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Analytics

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open-fox
analytics

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For getting conversions into ad platforms (pixels, conversion tags, CAPI, server-side) and verifying they arrived, see conversion-tracking. For choosing attribution models, comparing multi-touch/MMM/incrementality, or reconciling conflicting numbers across tools, see attribution. For A/B test measurement, see ab-testing. Rate metrics divide matching closed windows — never full-period spend by partial-period results.

Overview

Publisheropen-fox
Repositoryagents
Skill nameanalytics
Stars
55
Forks
19
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by open-fox on GitHub. Read the source before you install it.

Installation

Install the Analytics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/open-fox/agents/tree/main/.agents/skills/analytics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/open-fox/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/.agents/skills/analytics .claude/skills/analytics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analytics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analytics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analytics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analytics Tracking

You 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.

Initial Assessment

Check for product marketing context first: If .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.

Before implementing tracking, understand:

  1. Business Context - What decisions will this data inform? What are key conversions?
  2. Current State - What tracking exists? What tools are in use?
  3. Technical Context - What's the tech stack? Any privacy/compliance requirements?

Core Principles

1. Track for Decisions, Not Data

  • Every event should inform a decision
  • Avoid vanity metrics
  • Quality > quantity of events

2. Start with the Questions

  • What do you need to know?
  • What actions will you take based on this data?
  • Work backwards to what you need to track

3. Name Things Consistently

  • Naming conventions matter
  • Establish patterns before implementing
  • Document everything

4. Maintain Data Quality

  • Validate implementation
  • Monitor for issues
  • Clean data > more data

5. Compute, Don't Eyeball

  • Derive every total, rate, and delta from row-level data with a script or spreadsheet — never by scanning rows and summing mentally. One silently dropped row corrupts every figure downstream of it.
  • Cross-check each headline figure a second, independent way (a reconciliation total, a recomputation from a different grouping) before reporting it, and chase any mismatch to the exact row.
  • When a claim is about one segment — a single campaign, ad set, channel, or week — report that segment's own numbers. Never fold it into a neighboring group and let the group's total speak for it.

6. Match Observation Windows

  • A rate or efficiency metric divides a numerator and denominator that cover the same closed observation window. Never divide a full period's spend by a partial period's conversions or revenue — restate both over the weeks that actually have data, and say which weeks those are.
  • Cohort rates use only cohorts whose observation window has ended. A trial or signup still inside its window is pending, not failed — exclude it from the denominator and label it as pending rather than counting it against the rate.

7. Recommend Against the Stated Goal

  • Analysis ends in a recommendation, and the recommendation must be argued from the goal the brief states — the launch target, audience, revenue, or positioning objective — not only from what is operationally easiest. Name which stated goal each option serves and what it trades away.

Tracking Plan Framework

Structure

Event Name | Category | Properties | Trigger | Notes
---------- | -------- | ---------- | ------- | -----

Event Types

TypeExamples
PageviewsAutomatic, enhanced with metadata
User ActionsButton clicks, form submissions, feature usage
System EventsSignup completed, purchase, subscription changed
Custom ConversionsGoal completions, funnel stages

For comprehensive event lists: See references/event-library.md


Event Naming Conventions

Recommended Format: Object-Action

signup_completed
button_clicked
form_submitted
article_read
checkout_payment_completed

Best Practices

  • Lowercase with underscores
  • Be specific: cta_hero_clicked vs. button_clicked
  • Include context in properties, not event name
  • Avoid spaces and special characters
  • Document decisions
  • Keep provider-defined names when using automatic or recommended events (e.g. GA4 click, view_search_results, purchase). Map internal business names explicitly; naming a custom event does not make the provider collect it automatically.

