Workflow Automation

Workflow Automation

Orchestrates intelligent skill selection and execution for workflow 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_workflow_step(
    step_definition: Dict[str, Any],
    environment_context: Dict[str, str],
    historical_metrics: Dict[str, List[float]]
) -> Dict[str, Any]:
    """Route a workflow step to the optimal execution target.
    
    Evaluates step requirements against available runners/environments,
    factoring in historical success rates, environment constraints,
    and trigger matching for CI/CD or automation pipelines.
    
    Args:
        step_definition: Parsed step from workflow YAML/JSON
        environment_context: Current runtime environment variables & constraints
        historical_metrics: Past execution success rates per target
        
    Returns:
        Routing decision with target, confidence, and execution strategy
    """
    # Guard clause - validate step structure (Law 1)
    required_keys = {"id", "type", "triggers", "targets"}
    if not required_keys.issubset(step_definition.keys()):
        raise ValueError(f"Step missing required keys: {required_keys - step_definition.keys()}")
        
    # Parse constraints - Make illegal states unrepresentable (Law 2)
    target_pool = [t for t in step_definition["targets"] if t in environment_context]
    if not target_pool:
        return {"status": "unroutable", "reason": "no_valid_targets"}
        
    best_target = None
    best_score = 0.0
    
    for target in target_pool:
        # Calculate composite routing score
        trigger_match = _match_triggers(step_definition["triggers"], environment_context)
        historical_success = historical_metrics.get(target, [0.0])
        avg_success = sum(historical_success) / len(historical_success) if historical_success else 0.0
        env_compatibility = _calculate_env_compatibility(step_definition, target)
        
        score = (trigger_match * 0.4) + (avg_success * 0.4) + (env_compatibility * 0.2)
        
        if score > best_score:
            best_score = score
            best_target = target
            
    # Return immutable routing decision (Law 3)
    return {
        "status": "routed",
        "target": best_target,
        "confidence": best_score,
        "strategy": "parallel" if step_definition.get("parallel") else "sequential",
        "metadata": {"evaluated_targets": len(target_pool), "timestamp": time.time()}
    }

Pattern 2: Execution with Fallback

def execute_automation_step(
    step_config: Dict[str, Any],
    execution_context: Dict[str, Any],
    fallback_targets: List[str] = None
) -> Dict[str, Any]:
    """Execute a workflow automation step with resilient fallback handling.
    
    Implements domain-specific execution for CI/CD pipelines, API integrations,
    and infrastructure automation. Handles transient failures, rate limits,
    and environment-specific constraints with structured fallback chains.
    
    Args:
        step_config: Step configuration including command, timeout, and retry policy
        execution_context: Runtime context with secrets, environment vars, and state
        fallback_targets: Alternative execution targets if primary fails
        
    Returns:
        Execution result with status, output, timing, and fallback metadata
    """
    # Guard clause - validate execution prerequisites (Law 1)
    if not step_config.get("command") or not execution_context.get("secrets"):
        raise ValueError("Missing required command or secrets for execution")
        
    # Parse context securely - Ensure trusted state (Law 2)
    sanitized_context = _sanitize_execution_context(execution_context)
    max_retries = step_config.get("retry_policy", {}).get("max_attempts", 2)
    
    for attempt in range(max_retries + 1):
        try:
            # Execute step with timeout and resource limits
            result = _run_automation_command(
                command=step_config["command"],
                context=sanitized_context,
                timeout=step_config.get("timeout", 300)
            )
            
            # Validate output schema - Fail fast on invalid state (Law 4)
            _validate_step_output(result, step_config.get("output_schema"))
            
            return {
                "status": "success",
                "step_id": step_config["id"],
                "output": result,
                "attempts": attempt + 1,
                "latency_ms": time.time() * 1000,
                "fallback_used": False
            }
            
        except RateLimitError as e:
            # Transient - apply exponential backoff
            if attempt < max_retries:
                time.sleep(2 ** attempt)
                continue
            return _trigger_fallback(step_config, fallback_targets, "rate_limited")
            
        except CommandTimeoutError as e:
            # Resource constraint - switch to alternative target
            if fallback_targets:
                return _trigger_fallback(step_config, fallback_targets, "timeout")
            raise
            
    # All retries exhausted - Fail loud with audit trail (Law 4)
    return {
        "status": "failed",
        "step_id": step_config["id"],
        "error": "max_retries_exceeded",
        "attempts": max_retries + 1,
        "fallback_triggered": True
    }

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

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.

• GitHub Actions Workflow Syntax
• GitLab CI/CD Pipeline Configuration
• Actions Runner Controller (ARC) Documentation
• Docker Buildx Documentation
• Semgrep Static Analysis Rules

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