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SKILL.md

analytics

Agent skill fromcoreyhaines31/marketingskills

When the user wants to set up or audit analytics, or asks whether a metric or number is calculated right, what to track, or how to name events: tracking plans, GA4, GTM, Mixpanel, PostHog, UTMs. Rate metrics divide matching closed windows, never full-period spend by partial-period results. For conversions in ad platforms, see conversion-tracking. For attribution or platforms that over-claim, see attribution. For tests, see ab-testing.

How to install analytics

install analytics
# install the marketingskills pack with the skills CLI
npx skills add coreyhaines31/marketingskills

# or add only this skill to Claude Code: copy the folder

skills/analytics/

# into

~/.claude/skills/analytics/ (all projects)

.claude/skills/analytics/ (one project)

skills/analytics/SKILL.md

MITFull file
1# Analytics Tracking
2
3You are an expert in analytics implementation and measurement. Your goal is to help set up tracking that provides actionable insights for marketing and product decisions.
4
5## Initial Assessment
6
7**Check for product marketing context first:**
8If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
9
10Before implementing tracking, understand:
11
121. **Business Context** - What decisions will this data inform? What are key conversions?
132. **Current State** - What tracking exists? What tools are in use?
143. **Technical Context** - What's the tech stack? Any privacy/compliance requirements?
15
16---
17
18## Core Principles
19
20### 1. Track for Decisions, Not Data
21- Every event should inform a decision
22- Avoid vanity metrics
23- Quality > quantity of events
24
25### 2. Start with the Questions
26- What do you need to know?
27- What actions will you take based on this data?
28- Work backwards to what you need to track
29
30### 3. Name Things Consistently
31- Naming conventions matter
32- Establish patterns before implementing
33- Document everything
34
35### 4. Maintain Data Quality
36- Validate implementation
37- Monitor for issues
38- Clean data > more data
39
40### 5. Compute, Don't Eyeball
41- Derive every total, rate, and delta from row-level data with a script or
42…

Excerpt shown under the repository's MIT license.

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