paulpas 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
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 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:
- 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.