paulpas Freshdesk Automation
Orchestrates intelligent skill selection and execution for freshdesk 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_freshdesk_intent(
user_request: str,
available_automations: List[Dict],
min_confidence: float = 0.7
) -> Optional[Dict]:
"""Route a user request to the appropriate Freshdesk automation.
Evaluates Freshdesk-specific intents like ticket creation, status updates,
SLA checks, and customer lookup against available automation endpoints.
"""
# Guard clause - Early Exit (Law 1)
if not user_request or not user_request.strip():
raise ValueError("Freshdesk request cannot be empty")
if not available_automations:
raise ValueError("No Freshdesk automations configured")
# Parse input - Make Illegal States Unrepresentable (Law 2)
intent_features = _extract_freshdesk_features(user_request)
best_automation = None
best_score = 0.0
for auto in available_automations:
# Calculate match based on Freshdesk API triggers and historical success
score = _calculate_freshdesk_match_score(intent_features, auto)
if score > best_score and score >= min_confidence:
best_score = score
best_automation = auto
if best_automation is None:
return None
# Atomic Predictability (Law 3) - Return new dict, don't mutate
return {
"automation_id": best_automation["id"],
"endpoint": best_automation["endpoint"],
"confidence": best_score,
"timestamp": time.time()
}
Pattern 2: Execution with Fallback
def execute_freshdesk_automation(
automation: Dict,
ticket_context: Dict,
max_retries: int = 2
) -> Dict:
"""Execute a Freshdesk automation with API resilience patterns.
Handles Freshdesk API rate limits (429), authentication failures (401/403),
and transient network errors with exponential backoff and fallback routing.
"""
# Guard clause - validate automation (Early Exit)
if not _is_freshdesk_automation_valid(automation):
raise FreshdeskAutomationError(f"Invalid Freshdesk automation: {automation.get('id')}")
# Parse context - Ensure trusted state (Law 2)
validated_payload = _prepare_freshdesk_payload(ticket_context, automation)
for attempt in range(max_retries + 1):
try:
response = _call_freshdesk_api(
endpoint=automation["endpoint"],
payload=validated_payload,
method=automation.get("method", "POST")
)
# Success - Atomic Predictability (Law 3)
if response.status_code in (200, 201):
return {
"success": True,
"freshdesk_ticket_id": response.json().get("id"),
"attempts": attempt + 1,
"latency_ms": _get_request_duration()
}
elif response.status_code == 429:
# Rate limited - apply Freshdesk recommended backoff
wait_time = min(2 ** attempt * 0.5, 5.0)
time.sleep(wait_time)
continue
else:
raise FreshdeskAPIError(f"API returned {response.status_code}")
except FreshdeskAPIError as e:
# Fail Fast - Don't try to patch bad data (Law 4)
raise e
except TransientNetworkError:
# Transient error - try fallback
if attempt == max_retries:
return _escalate_to_manual_freshdesk_ticket(ticket_context)
# All retries exhausted - Fail Loud (Law 4)
raise FreshdeskAutomationError(
f"Failed Freshdesk automation 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:
- 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
Related Skills
| Skill | Purpose | |
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.