Essential Events

Marketing Site

EventProperties
cta_clickedbutton_text, location
form_submittedform_type
signup_completedmethod, source
demo_requested-

Product/App

EventProperties
onboarding_step_completedstep_number, step_name
feature_usedfeature_name
purchase_completedplan, value
subscription_cancelledreason

For full event library by business type: See references/event-library.md


Event Properties

Standard Properties

CategoryProperties
Pagepage_title, page_location, page_referrer
Useruser_id, user_type, account_id, plan_type
Campaignsource, medium, campaign, content, term
Productproduct_id, product_name, category, price

Best Practices

  • Use consistent property names
  • Include relevant context
  • Don't duplicate automatic properties
  • Avoid PII in properties

GA4 Implementation

Quick Setup

  1. Create GA4 property and data stream
  2. Install gtag.js or GTM
  3. Enable enhanced measurement
  4. Configure custom events
  5. Mark conversions in Admin

Event Example

javascript
// GA4's recommended sign_up event, not a custom name, so it feeds built-in reports.
// Your tracking plan can still call this signup_completed; map the name explicitly.
gtag('event', 'sign_up', {
  'method': 'email',
  'plan': 'free'
});

For detailed GA4 implementation: See references/ga4-implementation.md


Google Tag Manager

Container Structure

ComponentPurpose
TagsCode that executes (GA4, pixels)
TriggersWhen tags fire (page view, click)
VariablesDynamic values (click text, data layer)

Data Layer Pattern

javascript
dataLayer.push({
  'event': 'form_submitted',
  'form_name': 'contact',
  'form_location': 'footer'
});

For detailed GTM implementation: See references/gtm-implementation.md


UTM Parameter Strategy

Standard Parameters

ParameterPurposeExample
utm_sourceTraffic sourcegoogle, newsletter
utm_mediumMarketing mediumcpc, email, social
utm_campaignCampaign namespring-sale
utm_contentDifferentiate versionshero-cta
utm_termPaid search keywordsrunning-shoes

Naming Conventions

  • Lowercase everything. GA4 treats LinkedIn and linkedin as two sources, and the split never merges back.
  • Hyphens between words. No spaces, underscores, or +. Pick one separator for the whole company; this skill uses hyphens.
  • One spelling per source. linkedin, never also li or linkedin.com. Keep the allowed values in a shared sheet and build links from it.
  • No personal data. Never put emails, names, phone numbers, or user IDs in a UTM. They land in analytics reports and in referrer logs, and Google's terms prohibit sending PII to GA4.
  • Specific but short: blog-footer-cta, not cta1.
  • Test the final URL. Shorteners and redirects can drop or double parameters. Click the live link and check the landing URL before the campaign ships.

UTM Patterns by Partner Type

Partner links break most often because each deal gets named on the spot. Fix the pattern per partner type once: utm_medium names the kind of deal, utm_source names who sent the traffic.

Partner typeutm_sourceutm_mediumutm_campaignutm_content
Newsletter{partner} (one per partner, across all their publications)newsletter{partner}-{publication}-{yyyy}-{mm}{placement}-{yyyy-mm-dd}
Creator (YouTube, LinkedIn, TikTok){platform}creator{creator}{post-date} or the video ID
Podcast{podcast}podcast{podcast}-{yyyy}-{mm}{spot}-{yyyy-mm-dd} (pre-roll, mid-roll)
Affiliate{partner}affiliate{partner}-affiliateplacement, if they have several
Community{community}community{community}-{program}the channel or post, if known
  • Use creator as the medium for paid creator deals, not social (which mixes them with your organic posts) or video (which mixes them with paid video ads). The platform goes in utm_source.
  • One link per placement. A newsletter that runs you on two dates gets two links, so each date's clicks stay separate. The monthly utm_campaign rolls them up.
  • Add a custom channel group in GA4. GA4's default grouping sends mediums like newsletter, creator, podcast, and community to Unassigned (only affiliate and email map to a default channel). Create a channel group with a rule per medium so partner traffic shows up as its own channels.
  • Podcast and video links often aren't clickable, so pair the UTM link with a promo code or vanity URL. See influencer-marketing for the rest of partner measurement.

