---

name: helpdesk-automation compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent helpdesk automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: helpdesk-automation, helpdesk automation, how do i helpdesk-automation, orchestrate helpdesk-automation, automate helpdesk-automation, agent helpdesk-automation archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

Helpdesk Automation

Orchestrates intelligent skill selection and execution for helpdesk 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_helpdesk_request(
    ticket: Dict[str, Any],
    support_channels: List[Dict[str, Any]],
    sla_threshold_hours: float = 4.0
) -> Dict[str, Any]:
    """Route a helpdesk ticket to the optimal support channel.
    
    Evaluates channels based on:
    - Intent match (billing, technical, account, general)
    - Current queue depth and agent availability
    - SLA urgency and historical resolution time
    
    Args:
        ticket: Parsed ticket data with intent, priority, and customer tier
        support_channels: List of available channel configs with capacity limits
        sla_threshold_hours: Max hours before escalation is triggered
        
    Returns:
        Selected channel config with routing metadata
    """
    if not ticket.get("intent") or not support_channels:
        raise ValueError("Ticket intent and at least one support channel are required")
        
    intent = ticket["intent"].lower()
    priority = ticket.get("priority", "medium")
    customer_tier = ticket.get("customer_tier", "standard")
    
    best_channel = None
    best_score = -1.0
    
    for channel in support_channels:
        # Calculate match score based on intent alignment and capacity
        intent_match = 1.0 if intent in channel["supported_intents"] else 0.3
        capacity_factor = 1.0 - (channel["current_queue"] / max(channel["max_capacity"], 1))
        priority_boost = {"critical": 1.5, "high": 1.2, "medium": 1.0, "low": 0.8}.get(priority, 1.0)
        
        score = intent_match * capacity_factor * priority_boost
        
        # Apply customer tier weighting
        if customer_tier == "enterprise":
            score *= 1.2
            
        if score > best_score:
            best_score = score
            best_channel = channel
            
    if best_channel is None or best_score < 0.5:
        return {"fallback": "general_triage", "reason": "low_match_score", "score": best_score}
        
    return {
        "channel_id": best_channel["id"],
        "channel_name": best_channel["name"],
        "estimated_wait_minutes": int(best_channel["avg_wait_time"]),
        "routing_score": round(best_score, 3),
        "sla_compliant": best_channel["avg_wait_time"] <= sla_threshold_hours * 60
    }

Pattern 2: Execution with Fallback

def execute_ticket_routing(
    channel_config: Dict[str, Any],
    ticket_data: Dict[str, Any],
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute ticket routing with resilience patterns for helpdesk systems.
    
    Implements fallback chain for ticketing API failures:
    1. Retry with exponential backoff
    2. Route to backup channel (e.g., email queue)
    3. Escalate to human supervisor if SLA breach imminent
    
    Args:
        channel_config: Target support channel configuration
        ticket_data: Validated ticket payload ready for submission
        max_retries: Maximum API retry attempts
        
    Returns:
        Routing result with ticket ID, status, and fallback metadata
    """
    ticketing_api = get_ticketing_service(channel_config["provider"])
    fallback_queue = get_email_queue(channel_config.get("backup_email"))
    
    for attempt in range(max_retries + 1):
        try:
            # Submit to primary helpdesk system
            response = ticketing_api.create_ticket(
                subject=ticket_data["subject"],
                body=ticket_data["body"],
                priority=ticket_data["priority"],
                tags=ticket_data.get("tags", [])
            )
            
            return {
                "success": True,
                "ticket_id": response["id"],
                "channel": channel_config["name"],
                "attempts": attempt + 1,
                "sla_status": "on_track"
            }
            
        except RateLimitError:
            if attempt < max_retries:
                time.sleep(2 ** attempt)
                continue
            # Fallback to email queue when API is throttled
            return _route_to_email_queue(fallback_queue, ticket_data)
            
        except ConnectionError as e:
            # System down - escalate to human if SLA critical
            if ticket_data.get("priority") == "critical":
                return _escalate_to_human_supervisor(ticket_data)
            raise TicketRoutingError(f"Helpdesk system unreachable: {e}")
            
    return _route_to_email_queue(fallback_queue, ticket_data)

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

Related Skills

| Skill | Purpose | |

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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.

• Zendesk API Documentation
• Freshdesk Automation Guide
• Intercom API for Customer Support
• Help Scout API Documentation
• Jira Service Management REST API