Analytics Tracking & Measurement Strategy workflow skill. Use this skill when the user needs Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Analytics Tracking & Measurement Strategy workflow skill. Use this skill when the user needs Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-bundle-data-analytics/skills/analytics-tracking from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
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. ---
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Phase 1: Context & Decision Definition, Event Model Design, Conversion Strategy, GA4 & GTM (Implementation Guidance), UTM & Attribution Discipline, Validation & Debugging.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
This skill is applicable to execute the workflow or actions described in the overview.
Use when the request clearly matches the imported source intent: Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Use when copied upstream references, examples, or scripts materially improve the answer.
Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: 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
Examples
Example 1: Ask for the upstream workflow directly
Use @analytics-tracking-v3 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @analytics-tracking-v3 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @analytics-tracking-v3 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @analytics-tracking-v3 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
What you need to know
What action you’ll take
What signal proves it
cosmetic clicks
redundant events
UI noise
intent
Imported Operating Notes
Imported: 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.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-bundle-data-analytics/skills/analytics-tracking, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@ab-test-setup-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@algolia-search-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@algorithmic-art-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@azd-deployment-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: 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
Imported: 🔢 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.
Imported: 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
Imported: 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
Imported: 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
Imported: UTM & Attribution Discipline
UTM Rules
lowercase only
consistent separators
documented centrally
never overwritten client-side
UTMs exist to explain performance, not inflate numbers.
Imported: 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
Imported: Privacy & Compliance
Consent before tracking where required
Data minimization
User deletion support
Retention policies reviewed
Analytics that violate trust undermine optimization.
Imported: 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
Imported: 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?
Imported: Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
