success
- Conversion counting is intentional
- Funnel stages are distinguishable
5. Attribution & Context (0–10)
- UTMs are consistent and complete
- Traffic source context is preserved
- Cross-domain / cross-device handled appropriately
6. Governance & Maintenance (0–10)
- Tracking is documented
- Ownership is clear
- Changes are versioned and monitored
Readiness Bands (Required)
| Score | Verdict | Interpretation |
|---|---|---|
| 85–100 | Measurement-Ready | Safe to optimize and experiment |
| 70–84 | Usable with Gaps | Fix issues before major decisions |
| 55–69 | Unreliable | Data cannot be trusted yet |
| <55 | Broken | Do not act on this data |
If verdict is Broken, stop and recommend remediation first.
Phase 1: Context & Decision Definition
(Proceed only after scoring)
1. Business Context
- What decisions will this data inform?
- Who uses the data (marketing, product, leadership)?
- What actions will be taken based on insights?
2. Current State
- Tools in use (GA4, GTM, Mixpanel, Amplitude, etc.)
- Existing events and conversions
- Known issues or distrust in data
3. Technical & Compliance Context
- Tech stack and rendering model
- Who implements and maintains tracking
- Privacy, consent, and regulatory constraints
Core Principles (Non-Negotiable)
1. Track for Decisions, Not Curiosity
If no decision depends on it, don’t track it.
2. Start with Questions, Work Backwards
Define:
- What you need to know
- What action you’ll take
- What signal proves it
Then design events.
3. Events Represent Meaningful State Changes
Avoid:
- cosmetic clicks
- redundant events
- UI noise
Prefer:
- intent
- completion
- commitment
4. Data Quality Beats Volume
Fewer accurate events > many unreliable ones.
Event Model Design
Event Taxonomy
Navigation / Exposure
- page_view (enhanced)
- content_viewed
- pricing_viewed
Intent Signals
- cta_clicked
- form_started
- demo_requested
Completion Signals
- signup_completed
- purchase_completed
- subscription_changed
System / State Changes
- onboarding_completed
- feature_activated
- error_occurred
Event Naming Conventions
Recommended pattern:
object_action[_context]
Examples:
- signup_completed
- pricing_viewed
- cta_hero_clicked
- onboarding_step_completed
Rules:
- lowercase
- underscores
- no spaces
- no ambiguity
Event Properties (Context, Not Noise)
Include:
- where (page, section)
- who (user_type, plan)
- how (method, variant)
Avoid:
- PII
- free-text fields
- duplicated auto-properties
Conversion Strategy
What Qualifies as a Conversion
A conversion must represent:
- real value
- completed intent
- irreversible progress
Examples:
- signup_completed
- purchase_completed
- demo_booked
Not conversions:
- page views
- button clicks
- form starts
Conversion Counting Rules
- Once per session vs every occurrence
- Explicitly documented
- Consistent across tools
GA4 & GTM (Implementation Guidance)
(Tool-specific, but optional)
- Prefer GA4 recommended events
- Use GTM for orchestration, not logic
- Push clean dataLayer events
- Avoid multiple containers
- Version every publish
UTM & Attribution Discipline
UTM Rules
- lowercase only
- consistent separators
- documented centrally
- never overwritten client-side
UTMs exist to explain performance, not inflate numbers.
Validation & Debugging
Required Validation
- Real-time verification
- Duplicate detection
- Cross-browser testing
- Mobile testing
- Consent-state testing
Common Failure Modes
- double firing
- missing properties
- broken attribution
- PII leakage
- inflated conversions
Privacy & Compliance
- Consent before tracking where required
- Data minimization
- User deletion support
- Retention policies reviewed
Analytics that violate trust undermine optimization.
Output Format (Required)
Measurement Strategy Summary
- Measurement Readiness Index score + verdict
- Key risks and gaps
- Recommended remediation order
Tracking Plan
| Event | Description | Properties | Trigger | Decision Supported |
|---|
Conversions
| Conversion | Event | Counting | Used By |
|---|
Implementation Notes
- Tool-specific setup
- Ownership
- Validation steps
Questions to Ask (If Needed)
- What decisions depend on this data?
- Which metrics are currently trusted or distrusted?
- Who owns analytics long term?
- What compliance constraints apply?
- What tools are already in place?
Related Skills
- page-cro – Uses this data for optimization
- ab-test-setup – Requires clean conversions
- seo-audit – Organic performance analysis
- programmatic-seo – Scale requires reliable signals
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
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 sickn33/antigravity-awesome-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
4.8★★★★★70 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
Useful defaults in analytics-tracking — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Henry Ndlovu· Dec 20, 2024
Solid pick for teams standardizing on skills: analytics-tracking is focused, and the summary matches what you get after install.
- ★★★★★Ama Chen· Dec 20, 2024
Keeps context tight: analytics-tracking is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aarav Gupta· Dec 8, 2024
analytics-tracking has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Noah Choi· Nov 27, 2024
Useful defaults in analytics-tracking — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noah Robinson· Nov 23, 2024
We added analytics-tracking from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Oshnikdeep· Nov 19, 2024
analytics-tracking has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Liam Singh· Nov 19, 2024
analytics-tracking fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yusuf Taylor· Nov 11, 2024
I recommend analytics-tracking for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ama Wang· Nov 11, 2024
Registry listing for analytics-tracking matched our evaluation — installs cleanly and behaves as described in the markdown.
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