# Clickup Automation > Implements intelligent clickup automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/clickup-automation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/clickup-automation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/clickup-automation/raw - Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS) - 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/clickup-automation --- # Clickup Automation Orchestrates intelligent skill selection and execution for clickup 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_clickup_action( user_request: str, workspace_id: str, available_lists: List[Dict], min_confidence: float = 0.7 ) -> Optional[Dict]: """Route a natural language request to the appropriate ClickUp action. Validates workspace access, extracts intent (create/update/comment), and matches against available ClickUp lists/tasks. """ # Guard clause - Early Exit (Law 1) if not user_request or not workspace_id: raise ValueError("Request and workspace_id are required") # Parse intent and extract entities at boundary (Law 2) intent = _extract_clickup_intent(user_request) target_list_id = _resolve_list_id(user_request, available_lists) if intent not in ("create_task", "update_status", "add_comment", "assign"): return None # Score available actions based on list availability and historical success best_action = None best_score = 0.0 for action in available_actions: score = _calculate_action_match(intent, target_list_id, action) if score > best_score and score >= min_confidence: best_score = score best_action = action if not best_action: return None # Atomic Predictability (Law 3) - Return new dict, don't mutate return { "action": best_action["type"], "target_list_id": target_list_id, "workspace_id": workspace_id, "confidence": best_score, "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def execute_clickup_operation( action_config: Dict, api_key: str, max_retries: int = 2 ) -> Dict: """Execute a ClickUp API operation with resilience patterns. Handles rate limits, transient network errors, and implements a fallback chain: direct API -> webhook queue -> manual sync log. """ base_url = f"https://api.clickup.com/api/v2" headers = {"Authorization": api_key, "Content-Type": "application/json"} for attempt in range(max_retries + 1): try: if action_config["action"] == "create_task": payload = {"name": action_config["task_name"], "description": action_config.get("desc", "")} response = requests.post(f"{base_url}/list/{action_config['target_list_id']}/task", headers=headers, json=payload) elif action_config["action"] == "update_status": payload = {"status": action_config["new_status"]} response = requests.put(f"{base_url}/task/{action_config['task_id']}", headers=headers, json=payload) else: raise ValueError(f"Unsupported action: {action_config['action']}") response.raise_for_status() return { "success": True, "clickup_id": response.json().get("id"), "attempts": attempt + 1, "latency_ms": time.time() * 1000 } except requests.exceptions.HTTPError as e: if e.response.status_code == 429: time.sleep(2 ** attempt) # Exponential backoff continue raise ClickUpAPIError(f"ClickUp API failed: {e}") from e except requests.exceptions.ConnectionError: if attempt == max_retries: return _queue_for_manual_sync(action_config) # Fail Loud (Law 4) - All retries exhausted raise ClickUpAPIError(f"Failed after {max_retries + 1} attempts") ``` ### 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 | |