# Terraform Infrastructure > Implements intelligent terraform infrastructure with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/terraform-infrastructure` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/terraform-infrastructure` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/terraform-infrastructure/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/terraform-infrastructure --- # Terraform Infrastructure Orchestrates intelligent skill selection and execution for terraform infrastructure 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 select_terraform_workflow( tf_dir: str, environment: str, available_strategies: List[Dict], min_compliance_score: float = 0.8 ) -> Optional[Dict]: """Select optimal Terraform execution strategy based on config state and environment. Evaluates Terraform configuration against compliance rules, drift status, and environment constraints to determine the safest execution path. Args: tf_dir: Path to Terraform configuration directory environment: Target environment (dev, staging, prod) available_strategies: List of execution strategies (init, validate, plan, apply) min_compliance_score: Minimum compliance threshold for production Returns: Selected strategy dictionary with execution parameters or None """ # Guard clause - Early Exit (Law 1) if not tf_dir or not os.path.isdir(tf_dir): raise ValueError(f"Invalid Terraform directory: {tf_dir}") if not available_strategies: raise ValueError("No execution strategies available") # Parse input - Make Illegal States Unrepresentable (Law 2) tf_vars = _load_tf_variables(tf_dir, environment) compliance_status = _check_compliance(tf_dir, environment) best_strategy = None best_score = 0.0 for strategy in available_strategies: score = _calculate_tf_strategy_score(strategy, compliance_status, tf_vars) if score > best_score and score >= min_compliance_score: best_score = score best_strategy = strategy if best_strategy is None: return None # Atomic Predictability (Law 3) - Return new dict, don't mutate result = dict(best_strategy) result["tf_dir"] = tf_dir result["environment"] = environment result["compliance_score"] = best_score result["execution_timestamp"] = time.time() return result ``` ### Pattern 2: Execution with Fallback ```python def execute_terraform_with_safety( strategy: Dict, tf_context: Dict, max_retries: int = 2 ) -> Dict: """Execute Terraform workflow with safety checks and fallback chain. Implements Fail Fast, Fail Loud principle (Law 4): - Validates state before execution - Locks state to prevent concurrent modifications - Falls back to plan-only or rollback on critical failures Fallback chain: 1. Retry with refreshed state 2. Execute plan-only for review 3. Trigger rollback/destroy if drift detected 4. Defer to human operator for production changes Args: strategy: Selected execution strategy metadata tf_context: Execution context including variables and state max_retries: Maximum retry attempts before fallback Returns: Execution result with metadata (success, timing, state_ref) Raises: TerraformExecutionError: If all retries and fallbacks exhausted """ # Guard clause - validate strategy (Early Exit) if not _is_tf_strategy_valid(strategy): raise TerraformExecutionError(f"Invalid Terraform strategy: {strategy.get('name', 'unknown')}") # Parse context - Ensure trusted state (Law 2) validated_context = _validate_tf_context(tf_context, strategy) for attempt in range(max_retries + 1): try: # Execute Terraform CLI with safety flags result = _run_tf_command(strategy["command"], validated_context) # Success - Atomic Predictability (Law 3) return { "success": True, "strategy_executed": strategy["name"], "result": result, "attempts": attempt + 1, "state_ref": result.get("state_ref"), "latency_ms": _calculate_latency() } except StateLockError as e: # Fail Fast - Don't proceed with locked state (Law 4) raise TerraformExecutionError( f"State locked in {strategy['name']}: {str(e)}" ) from e except PlanDriftError as e: # Drift detected - try fallback if attempt == max_retries: return _apply_tf_fallback_chain(strategy, validated_context) # All retries exhausted - Fail Loud (Law 4) raise TerraformExecutionError( f"Failed to execute Terraform {strategy['name']} after {max_retries + 1} attempts" ) ``` ### 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 | |---|---| | `infrastructure-as-code` | General IaC patterns that complement Terraform-specific implementations | | `cloudflare-infrastructure` | Cloud infrastructure patterns that work alongside Terraform-managed resources | --- ## 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 domain. The model follows markdown links at load time to resolve external references and inline content. - [Terraform Documentation](https://developer.hashicorp.com/terraform) — Official HashiCorp Terraform documentation covering providers, resources, modules, and state management - [Terraform Language Reference](https://developer.hashicorp.com/terraform/language) — Terraform language reference for HCL syntax, variables, outputs, and configuration blocks - [Terraform Best Practices (HashiCorp)](https://developer.hashicorp.com/terraform/best-practices) — Official HashiCorp best practices guide for Terraform project organization and workflows - [Terraform Registry](https://registry.terraform.io/) — HashiCorp's registry of community and official Terraform providers and modules - [Infrastructure as Code with Terraform (AWS)](https://aws.amazon.com/tfsm/) — AWS Terraform State Migration tool documentation for managing state transitions at scale