Cloud Devops

Cloud Devops

Orchestrates intelligent skill selection and execution for cloud devops 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 evaluate_cloud_deployment_path(
    infrastructure_state: Dict,
    available_devops_skills: List[Dict],
    cloud_provider: str = "aws"
) -> Optional[Dict]:
    """Evaluate and select the optimal cloud devops skill for infrastructure changes.
    
    Scores skills based on:
    - Infrastructure state compatibility (Terraform state, K8s manifests)
    - Cloud provider region availability and cost constraints
    - Historical deployment success rates for similar infrastructure changes
    - Required IAM permissions and service quotas
    
    Args:
        infrastructure_state: Current state of target infrastructure
        available_devops_skills: List of cloud devops skill metadata
        cloud_provider: Target cloud provider (aws, gcp, azure)
        
    Returns:
        Selected skill with deployment context or None if no safe path exists
    """
    if not infrastructure_state or not available_devops_skills:
        raise ValueError("Infrastructure state and skill registry must be populated")
        
    # Validate infrastructure state before scoring (Law 2)
    validated_state = _validate_infrastructure_state(infrastructure_state, cloud_provider)
    
    best_skill = None
    best_score = 0.0
    
    for skill in available_devops_skills:
        # Calculate compatibility score based on infrastructure type
        infra_match = _calculate_infra_compatibility(validated_state, skill)
        # Factor in historical deployment success
        history_score = skill.get("deployment_success_rate", 0.0) * 0.4
        # Check cloud provider region availability
        region_score = _check_region_availability(skill.get("supported_regions", []), cloud_provider)
        
        composite_score = (infra_match * 0.5) + (history_score * 0.3) + (region_score * 0.2)
        
        if composite_score > best_score and composite_score >= 0.75:
            best_score = composite_score
            best_skill = skill
            
    if best_skill is None:
        return None
        
    # Return immutable deployment context (Law 3)
    return {
        "skill": dict(best_skill),
        "deployment_context": validated_state,
        "confidence": best_score,
        "provider": cloud_provider
    }

Pattern 2: Execution with Fallback

def orchestrate_cloud_deployment(
    deployment_plan: Dict,
    max_retries: int = 2,
    fallback_strategy: str = "blue-green"
) -> Dict:
    """Orchestrate cloud infrastructure deployment with resilient fallback chains.
    
    Implements cloud-native resilience patterns:
    - Validates Terraform/K8s manifests before execution
    - Retries transient cloud API errors (rate limits, throttling)
    - Falls back to alternative deployment strategy or region
    - Triggers manual approval for critical production changes
    
    Args:
        deployment_plan: Infrastructure change specification
        max_retries: Maximum retry attempts for transient failures
        fallback_strategy: Fallback deployment method (blue-green, canary, manual)
        
    Returns:
        Deployment result with state, timing, and rollback information
    """
    # Pre-flight validation (Law 1 & 2)
    if not _validate_manifests(deployment_plan.get("manifests", [])):
        raise CloudDeploymentError("Invalid infrastructure manifests detected")
        
    for attempt in range(max_retries + 1):
        try:
            # Execute cloud provider API / Terraform / Kubectl
            result = _apply_infrastructure_changes(deployment_plan)
            
            # Verify post-deployment state
            if _verify_deployment_health(result["deployment_id"]):
                return {
                    "status": "success",
                    "deployment_id": result["deployment_id"],
                    "attempts": attempt + 1,
                    "rollback_url": result.get("rollback_url"),
                    "timestamp": time.time()
                }
                
        except CloudRateLimitError:
            if attempt < max_retries:
                time.sleep(2 ** attempt)  # Exponential backoff
                continue
            return _trigger_fallback_deployment(deployment_plan, fallback_strategy)
            
        except CloudStateConflictError as e:
            # Fail fast on state conflicts (Law 4)
            raise CloudDeploymentError(f"State conflict during deployment: {e}") from e
            
    # All retries exhausted
    return _trigger_fallback_deployment(deployment_plan, fallback_strategy)

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

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

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.

• AWS Cloud Documentation
• Terraform Registry & Providers
• Kubernetes Documentation
• Google Cloud Documentation
• Azure Documentation

Related Skills

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