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

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

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 | |