# K8S Debugger > Implements intelligent k8s debugger with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/k8s-debugger` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/k8s-debugger` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/k8s-debugger/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/k8s-debugger --- # 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 ```python 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 ```python 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 | | --- --- ## 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]()