paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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._-]+$", image):
raise ValueError(f"Invalid container image format: {image}")
# Parse resource limits/requests to ensure valid Kubernetes format
parsed_resources = {}
for key, value in resources.items():
if not re.match(r"^\d+(\.\d+)?(Ki|Mi|Gi|Ti|K|M|G|T)?$", 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
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.