analytics-tracking

Overview of analytics-tracking skill

The analytics-tracking skill helps you design, audit, and implement measurement that answers real business questions instead of collecting noisy event data. It is best for teams setting up GA4, GTM, UTM conventions, conversion tracking, product usage events, or a tracking plan for a marketing site, SaaS app, or ecommerce flow.

Who this analytics-tracking skill is for

Use this skill if you need to:

• decide what should be tracked before engineering starts
• fix unclear or broken analytics implementations
• create a practical event taxonomy for GA4, GTM, Mixpanel, or Segment workflows
• define UTM rules across paid, organic, email, and partnerships
• connect events to decisions like signup quality, funnel dropoff, feature adoption, or revenue attribution

It is especially useful for marketers, growth teams, PMs, founders, and agents working across product and marketing data.

What job it helps you get done

The real job-to-be-done is not “add more analytics.” It is to turn a vague goal like “measure our funnel” into a usable tracking plan with:

• key conversions
• event names
• parameters
• trigger logic
• implementation notes
• validation steps

That makes the analytics-tracking skill more valuable than a generic prompt when you need structured output that can be handed to marketing ops, product, or engineering.

What makes this skill different

This skill is opinionated in the right places:

• it starts with the decisions the data should inform
• it checks for existing product marketing context first
• it pushes consistent event naming such as object_action
• it includes implementation guidance for both GA4 and GTM
• it ships with reference files that go beyond the main SKILL.md

The included references are the main differentiator. references/event-library.md gives practical event options by business type, while references/ga4-implementation.md and references/gtm-implementation.md make the skill more install-worthy for teams that need execution detail, not just strategy.

When analytics-tracking is a strong fit

Choose analytics-tracking when the request sounds like:

• “What should we track for our SaaS funnel?”
• “How do we set up GA4 and GTM for signups and upgrades?”
• “Our events are inconsistent and reporting is unreliable.”
• “We need a UTM naming standard.”
• “How do we audit whether events are firing correctly?”

If the task is specifically experiment design and A/B test measurement, the repo itself points users to a separate ab-test-setup skill.

How to Use analytics-tracking skill

analytics-tracking install context

Install the analytics-tracking skill from the repository with:

npx skills add https://github.com/coreyhaines31/marketingskills --skill analytics-tracking

After install, open the skill folder and read these files first:

• skills/analytics-tracking/SKILL.md
• skills/analytics-tracking/references/event-library.md
• skills/analytics-tracking/references/ga4-implementation.md
• skills/analytics-tracking/references/gtm-implementation.md
• skills/analytics-tracking/evals/evals.json

The references matter more than usual here because they contain the event examples, naming patterns, implementation notes, and debugging guidance that improve output quality.

Start with existing context before asking for more

The skill explicitly tells the agent to check for:

• .agents/product-marketing-context.md
• .claude/product-marketing-context.md

That matters because analytics design is much better when tied to positioning, funnel stages, ICP, and core conversion actions already documented elsewhere. If that file exists, use it before asking the user repeated discovery questions.

What inputs analytics-tracking needs

For a good analytics-tracking usage flow, provide these inputs up front:

• business type: SaaS, ecommerce, lead gen, marketplace, media, etc.
• main conversions: signup, demo booked, purchase, activation, upgrade
• tools in use: GA4, GTM, Segment, Mixpanel, ad platforms
• site or product scope: marketing site only, app only, or both
• traffic channels: paid search, paid social, email, organic, partners
• technical constraints: SPA, server-side rendering, consent banner, dev access
• privacy requirements: GDPR, consent mode, restricted PII handling
• current problems: duplicate events, missing attribution, weak naming, no QA

Without this, the skill can still help, but the output will be more generic and less implementation-ready.

Turn a rough goal into a strong prompt

Weak prompt:

“Help me with analytics.”

Strong prompt:

“Use the analytics-tracking skill to create a tracking plan for our B2B SaaS website and app. We use GA4 and GTM. Primary conversions are demo bookings, free trial starts, and paid upgrades. We want to measure CTA clicks, form starts/submits, onboarding completion, feature adoption, and plan upgrades. Please propose event names in object_action format, required parameters, GTM trigger ideas, GA4 conversion recommendations, and a QA checklist.”

Why this works:

• defines the business model
• names the important conversions
• states the stack
• asks for output in a usable implementation format

Recommended output format for real usage

Ask the skill to return a table with columns like:

• event name
• business purpose
• trigger condition
• parameters
• destination tools
• conversion status
• notes / edge cases

This matches how teams actually implement analytics-tracking work. It also reduces handoff friction between strategy and implementation.

Repository files to read first

If you are evaluating the skill before adoption, read in this order:

1. SKILL.md for the operating principles
2. references/event-library.md for candidate events by use case
3. references/ga4-implementation.md if GA4 is in scope
4. references/gtm-implementation.md if GTM is in scope
5. evals/evals.json to see the expected shape of good outputs

The evals are useful because they reveal what the skill is supposed to do in practice: check context first, tie tracking to decisions, use consistent naming, and produce a tracking plan rather than loose suggestions.

How to use analytics-tracking for Data Analysis

The analytics-tracking skill is mainly for implementation planning, but it is also useful upstream of Data Analysis because it standardizes the data you will later query. Use it to define:

• canonical event names
• consistent parameters
• funnel stages
• conversion points
• attribution fields

That makes later analysis cleaner and reduces time spent reconciling messy event data. For Data Analysis teams, the best use is to have analytics-tracking define the measurement schema before dashboards or SQL work begins.

