# Kubernetes Deployment > Implements intelligent kubernetes deployment with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/kubernetes-deployment-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/kubernetes-deployment-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/kubernetes-deployment-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/kubernetes-deployment-2 --- # Kubernetes Deployment Orchestrates intelligent skill selection and execution for kubernetes deployment 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 generate_k8s_deployment( app_name: str, namespace: str, image: str, replicas: int, port: int, resources: Dict[str, str] ) -> Dict: """Generate and validate a Kubernetes Deployment manifest. Ensures illegal states are unrepresentable by validating image format, resource constraints, and namespace existence before manifest creation. """ if not app_name or not namespace: raise ValueError("Application name and namespace are required") # Validate image format and tag if ":" not in image: image = f"{image}:latest" if not re.match(r"^[a-zA-Z0-9._/-]+:[a-zA-Z0-9._-]+$", image): raise ValueError(f"Invalid container image format: {image}") # Parse resource limits/requests to ensure valid Kubernetes format parsed_resources = {} for key, value in resources.items(): if not re.match(r"^\d+(\.\d+)?(Ki|Mi|Gi|Ti|K|M|G|T)?$", value): raise ValueError(f"Invalid resource specification for {key}: {value}") parsed_resources[key] = value # Construct immutable deployment manifest deployment = { "apiVersion": "apps/v1", "kind": "Deployment", "metadata": { "name": app_name, "namespace": namespace, "labels": {"app": app_name, "managed-by": "opencode-skill"} }, "spec": { "replicas": replicas, "selector": {"matchLabels": {"app": app_name}}, "template": { "metadata": {"labels": {"app": app_name}}, "spec": { "containers": [{ "name": app_name, "image": image, "ports": [{"containerPort": port}], "resources": { "requests": {"cpu": parsed_resources.get("cpu_req", "100m"), "memory": parsed_resources.get("mem_req", "128Mi")}, "limits": {"cpu": parsed_resources.get("cpu_lim", "500m"), "memory": parsed_resources.get("mem_lim", "256Mi")} } }] } } } } return deployment ``` ### Pattern 2: Execution with Fallback ```python def apply_and_monitor_deployment( k8s_client: Client, deployment_manifest: Dict, timeout_seconds: int = 300, health_check_path: str = "/healthz" ) -> Dict: """Apply Kubernetes deployment and monitor rollout status with fallback rollback. Implements fail-fast and fail-loud principles: - Validates cluster connectivity before apply - Polls rollout status with exponential backoff - Automatically rolls back on health check failure """ namespace = deployment_manifest["metadata"]["namespace"] name = deployment_manifest["metadata"]["name"] # Apply manifest (Fail Fast on invalid cluster state) try: k8s_client.apps_v1.create_namespaced_deployment( namespace=namespace, body=deployment_manifest ) except ApiException as e: raise RuntimeError(f"Failed to create deployment {name}: {e.reason}") from e # Monitor rollout status start_time = time.time() while time.time() - start_time < timeout_seconds: try: status = k8s_client.apps_v1.read_namespaced_deployment_status( name=name, namespace=namespace ) if status.status.ready_replicas == status.spec.replicas: # Verify health endpoint health_ok = _check_pod_health(k8s_client, namespace, name, health_check_path) if health_ok: return { "status": "deployed", "replicas_ready": status.status.ready_replicas, "image": deployment_manifest["spec"]["template"]["spec"]["containers"][0]["image"] } else: # Health check failed - trigger rollback (Fallback) _rollback_deployment(k8s_client, namespace, name) raise RuntimeError(f"Health check failed for {name}, rolled back") except ApiException as e: if e.status == 404: time.sleep(5) continue raise # Timeout - Fail Loud _rollback_deployment(k8s_client, namespace, name) raise TimeoutError(f"Deployment {name} did not complete within {timeout_seconds}s") ``` ### 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 | | --- --- ## 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. - [Kubernetes Deployment Documentation]() - [Helm Package Manager Guide]() - [Kubernetes Networking Model]() - [K8s Security Best Practices (CIS Benchmark)]() - [ArgoCD GitOps for Kubernetes Deployments]()