paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.