---

name: k8s-debugger compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent k8s debugger 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: k8s-debugger, k8s debugger, how do i k8s-debugger, orchestrate k8s-debugger, automate k8s-debugger, agent k8s-debugger, kubernetes, container orchestration 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"

---

K8S Debugger

Orchestrates intelligent skill selection and execution for k8s debugger 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 parse_k8s_debug_request(
    user_query: str,
    cluster_context: Dict[str, Any],
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Parse Kubernetes debugging request and map to debugging strategy.
    
    Extracts resource types, namespaces, and error signatures from user input.
    Maps to appropriate debugging workflows based on k8s error patterns.
    
    Args:
        user_query: Natural language description of the k8s issue
        cluster_context: Active cluster config, available namespaces, auth tokens
        min_confidence: Minimum confidence threshold for strategy selection
        
    Returns:
        Debugging strategy dict with target resources, error type, and steps
    """
    if not user_query or not user_query.strip():
        raise ValueError("Kubernetes debug query cannot be empty")
        
    # Extract k8s entities and error signatures
    entities = _extract_k8s_entities(user_query)
    error_type = _classify_k8s_error(user_query)
    
    # Map to debugging strategy
    strategy_map = {
        "CrashLoopBackOff": "pod_restart_analysis",
        "ImagePullBackOff": "registry_auth_check",
        "OOMKilled": "resource_limit_analysis",
        "Pending": "scheduler_affinity_check",
        "default": "general_cluster_health"
    }
    
    strategy_name = strategy_map.get(error_type, "general_cluster_health")
    
    # Validate against available cluster context
    if not _verify_cluster_access(cluster_context, entities.get("namespace")):
        raise PermissionError("Insufficient RBAC permissions for target namespace")
        
    return {
        "strategy": strategy_name,
        "target_resources": entities.get("resources", []),
        "namespace": entities.get("namespace", "default"),
        "error_signature": error_type,
        "confidence": 0.85,
        "steps": _generate_debug_steps(strategy_name)
    }

Pattern 2: Execution with Fallback

def execute_k8s_debug_workflow(
    strategy: Dict,
    cluster_client: Any,
    max_retries: int = 2
) -> Dict:
    """Execute Kubernetes debugging workflow with resilient error handling.
    
    Runs targeted kubectl diagnostics, parses cluster state, and applies
    fallback strategies if initial diagnostics are inconclusive.
    
    Args:
        strategy: Debugging strategy from parse_k8s_debug_request
        cluster_client: Initialized kubernetes client or kubectl wrapper
        max_retries: Maximum retry attempts for transient API errors
        
    Returns:
        Debug findings dict with root cause, evidence, and remediation steps
    """
    namespace = strategy.get("namespace", "default")
    resources = strategy.get("target_resources", [])
    
    for attempt in range(max_retries + 1):
        try:
            # Gather diagnostic data in parallel
            pod_status = cluster_client.get_resource_status("pods", namespace)
            recent_events = cluster_client.get_events(namespace, since="1h")
            resource_logs = cluster_client.get_logs(resources, tail=100)
            
            # Analyze findings against known k8s failure patterns
            findings = _analyze_k8s_findings(pod_status, recent_events, resource_logs)
            
            if findings.get("root_cause"):
                return {
                    "success": True,
                    "strategy_executed": strategy["strategy"],
                    "findings": findings,
                    "remediation": _generate_remediation(findings),
                    "attempts": attempt + 1
                }
                
        except ApiException as e:
            if e.status == 429:  # API server throttling
                time.sleep(2 ** attempt)
                continue
            elif e.status == 403:
                raise PermissionError("RBAC denied during diagnostic collection") from e
            raise
            
    # Fallback: Escalate to cluster-level diagnostics
    return _execute_cluster_fallback_diagnostics(strategy, cluster_client)

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 | |

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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 kubectl Debug Command
• Kubernetes Troubleshooting Guide
• EKS Pod Troubleshooting (AWS)
• kubectl Debug with ephemeral containers
• Kubernetes Event Diagnostics