---

name: kubernetes-deployment compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent kubernetes deployment with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: kubernetes-deployment, kubernetes deployment, how do i kubernetes-deployment, orchestrate kubernetes-deployment, automate kubernetes-deployment, agent kubernetes-deployment, container orchestration, deployment archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

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

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._-]+quot;, 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)?quot;, 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

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