Google Analytics Automation via Rube MCP

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/google-analytics-automation

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

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

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Prerequisites, Common Patterns, Known Pitfalls, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Automate Google Analytics tasks via Rube MCP (Composio): run reports, list accounts/properties, funnels, pivots, key events. Always search tools first for current schemas.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Verify Rube MCP is available by confirming RUBESEARCHTOOLS responds
2. Call RUBEMANAGECONNECTIONS 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
5. GOOGLEANALYTICSLIST_ACCOUNTS - List all accessible GA4 accounts [Required]
6. GOOGLEANALYTICSLIST_PROPERTIES - List properties under an account [Required]
7. pageSize: Number of results per page

Imported Workflow Notes

Imported: Setup

Get Rube MCP: Add

https://rube.app/mcp

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

Imported: 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

Imported: 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

Examples

Example 1: Ask for the upstream workflow directly

Use @google-analytics-automation to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @google-analytics-automation against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @google-analytics-automation for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @google-analytics-automation using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/google-analytics-automation

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @github-issue-creator

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @github-workflow-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gitlab-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gitlab-ci-patterns

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Quick Reference

Imported: 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

Imported: 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)

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.