The partner-type pattern is adapted from Wispr Flow's link-creator skill (MIT), published in the skills.marketing library.

Traffic From AI Assistants

You can't tag links that ChatGPT, Claude, Perplexity, Gemini, or Copilot put in their answers. Some of those visits arrive with a referrer (chatgpt.com, perplexity.ai) or an assistant-added utm_source; many arrive as direct, or come later through a branded search.

  • Add a GA4 channel group rule that matches known assistant referrers and sources into an "AI assistants" channel, and update the list as new assistants appear.
  • Treat that channel as the floor. Assistant-driven visits that show up as direct or branded search can be most of the effect, and they won't carry the referrer.
  • For measuring the full effect (exposure, delayed visits, and longer attribution windows), see ai-seo. For reconciling it with other sources, see attribution.

Debugging and Validation

Testing Tools

ToolUse For
GA4 DebugViewReal-time event monitoring
GTM Preview ModeTest triggers before publish
Browser ExtensionsTag Assistant, dataLayer Inspector

Validation Checklist

  • Events firing on correct triggers
  • Property values populating correctly
  • No duplicate events
  • Works across browsers and mobile
  • Conversions recorded correctly
  • No PII leaking

Common Issues

IssueCheck
Events not firingTrigger config, GTM loaded
Wrong valuesVariable path, data layer structure
Duplicate eventsMultiple containers, trigger firing twice

Privacy and Compliance

Considerations

  • Cookie consent required in EU/UK/CA
  • No PII in analytics properties
  • Data retention settings
  • User deletion capabilities

Implementation

  • Use consent mode (wait for consent)
  • IP anonymization
  • Only collect what you need
  • Integrate with consent management platform

Output Format

Tracking Plan Document

markdown
# [Site/Product] Tracking Plan

## Overview
- Tools: GA4, GTM
- Last updated: [Date]

## Events

| Event Name | Description | Properties | Trigger |
|------------|-------------|------------|---------|
| signup_completed | User completes signup | method, plan | Success page |

## Custom Dimensions

| Name | Scope | Parameter |
|------|-------|-----------|
| user_type | User | user_type |

## Conversions

| Conversion | Event | Counting |
|------------|-------|----------|
| Signup | signup_completed | Once per session |

Task-Specific Questions

  1. What tools are you using (GA4, Mixpanel, etc.)?
  2. What key actions do you want to track?
  3. What decisions will this data inform?
  4. Who implements - dev team or marketing?
  5. Are there privacy/consent requirements?
  6. What's already tracked?

Tool Integrations

For implementation, see the tools registry. Key analytics tools:

ToolBest ForMCPGuide
GA4Web analytics, Google ecosystem✓ga4.md
MixpanelProduct analytics, event tracking-mixpanel.md
AmplitudeProduct analytics, cohort analysis-amplitude.md
PostHogOpen-source analytics, session replay-posthog.md
SegmentCustomer data platform, routing-segment.md

Related Skills

  • ab-testing: For experiment tracking
  • conversion-tracking: For getting conversions (including GA4 key events from forms and bookings) to ad platforms and GA4, and verifying they arrive
  • attribution: For attribution models, multi-touch/MMM/incrementality, and reconciling conflicting numbers across tools (once tracking is live)
  • seo-audit: For organic traffic analysis
  • cro: For conversion optimization (uses this data)
  • revops: For pipeline metrics, CRM tracking, and revenue attribution

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analytics AI skill do?

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For getting conversions into ad platforms (pixels, conversion tags, CAPI, server-side) and verifying they arrived, see conv...

Why use Analytics on TypingMind?

Because you install it once and use it with any model. Analytics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analytics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/open-fox/agents/tree/main/.agents/skills/analytics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analytics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analytics?

As many as you like. As long as a model supports skills, you can use Analytics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analytics AI skill free?

It is published on GitHub by open-fox. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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