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 ```python 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 ```python 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](<https://aws.amazon.com/documentation/>) - [Terraform Registry & Providers](<https://registry.terraform.io/>) - [Kubernetes Documentation](<https://kubernetes.io/docs/home/>) - [Google Cloud Documentation](<https://cloud.google.com/docs>) - [Azure Documentation](<https://learn.microsoft.com/en-us/azure/>) ## Related Skills | Skill | Purpose | |