# Outlook Automation > Implements intelligent outlook automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/outlook-automation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/outlook-automation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/outlook-automation/raw - Safety review: PASS (external: skill-scanner PASS, skillspector CAUTION) - 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/outlook-automation --- # 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 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 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 ```python 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: 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 --- ## 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 | |