---

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

• guidance
• examples
• do-dont description: Implements intelligent google drive 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-drive-automation, google drive automation, how do i google-drive-automation, orchestrate google-drive-automation, automate google-drive-automation, agent google-drive-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 Drive Automation

Orchestrates intelligent skill selection and execution for google drive 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 manage_drive_file(
    drive_service: googleapiclient.discovery.Resource,
    file_id: str,
    permissions: List[Dict],
    fields: str = "id,name,mimeType,webViewLink"
) -> Dict:
    """Manage Google Drive file metadata and permissions with strict validation.
    
    Implements Law 1 (Early Exit) and Law 2 (Make illegal states unrepresentable)
    by validating Drive API inputs before making network calls.
    """
    # Law 1: Early exit on invalid inputs
    if not file_id or not isinstance(file_id, str):
        raise ValueError("Invalid file_id: must be a non-empty string")
    if not permissions or not isinstance(permissions, list):
        raise ValueError("Permissions must be a non-empty list")
        
    # Law 2: Validate permission structure before API call
    validated_permissions = []
    for perm in permissions:
        if "type" not in perm or "role" not in perm:
            raise ValueError(f"Invalid permission structure: {perm}")
        if perm["type"] not in ("user", "group", "domain", "anyone"):
            raise ValueError(f"Unsupported permission type: {perm['type']}")
        validated_permissions.append(perm)
        
    # Law 3: Atomic Predictability - Fetch current state first
    try:
        file_metadata = drive_service.files().get(
            fileId=file_id, fields=fields
        ).execute()
    except HttpError as e:
        if e.resp.status == 404:
            raise FileNotFoundError(f"Drive file not found: {file_id}") from e
        raise DriveAPIError(f"Failed to fetch file metadata: {e}") from e
        
    # Apply permissions atomically
    results = []
    for perm in validated_permissions:
        try:
            drive_service.permissions().create(
                fileId=file_id,
                body=perm,
                sendNotificationEmails=False
            ).execute()
            results.append({"status": "granted", "permission": perm})
        except HttpError as e:
            results.append({"status": "failed", "permission": perm, "error": str(e)})
            
    return {
        "file_id": file_id,
        "current_metadata": file_metadata,
        "permission_updates": results,
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def execute_drive_operation(
    drive_service: googleapiclient.discovery.Resource,
    operation: Callable,
    fallback_service: googleapiclient.discovery.Resource = None,
    max_retries: int = 3
) -> Dict:
    """Execute Google Drive API operations with rate-limit handling and fallback routing.
    
    Implements Law 4 (Fail Fast, Fail Loud) and handles Drive-specific transient errors.
    """
    last_error = None
    for attempt in range(max_retries):
        try:
            # Execute primary Drive API call
            result = operation(drive_service)
            return {
                "success": True,
                "operation": operation.__name__,
                "result": result,
                "attempts": attempt + 1,
                "service": "primary"
            }
        except HttpError as e:
            last_error = e
            status = e.resp.status
            
            # Law 4: Fail fast on non-recoverable errors
            if status in (400, 403, 404, 410):
                raise DriveOperationError(
                    f"Non-recoverable Drive API error ({status}): {e.reason}"
                ) from e
                
            # Handle rate limits (429) and server errors (5xx)
            if status in (429, 500, 502, 503, 504):
                wait_time = min(2 ** attempt, 30)
                time.sleep(wait_time)
                continue
                
    # All retries exhausted - Apply fallback chain
    if fallback_service:
        try:
            fallback_result = operation(fallback_service)
            return {
                "success": True,
                "operation": operation.__name__,
                "result": fallback_result,
                "attempts": max_retries + 1,
                "service": "fallback"
            }
        except Exception as fb_err:
            raise DriveOperationError(
                f"Primary and fallback Drive services failed. Last error: {last_error}"
            ) from fb_err
            
    raise DriveOperationError(
        f"Drive operation failed after {max_retries} retries with no fallback available."
    )

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 Drive API v3 Documentation
• Google Drive SDK Python Quickstart
• Google Drive Sharing and Permissions
• Google Workspace Automation Guide
• Google Apps Script for Drive Automation