Google Analytics

OrganizationPopular

nowork-studio

google-analytics

Analyze connected Google Analytics 4 traffic, acquisition, engagement, landing pages, ecommerce, events, and conversions, and safely manage supported GA4 key events or custom dimensions through NotFair MCP. Use for GA4, Google Analytics, traffic or conversion drops, channel attribution, realtime activity, report requests, measurement configuration, or Analytics MCP setup.

Overview

Publisher	nowork-studio
Repository	`notfair-plugin`
Skill name	`google-analytics`
Stars	3.8K
Forks	488
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

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.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by nowork-studio on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/nowork-studio/notfair-plugin.git /tmp/notfair-plugin
mkdir -p .claude/skills
cp -r /tmp/notfair-plugin/analytics/google-analytics .claude/skills/google-analytics

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Google 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 Google 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 Google 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.

Google Analytics

Read ../shared/operating-contract.md. Use the live connector's instructions and schemas to choose tools for the task.

Select the property and question

Resolve ~~google-analytics to the actual GA4 connector and inspect its current tools. Confirm access and the available properties from live data.
Select the exact properties/123456789 resource returned by the connector. Never substitute a G- measurement ID or Google account ID.
Define the business question, primary metric, conversion or key-event definition, property timezone, date range, and comparison window. If the platform is missing or unauthorized, direct the user to reconnect the universal NotFair plugin and stop before claiming live data.

Pull decision-grade evidence

Pull the smallest useful set of reports across channel, source/medium, campaign, landing page, device, geography, or event. Choose available reporting capabilities and batch related reads when supported and useful.

Compare complete equivalent periods and show absolute values plus deltas.
Use metadata before guessing an unfamiliar dimension/metric pair. Respect the current API's dimension, metric, fan-out, and quota limits exposed by the tool.
Check response metadata for sampling, thresholding, or quota warnings. A collapsed (other) row means detailed rows may not sum to the total.
Treat recent/intraday data as provisional and state the property's timezone.
Keep GA4 attribution separate from ad-platform attribution. Explain discrepancies instead of blending incompatible numbers.
Do not claim causality from a correlated channel or page change without supporting evidence.

Lead with what changed, where it changed, the evidence-backed likely driver, confidence, and the next measurement or business action. Include the report definition so another operator can reproduce it.

Change measurement configuration

Use currently available capabilities for supported key-event and custom-dimension changes. Show the exact property, current state, proposed state, downstream reporting impact, and rollback before asking for approval.

Creating and deleting a key event are reversible counterparts when the same event definition is available.
Archiving a custom dimension is irreversible in GA4 and its parameter name cannot be reused. Require explicit approval that names the property and dimension before archiving.
Respect the read/write boundary described by the live capability.
Confirm success from returned before/after evidence or a fresh configuration read; report partial failures without retrying blindly.

Do not describe a report as saved or a dashboard as published unless the connected tool explicitly supports that operation and confirms it.

Bundled files

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

agents/openai.yaml1 KB
evals/evals.json2 KB

Frequently asked questions

What does the Google Analytics AI skill do?

Why use Google Analytics on TypingMind?

Because you install it once and use it with any model. Google 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 Google Analytics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nowork-studio/notfair-plugin/tree/main/analytics/google-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 Google 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 Google Analytics?

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

Is the Google Analytics AI skill free?

Yes. It is published on GitHub by nowork-studio under the MIT license. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Google Analytics

Select the property and question

Pull decision-grade evidence

Change measurement configuration

Bundled files

Frequently asked questions

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