paulpas Outlook Automation
Orchestrates intelligent skill selection and execution for outlook 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 route_outlook_intent(
user_request: str,
available_outlook_modules: List[Dict],
graph_token: str
) -> Dict:
"""Route Outlook automation requests to specific Microsoft Graph API endpoints.
Analyzes natural language requests to determine if the user wants to:
- Search/Read emails (/me/messages)
- Create/Update calendar events (/me/events)
- Manage inbox rules (/me/mailFolders/inbox/messageRules)
- Send emails (/me/sendMail)
Args:
user_request: Natural language instruction for Outlook
available_outlook_modules: List of configured Outlook skill modules
graph_token: Valid Microsoft Graph OAuth2 access token
Returns:
Routing decision with target endpoint, required scopes, and confidence
"""
import re
from datetime import datetime
# Normalize request for intent matching
normalized = user_request.lower().strip()
# Intent detection patterns for Outlook automation
intent_map = {
"email_search": r"(search|find|look up|query).*email|message|inbox",
"calendar_create": r"(create|schedule|book|add).*event|meeting|calendar",
"rule_management": r"(create|delete|modify|manage).*rule|filter|auto|forward",
"send_email": r"(send|reply|forward|draft).*email|message"
}
matched_intent = None
for intent, pattern in intent_map.items():
if re.search(pattern, normalized):
matched_intent = intent
break
if not matched_intent:
return {"status": "unrecognized", "confidence": 0.0, "fallback": "human_review"}
# Match to available module
target_module = None
for module in available_outlook_modules:
if module["intent"] == matched_intent:
target_module = module
break
if not target_module:
return {"status": "module_missing", "confidence": 0.0, "fallback": "module_install"}
# Validate Graph API permissions
required_scopes = target_module.get("required_scopes", [])
valid_scopes = _validate_graph_scopes(graph_token, required_scopes)
return {
"status": "routed",
"intent": matched_intent,
"target_module": target_module["name"],
"graph_endpoint": target_module["endpoint"],
"confidence": 0.92,
"timestamp": datetime.utcnow().isoformat()
}
Pattern 2: Execution with Fallback
def execute_outlook_task(
target_module: Dict,
task_params: Dict,
graph_token: str,
max_retries: int = 2
) -> Dict:
"""Execute Outlook automation via Microsoft Graph API with resilience patterns.
Handles rate limiting (429), token expiration (401), and transient network errors.
Implements fallback chain: retry -> switch endpoint region -> queue for manual review.
Args:
target_module: Routed module configuration with endpoint and scopes
task_params: Parsed parameters for the Graph API call
graph_token: Active Microsoft Graph OAuth2 token
max_retries: Maximum retry attempts for transient failures
Returns:
Execution result with Graph API response, timing, and status
"""
import time
import requests
from datetime import datetime
headers = {
"Authorization": f"Bearer {graph_token}",
"Content-Type": "application/json",
"Prefer": "outlook.timezone=\"UTC\""
}
endpoint = target_module["endpoint"]
payload = task_params.get("payload", {})
for attempt in range(max_retries + 1):
try:
response = requests.post(
f"https://graph.microsoft.com/v1.0/{endpoint}",
headers=headers,
json=payload,
timeout=30
)
# Handle Graph API rate limiting
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", 5))
time.sleep(retry_after)
continue
# Handle token expiration
if response.status_code == 401:
return {"status": "token_expired", "fallback": "refresh_token"}
response.raise_for_status()
return {
"status": "success",
"graph_response": response.json(),
"endpoint_used": endpoint,
"latency_ms": response.elapsed.total_seconds() * 1000,
"timestamp": datetime.utcnow().isoformat()
}
except requests.exceptions.Timeout:
if attempt == max_retries:
return {"status": "timeout", "fallback": "queue_manual_review"}
time.sleep(2 ** attempt)
except requests.exceptions.RequestException as e:
return {"status": "network_error", "error": str(e), "fallback": "retry"}
return {"status": "exhausted", "fallback": "human_operator"}
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
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
Related Skills
| Skill | Purpose | |