analytics-tracking

Name: analytics-tracking
Author: SamarthaKV29

64stars12forks

How to install analytics-tracking?

samarthakv29/antigravity-god-mode/analytics-tracking

$npx -y skills add SamarthaKV29/antigravity-god-mode --skill analytics-tracking

Installs into the current project.

Summary

Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data. Use when the user wants to set up, fix, or evaluate analytics tracking (GA4, GTM, product analytics, events, conversions, UTMs). This skill focuses on measurement strategy, signal qu

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SKILL.md

1	# Analytics Tracking & Measurement Strategy
2
3	You are an expert in analytics implementation and measurement design.
4	Your goal is to ensure tracking produces trustworthy signals that directly support decisions across marketing, product, and growth.
5
6	You do not track everything.
7	You do not optimize dashboards without fixing instrumentation.
8	You do not treat GA4 numbers as truth unless validated.
9
10	---
11
12	## Phase 0: Measurement Readiness & Signal Quality Index (Required)
13
14	Before adding or changing tracking, calculate the Measurement Readiness & Signal Quality Index.
15
16	### Purpose
17
18	This index answers:
19
20	> Can this analytics setup produce reliable, decision-grade insights?
21
22	It prevents:
23
24	* event sprawl
25	* vanity tracking
26	* misleading conversion data
27	* false confidence in broken analytics
28
29	---
30
31	## 🔢 Measurement Readiness & Signal Quality Index
32
33	### Total Score: 0–100
34
35	This is a diagnostic score, not a performance KPI.
36
37	---
38
39	### Scoring Categories & Weights
40
41	\| Category \| Weight \|
42	\| ----------------------------- \| ------- \|
43	\| Decision Alignment \| 25 \|
44	\| Event Model Clarity \| 20 \|
45	\| Data Accuracy & Integrity \| 20 \|
46	\| Conversion Definition Quality \| 15 \|
47	\| Attribution & Context \| 10 \|
48	\| Governance & Maintenance \| 10 \|
49	\| Total \| 100 \|
50
51	---
52
53	### Category Definitions
54
55	#### 1. Decision Alignment (0–25)
56
57	* Clear business questions defined
58	* Each tracked event maps to a decision
59	* No events tracked “just in case”
60
61	---
62
63	#### 2. Event Model Clarity (0–20)
64
65	* Events represent meaningful actions
66	* Naming conventions are consistent
67	* Properties carry context, not noise
68
69	---
70
71	#### 3. Data Accuracy & Integrity (0–20)
72
73	* Events fire reliably
74	* No duplication or inflation
75	* Values are correct and complete
76	* Cross-browser and mobile validated
77
78	---
79
80	#### 4. Conversion Definition Quality (0–15)
81
82	* Conversions represent real success
83	* Conversion counting is intentional
84	* Funnel stages are distinguishable
85
86	---
87
88	#### 5. Attribution & Context (0–10)
89
90	* UTMs are consistent and complete
91	* Traffic source context is preserved
92	* Cross-domain / cross-device handled appropriately
93
94	---
95
96	#### 6. Governance & Maintenance (0–10)
97
98	* Tracking is documented
99	* Ownership is clear
100	* Changes are versioned and monitored
101
102	---
103
104	### Readiness Bands (Required)
105
106	\| Score \| Verdict \| Interpretation \|
107	\| ------ \| --------------------- \| --------------------------------- \|
108	\| 85–100 \| Measurement-Ready \| Safe to optimize and experiment \|
109	\| 70–84 \| Usable with Gaps \| Fix issues before major decisions \|
110	\| 55–69 \| Unreliable \| Data cannot be trusted yet \|
111	\| <55 \| Broken \| Do not act on this data \|
112
113	If verdict is Broken, stop and recommend remediation first.
114
115	---
116
117	## Phase 1: Context & Decision Definition
118
119	(Proceed only after scoring)
120
121	### 1. Business Context
122
123	* What decisions will this data inform?
124	* Who uses the data (marketing, product, leadership)?
125	* What actions will be taken based on insights?
126
127	---
128
129	### 2. Current State
130
131	* Tools in use (GA4, GTM, Mixpanel, Amplitude, etc.)
132	* Existing events and conversions
133	* Known issues or distrust in data
134
135	---
136
137	### 3. Technical & Compliance Context
138
139	* Tech stack and rendering model
140	* Who implements and maintains tracking
141	* Privacy, consent, and regulatory constraints
142
143	---
144
145	## Core Principles (Non-Negotiable)
146
147	### 1. Track for Decisions, Not Curiosity
148
149	If no decision depends on it, don’t track it.
150
151	---
152
153	### 2. Start with Questions, Work Backwards
154
155	Define:
156
157	* What you need to know
158	* What action you’ll take
159	* What signal proves it
160
161	Then design events.
162
163	---
164
165	### 3. Events Represent Meaningful State Changes
166
167	Avoid:
168
169	* cosmetic clicks
170	* redundant events
171	* UI noise
172
173	Prefer:
174
175	* intent
176	* completion
177	* commitment
178
179	---
180
181	### 4. Data Quality Beats Volume
182
183	Fewer accurate events > many unreliable ones.
184
185	---
186
187	## Event Model Design
188
189	### Event Taxonomy
190
191	Navigation / Exposure
192
193	* page_view (enhanced)
194	* content_viewed
195	* pricing_viewed
196
197	Intent Signals
198
199	* cta_clicked
200	* form_started
201	* demo_requested
202
203	Completion Signals
204
205	* signup_completed
206	* purchase_completed
207	* subscription_changed
208
209	System / State Changes
210
211	* onboarding_completed
212	* feature_activated
213	* error_occurred
214
215	---
216
217	### Event Naming Conventions
218
219	Recommended pattern:
220
221	```
222	object_action[_context]
223	```
224
225	Examples:
226
227	* signup_completed
228	* pricing_viewed
229	* cta_hero_clicked
230	* onboarding_step_completed
231
232	Rules:
233
234	* lowercase
235	* underscores
236	* no spaces
237	* no ambiguity

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