paulpas 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
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 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:
- 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.