# Google Analytics Automation > Implements intelligent google analytics automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/google-analytics-automation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/google-analytics-automation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/google-analytics-automation/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/google-analytics-automation --- # 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 ```python 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/'") 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 ```python 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 | | --- --- ## 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]()