paulpas Infra Drift Detector
Orchestrates intelligent skill selection and execution for infra drift detector 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: Drift State Comparison & Scoring
def evaluate_drift_state(
desired_state: Dict[str, Any],
current_state: Dict[str, Any],
drift_threshold: float = 0.85
) -> Dict[str, Any]:
"""Evaluate infrastructure drift between desired and current state.
Implements Law 2 (Parse at boundary) by validating IaC formats before diffing.
Implements Law 1 (Early Exit) by returning immediately on schema mismatches.
Args:
desired_state: Parsed Terraform/CloudFormation/Policy state
current_state: Live cloud provider state or last known snapshot
drift_threshold: Minimum confidence to trigger auto-remediation
Returns:
Drift analysis dict with severity, affected resources, and confidence
"""
# Law 1: Early exit on invalid state formats
if not _validate_state_schema(desired_state) or not _validate_state_schema(current_state):
raise ValueError("Invalid state format: expected Terraform/CloudFormation schema")
# Law 2: Parse inputs at boundary - extract resource IDs and attributes
desired_resources = _extract_resources(desired_state)
current_resources = _extract_resources(current_state)
drift_report = {
"total_resources": len(desired_resources),
"drifted_resources": [],
"confidence_score": 0.0,
"severity": "low"
}
for res_id, desired_attrs in desired_resources.items():
current_attrs = current_resources.get(res_id, {})
match_ratio = _calculate_attribute_similarity(desired_attrs, current_attrs)
if match_ratio < drift_threshold:
drift_report["drifted_resources"].append({
"resource_id": res_id,
"match_ratio": match_ratio,
"missing_attrs": set(desired_attrs.keys()) - set(current_attrs.keys())
})
# Law 3: Atomic predictability - return new structure
drift_report["confidence_score"] = _compute_overall_confidence(drift_report["drifted_resources"])
drift_report["severity"] = "critical" if len(drift_report["drifted_resources"]) > 5 else "moderate"
return drift_report
Pattern 2: Drift Execution & Remediation Routing
def execute_drift_response(
drift_report: Dict[str, Any],
routing_config: Dict[str, Any],
fallback_handlers: List[str] = None
) -> Dict[str, Any]:
"""Execute response workflow for detected infrastructure drift.
Implements Law 4 (Fail Fast/Loud) by halting on missing remediation configs.
Implements fallback chain: auto-remediate -> alert -> defer to human.
Args:
drift_report: Output from evaluate_drift_state
routing_config: Maps severity levels to execution pipelines
fallback_handlers: Ordered list of fallback skill names
Returns:
Execution result with applied actions and audit trail
"""
# Law 1: Early exit if no drift detected
if not drift_report.get("drifted_resources"):
return {"status": "clean", "actions_taken": [], "audit_log": []}
# Law 2: Parse routing config at boundary
pipeline = routing_config.get(drift_report["severity"], routing_config.get("default"))
if not pipeline:
raise ValueError(f"No execution pipeline configured for severity: {drift_report['severity']}")
audit_log = []
actions_taken = []
for step in pipeline.get("steps", []):
try:
# Law 4: Fail immediately on invalid step config
if not _validate_step_config(step):
raise ValueError(f"Invalid step configuration: {step.get('id')}")
result = _execute_drift_step(step, drift_report)
actions_taken.append(result)
audit_log.append({"step": step["id"], "status": "success", "timestamp": time.time()})
except TransientAPIError:
# Fallback chain: retry -> alternative handler -> human
if fallback_handlers:
audit_log.append({"step": step["id"], "status": "fallback_triggered", "timestamp": time.time()})
return _apply_drift_fallback(fallback_handlers, drift_report, audit_log)
raise
# Law 3: Return immutable result structure
return {
"status": "completed",
"actions_taken": actions_taken,
"audit_log": audit_log,
"drift_resolved": len(actions_taken) == len(drift_report["drifted_resources"])
}
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.