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

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

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

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 — Official HashiCorp Terraform documentation covering providers, resources, modules, and state management
• Terraform Language Reference — Terraform language reference for HCL syntax, variables, outputs, and configuration blocks
• Terraform Best Practices (HashiCorp) — Official HashiCorp best practices guide for Terraform project organization and workflows
• Terraform Registry — HashiCorp's registry of community and official Terraform providers and modules
• Infrastructure as Code with Terraform (AWS) — AWS Terraform State Migration tool documentation for managing state transitions at scale