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