---

name: infra-drift-detector compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent infra drift detector 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: infra-drift-detector, infra drift detector, how do i infra-drift-detector, orchestrate infra-drift-detector, automate infra-drift-detector, agent infra-drift-detector 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"

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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

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: 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:

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.

• Terraform State Documentation
• Open Policy Agent (OPA) Governance
• AWS Config for Compliance Checking
• Cloud Custodian for Infrastructure Policy
• Infrastructure as Code (IaC) Security