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