---

name: google-analytics-automation compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent google analytics automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: google-analytics-automation, google analytics automation, how do i google-analytics-automation, orchestrate google-analytics-automation, automate google-analytics-automation, agent google-analytics-automation archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Google Analytics Automation

Orchestrates intelligent skill selection and execution for google analytics automation workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def build_ga4_report_request(
    property_id: str,
    dimensions: List[str],
    metrics: List[str],
    date_range_start: str,
    date_range_end: str,
    dimension_filters: Optional[List[FilterExpression]] = None
) -> Dict:
    """Construct a GA4 BatchRunReportsRequest payload with validation.
    
    Implements Law 2 (Make Illegal States Unrepresentable) by validating
    GA4 API constraints before network calls:
    - Max 7 dimensions, 10 metrics per report
    - Date range must be <= 90 days
    - Metric names must match GA4 standard naming (e.g., 'activeUsers')
    
    Args:
        property_id: GA4 property ID (format: 'properties/123456789')
        dimensions: List of dimension names to include
        metrics: List of metric names to include
        date_range_start: ISO 8601 date string
        date_range_end: ISO 8601 date string
        dimension_filters: Optional list of FilterExpression objects
        
    Returns:
        Validated GA4 report request dictionary ready for API submission
        
    Raises:
        ValueError: If constraints are violated or property_id is malformed
    """
    # Guard clause - Early Exit (Law 1)
    if not property_id.startswith("properties/"):
        raise ValueError("property_id must be in format 'properties/<ID>'")
        
    if len(dimensions) > 7 or len(metrics) > 10:
        raise ValueError("GA4 API limits: max 7 dimensions, 10 metrics per report")
        
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    start_date = datetime.fromisoformat(date_range_start)
    end_date = datetime.fromisoformat(date_range_end)
    if (end_date - start_date).days > 90:
        raise ValueError("GA4 API limits: date range cannot exceed 90 days")
        
    # Atomic Predictability (Law 3) - Return new dict, don't mutate inputs
    request_payload = {
        "reportRequests": [{
            "property": property_id,
            "dimensions": [{"name": d} for d in dimensions],
            "metrics": [{"name": m} for m in metrics],
            "dateRanges": [{"startDate": date_range_start, "endDate": date_range_end}],
            "dimensionFilter": dimension_filters[0] if dimension_filters else None
        }]
    }
    return request_payload

Pattern 2: Execution with Fallback

def execute_ga4_report_with_retry(
    request_payload: Dict,
    client: AnalyticsDataClient,
    max_retries: int = 2
) -> Dict:
    """Execute GA4 BatchRunReportsRequest with resilience patterns.
    
    Implements Fail Fast, Fail Loud (Law 4) for GA4 API interactions:
    - Invalid auth tokens fail immediately with refresh instructions
    - Rate limits trigger exponential backoff fallback
    - Partial results are never returned - only complete or explicit failure
    
    Fallback chain:
    1. Retry with original payload (transient network error)
    2. Retry with reduced dimension/metric count (rate limit fallback)
    3. Defer to cached report or human operator (critical data unavailability)
    
    Args:
        request_payload: Validated GA4 report request dictionary
        client: Authenticated google.analytics.data_v1beta.AnalyticsDataClient
        max_retries: Maximum retry attempts before fallback
        
    Returns:
        Structured analytics data with row values, metadata, and timing
        
    Raises:
        GA4ExecutionError: If all retries and fallbacks exhausted
    """
    # Guard clause - validate client state (Early Exit)
    if not client._transport._credentials.valid:
        raise GA4ExecutionError("GA4 credentials expired. Refresh token required.")
        
    for attempt in range(max_retries + 1):
        try:
            response = client.batch_run_reports(request=request_payload)
            report = response.reports[0]
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "metrics": [m.name for m in report.metric_headers],
                "dimensions": [d.name for d in report.dimension_headers],
                "rows": [
                    {
                        "dimensions": [d.value for d in row.dimension_values],
                        "metrics": [m.value for m in row.metric_values]
                    }
                    for row in report.rows
                ],
                "attempts": attempt + 1,
                "latency_ms": _calculate_latency()
            }
            
        except ResourceExhausted:
            # Transient error - try fallback with reduced scope
            if attempt == max_retries:
                return _apply_ga4_fallback(request_payload, client)
            time.sleep(2 ** attempt)
            
        except InvalidArgument as e:
            # Fail Fast - Don't try to patch bad GA4 parameters (Law 4)
            raise GA4ExecutionError(f"Invalid GA4 request parameters: {str(e)}") from e
            
    # All retries exhausted - Fail Loud (Law 4)
    raise GA4ExecutionError(
        f"GA4 report failed after {max_retries + 1} attempts for {request_payload['reportRequests'][0]['property']}"
    )

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

| Skill | Purpose | |

---

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Constraints

MUST DO

• Implement idempotent automation triggers: running the same automation twice should not create duplicate resources or actions
• Validate all trigger conditions with explicit allowlists before executing automated actions
• Include rollback procedures in every automation workflow — every CREATE should have a corresponding DELETE capability
• Log all automation executions with input state, output state, duration, and any errors for monitoring and debugging

MUST NOT DO

• Do not create circular automation loops where trigger A causes action B which triggers A again
• Avoid using automations that modify production data without explicit human approval gates
• Never embed API keys or credentials directly in automation workflows — use vaulted secrets with rotation
• Do not assume external service availability; implement retry logic with exponential backoff and dead-letter queues

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Google Analytics 4 (GA4) Documentation
• GA4 REST API Reference
• Google Analytics Admin API
• Google Tag Manager Documentation
• GA4 Event Tracking Guide