Practical GA4 and GTM usage advice

If your stack includes GA4 and GTM, ask the skill for both the measurement plan and implementation notes. The references support:

• GA4 recommended events and custom events
• conversions setup
• custom dimensions and metrics
• DebugView and QA workflows
• GTM data layer patterns
• trigger design
• variable strategy
• naming conventions for tags, triggers, and variables

This is more useful than asking only for “what events should we track,” because event ideas without firing logic and validation steps often die in implementation.

Example prompt for a marketing site

“Use the analytics-tracking skill to define analytics for our lead-gen site. Track page views, CTA clicks, form starts, form submits, pricing page engagement, resource downloads, and outbound demo scheduler clicks. We use GA4 and GTM. Include event names, parameter recommendations, conversion settings, and GTM custom event suggestions.”

Example prompt for a SaaS product

“Use the analytics-tracking skill to create a product analytics plan for our SaaS app. We need signup, trial start, onboarding completed, feature used, invite sent, integration connected, and plan upgraded. Suggest object_action event names, parameters, when to mark as conversions, and how to push these through GTM or a data layer.”

Common adoption blocker to resolve early

The biggest blocker is unclear scope. Teams often mix three different jobs:

• marketing attribution
• product usage analytics
• revenue/conversion tracking

Tell the skill which of those matters most right now. Otherwise the output may be broad but harder to implement in one pass.

analytics-tracking skill FAQ

Is analytics-tracking beginner-friendly?

Yes, if you can describe your funnel and tools. The skill is stronger than a beginner’s blank-page workflow because it gives structure and references. But it works best when someone can answer basic questions about conversions, stack, and implementation ownership.

What is the main boundary of this analytics-tracking skill?

It helps define and guide implementation of tracking. It does not replace actual tag deployment, code changes, or account configuration done in GA4, GTM, Segment, or your application codebase. Treat it as a planning and execution aid, not an auto-installer.

How is this different from a normal analytics prompt?

A normal prompt often returns generic event lists. The analytics-tracking skill is better because it is anchored in:

• decision-first measurement
• naming conventions
• repository references for GA4 and GTM
• practical event libraries by business type
• expected output patterns shown in evals

That usually leads to more implementable plans and fewer vanity metrics.

When should I not use analytics-tracking?

Skip analytics-tracking when:

• you only need a quick GA4 UI click-path
• you are doing experiment design rather than tracking design
• your real issue is BI modeling or dashboard SQL, not event instrumentation
• you need vendor-specific setup for a tool not covered by the references

It can still help with the measurement layer, but it is not a substitute for deeper platform-specific engineering docs.

Does it support only GA4?

No. GA4 and GTM are the strongest supported paths because the references cover them directly. But the skill also fits broader event planning that can feed Mixpanel, Segment, or ad platforms, especially if you ask for tool-agnostic event definitions first and vendor mappings second.

Is analytics-tracking useful for auditing broken setups?

Yes. It is a good fit when events are inconsistent, duplicated, poorly named, or disconnected from business questions. Ask it to audit your current event list against target decisions, conversion points, naming rules, and parameter consistency.

How to Improve analytics-tracking skill

Give business decisions, not just tracking requests

The fastest way to improve analytics-tracking results is to say what decisions the data should support, for example:

• “We need to know which channels drive qualified demos.”
• “We need to see where trial users fail onboarding.”
• “We need to compare upgrade rates by acquisition source.”

This pushes the output toward useful events and away from generic engagement noise.

Provide your current event inventory if one exists

If you already have events, paste them in. Ask the skill to:

• deduplicate names
• normalize to object_action
• identify missing parameters
• flag vanity or low-value events
• map old events to a cleaner taxonomy

This produces much better output than asking for a plan from scratch when a messy implementation already exists.

Ask for parameter logic, not only event names

A common failure mode is getting a neat event list with weak parameter design. Improve analytics-tracking usage by asking for:

• required vs optional parameters
• allowed values
• naming conventions
• examples for each event
• which parameters become GA4 custom dimensions

That reduces ambiguity during implementation and improves downstream reporting.

Request QA and debugging steps in the first pass

Do not wait until the end to think about validation. Ask analytics-tracking to include:

• how to verify events in GTM Preview
• how to inspect GA4 DebugView
• how to test duplicate firing
• how to validate UTM capture
• what “done” looks like before launch

This is one of the highest-value improvements because many tracking plans fail during QA, not planning.

Split the work by funnel layer

If the first output feels too broad, rerun analytics-tracking in narrower passes:

1. acquisition and UTM conventions
2. website conversion events
3. product onboarding events
4. monetization and upgrade events
5. QA and reporting checks

This usually gives cleaner, more usable plans than one giant all-in request.

Use the references to pressure-test output quality

When a generated plan looks plausible but vague, compare it against:

• references/event-library.md for missing events or parameters
• references/ga4-implementation.md for GA4-specific setup details
• references/gtm-implementation.md for data layer and trigger design

That is the best way to improve analytics-tracking output without guessing what “good” should look like.

Common failure modes to watch for

Watch for these issues in analytics-tracking outputs:

• too many events with no business purpose
• no distinction between key conversions and supporting events
• event names that are inconsistent or too UI-specific
• missing parameters needed for segmentation
• no mention of consent, PII, or cross-domain concerns
• implementation advice that ignores your actual stack

If you see these, tighten the prompt and ask for a reduced, decision-linked event set.

Iterate after the first draft

A strong workflow is:

1. generate a draft tracking plan
2. remove low-value events
3. add missing parameters and trigger rules
4. mark primary conversions
5. add QA steps
6. hand off to implementation

The analytics-tracking skill performs best as an iterative planning tool, not a one-shot magic answer.