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Google Analytics Automation

CommunityPopular
davepoon
google-analytics-automation

Automate Google Analytics tasks via Rube MCP (Composio): run reports, list accounts/properties, funnels, pivots, key events. Always search tools first for current schemas.

Overview

Publisherdavepoon
Repositorybuildwithclaude
Skill namegoogle-analytics-automation
Stars
3.6K
Forks
586
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by davepoon on GitHub. Read the source before you install it.

Installation

Install the Google Analytics Automation 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/davepoon/buildwithclaude/tree/main/plugins/all-skills/skills/google-analytics-automation
  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/davepoon/buildwithclaude.git /tmp/buildwithclaude
mkdir -p .claude/skills
cp -r /tmp/buildwithclaude/plugins/all-skills/skills/google-analytics-automation .claude/skills/google-analytics-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Google Analytics Automation 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 Automation 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 Automation 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 Automation via Rube MCP

Automate Google Analytics 4 (GA4) reporting and property management through Composio's Google Analytics toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/google_analytics

Prerequisites

  • Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
  • Active Google Analytics connection via RUBE_MANAGE_CONNECTIONS with toolkit google_analytics
  • Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration. No API keys needed — just add the endpoint and it works.

  1. Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
  2. Call RUBE_MANAGE_CONNECTIONS with toolkit google_analytics
  3. If connection is not ACTIVE, follow the returned auth link to complete Google OAuth
  4. Confirm connection status shows ACTIVE before running any workflows

Core Workflows

1. List Accounts and Properties

When to use: User wants to discover available GA4 accounts and properties

Tool sequence:

  1. GOOGLE_ANALYTICS_LIST_ACCOUNTS - List all accessible GA4 accounts [Required]
  2. GOOGLE_ANALYTICS_LIST_PROPERTIES - List properties under an account [Required]

Key parameters:

  • pageSize: Number of results per page
  • pageToken: Pagination token from previous response
  • filter: Filter expression for properties (e.g., parent:accounts/12345)

Pitfalls:

  • Property IDs are numeric strings prefixed with 'properties/' (e.g., 'properties/123456')
  • Account IDs are prefixed with 'accounts/' (e.g., 'accounts/12345')
  • Always list accounts first, then properties under each account
  • Pagination required for organizations with many properties

2. Run Standard Reports

When to use: User wants to query metrics and dimensions from GA4 data

Tool sequence:

  1. GOOGLE_ANALYTICS_LIST_PROPERTIES - Get property ID [Prerequisite]
  2. GOOGLE_ANALYTICS_GET_METADATA - Discover available dimensions and metrics [Optional]
  3. GOOGLE_ANALYTICS_CHECK_COMPATIBILITY - Verify dimension/metric compatibility [Optional]
  4. GOOGLE_ANALYTICS_RUN_REPORT - Execute the report query [Required]

Key parameters:

  • property: Property ID (e.g., 'properties/123456')
  • dateRanges: Array of date range objects with startDate and endDate
  • dimensions: Array of dimension objects with name field
  • metrics: Array of metric objects with name field
  • dimensionFilter / metricFilter: Filter expressions
  • orderBys: Sort order configuration
  • limit: Maximum rows to return
  • offset: Row offset for pagination

Pitfalls:

  • Date format is 'YYYY-MM-DD' or relative values like 'today', 'yesterday', '7daysAgo', '30daysAgo'
  • Not all dimensions and metrics are compatible; use CHECK_COMPATIBILITY first
  • Use GET_METADATA to discover valid dimension and metric names
  • Maximum 9 dimensions per report request
  • Row limit defaults vary; set explicitly for large datasets
  • offset is for result pagination, not date pagination

3. Run Batch Reports

When to use: User needs multiple different reports from the same property in one call

Tool sequence:

  1. GOOGLE_ANALYTICS_LIST_PROPERTIES - Get property ID [Prerequisite]
  2. GOOGLE_ANALYTICS_BATCH_RUN_REPORTS - Execute multiple reports at once [Required]

Key parameters:

  • property: Property ID (required)
  • requests: Array of individual report request objects (same structure as RUN_REPORT)

Pitfalls:

  • Maximum 5 report requests per batch call
  • All reports in a batch must target the same property
  • Each individual report has the same dimension/metric limits as RUN_REPORT
  • Batch errors may affect all reports; check individual report responses

4. Run Pivot Reports

When to use: User wants cross-tabulated data (rows vs columns) like pivot tables

Tool sequence:

  1. GOOGLE_ANALYTICS_LIST_PROPERTIES - Get property ID [Prerequisite]
  2. GOOGLE_ANALYTICS_RUN_PIVOT_REPORT - Execute pivot report [Required]

Key parameters:

  • property: Property ID (required)
  • dateRanges: Date range objects
  • dimensions: All dimensions used in any pivot
  • metrics: Metrics to aggregate
  • pivots: Array of pivot definitions with fieldNames, limit, and orderBys

Pitfalls:

  • Dimensions used in pivots must also be listed in top-level dimensions
  • Pivot fieldNames reference dimension names from the top-level list
  • Complex pivots with many dimensions can produce very large result sets
  • Each pivot has its own independent limit and orderBys

5. Run Funnel Reports

When to use: User wants to analyze conversion funnels and drop-off rates

Tool sequence:

  1. GOOGLE_ANALYTICS_LIST_PROPERTIES - Get property ID [Prerequisite]
  2. GOOGLE_ANALYTICS_RUN_FUNNEL_REPORT - Execute funnel analysis [Required]

Key parameters:

  • property: Property ID (required)
  • dateRanges: Date range objects
  • funnel: Funnel definition with steps array
  • funnelBreakdown: Optional dimension to break down funnel by

Pitfalls:

  • Funnel steps are ordered; each step defines a condition users must meet
  • Steps use filter expressions similar to dimension/metric filters
  • Open funnels allow entry at any step; closed funnels require sequential progression
  • Funnel reports may take longer to process than standard reports

6. Manage Key Events

When to use: User wants to view or manage conversion events (key events) in GA4

Tool sequence:

  1. GOOGLE_ANALYTICS_LIST_PROPERTIES - Get property ID [Prerequisite]
  2. GOOGLE_ANALYTICS_LIST_KEY_EVENTS - List all key events for the property [Required]

Key parameters:

  • parent: Property resource name (e.g., 'properties/123456')
  • pageSize: Number of results per page
  • pageToken: Pagination token

Pitfalls:

  • Key events were previously called "conversions" in GA4
  • Property must have key events configured to return results
  • Key event names correspond to GA4 event names

Common Patterns

ID Resolution

Account name -> Account ID:

1. Call GOOGLE_ANALYTICS_LIST_ACCOUNTS
2. Find account by displayName
3. Extract name field (e.g., 'accounts/12345')

Property name -> Property ID:

1. Call GOOGLE_ANALYTICS_LIST_PROPERTIES with filter
2. Find property by displayName
3. Extract name field (e.g., 'properties/123456')

Dimension/Metric Discovery

1. Call GOOGLE_ANALYTICS_GET_METADATA with property ID
2. Browse available dimensions and metrics
3. Call GOOGLE_ANALYTICS_CHECK_COMPATIBILITY to verify combinations
4. Use verified dimensions/metrics in RUN_REPORT

Pagination

  • Reports: Use offset and limit for row pagination
  • Accounts/Properties: Use pageToken from response
  • Continue until pageToken is absent or rowCount reached

Common Dimensions and Metrics

Dimensions: date, city, country, deviceCategory, sessionSource, sessionMedium, pagePath, pageTitle, eventName

Metrics: activeUsers, sessions, screenPageViews, eventCount, conversions, totalRevenue, bounceRate, averageSessionDuration

Known Pitfalls

Property IDs:

  • Always use full resource name format: 'properties/123456'
  • Numeric ID alone will cause errors
  • Resolve property names to IDs via LIST_PROPERTIES

Date Ranges:

  • Format: 'YYYY-MM-DD' or relative ('today', 'yesterday', '7daysAgo', '30daysAgo')
  • Data processing delay means today's data may be incomplete
  • Maximum date range varies by property configuration

Compatibility:

  • Not all dimensions work with all metrics
  • Always verify with CHECK_COMPATIBILITY before complex reports
  • Custom dimensions/metrics have specific naming patterns

Response Parsing:

  • Report data is nested in rows array with dimensionValues and metricValues
  • Values are returned as strings; parse numbers explicitly
  • Empty reports return no rows key (not an empty array)

Quick Reference

TaskTool SlugKey Params
List accountsGOOGLE_ANALYTICS_LIST_ACCOUNTSpageSize, pageToken
List propertiesGOOGLE_ANALYTICS_LIST_PROPERTIESfilter, pageSize
Get metadataGOOGLE_ANALYTICS_GET_METADATAproperty
Check compatibilityGOOGLE_ANALYTICS_CHECK_COMPATIBILITYproperty, dimensions, metrics
Run reportGOOGLE_ANALYTICS_RUN_REPORTproperty, dateRanges, dimensions, metrics
Batch reportsGOOGLE_ANALYTICS_BATCH_RUN_REPORTSproperty, requests
Pivot reportGOOGLE_ANALYTICS_RUN_PIVOT_REPORTproperty, dateRanges, pivots
Funnel reportGOOGLE_ANALYTICS_RUN_FUNNEL_REPORTproperty, dateRanges, funnel
List key eventsGOOGLE_ANALYTICS_LIST_KEY_EVENTSparent, pageSize

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Frequently asked questions

What does the Google Analytics Automation AI skill do?

Automate Google Analytics tasks via Rube MCP (Composio): run reports, list accounts/properties, funnels, pivots, key events. Always search tools first for current schemas.

Why use Google Analytics Automation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davepoon/buildwithclaude/tree/main/plugins/all-skills/skills/google-analytics-automation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Google Analytics Automation?

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 Automation?

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

Is the Google Analytics Automation AI skill free?

Yes. It is published on GitHub by davepoon 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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