# Lambda Lang > Implements intelligent lambda lang with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/lambda-lang` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/lambda-lang` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/lambda-lang/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/lambda-lang --- # Lambda Lang Orchestrates intelligent skill selection and execution for lambda lang 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 parse_and_score_lambda_request( raw_request: str, available_lambdas: List[Dict], min_confidence: float = 0.75 ) -> Optional[Dict]: """Parse a lambda-lang request and score against available serverless functions. Implements Law 1 (Early Exit) and Law 2 (Immutable State) by validating the request structure before attempting any scoring or routing. """ if not raw_request or not isinstance(raw_request, str): raise ValueError("Lambda-lang request must be a non-empty string") # Law 2: Parse at boundary, never mutate raw input parsed = _parse_lambda_syntax(raw_request) if not parsed.get("intent") or not parsed.get("target_service"): raise ValueError("Missing required lambda-lang fields: intent, target_service") best_match = None best_score = 0.0 for func in available_lambdas: # Multi-factor scoring: trigger match, historical latency, region health trigger_match = _calculate_trigger_similarity(parsed["intent"], func.get("triggers", [])) latency_penalty = func.get("avg_cold_start_ms", 0) / 1000.0 region_health = func.get("availability_score", 1.0) score = (trigger_match * 0.6) + ((1.0 - latency_penalty) * 0.2) + (region_health * 0.2) if score > best_score and score >= min_confidence: best_score = score best_match = func if best_match is None: return None # Law 3: Return new structure, preserve original function metadata return { "selected_function": best_match["arn"], "confidence": best_score, "parsed_intent": parsed, "routing_metadata": {"timestamp": time.time(), "region": best_match.get("preferred_region")} } ``` ### Pattern 2: Execution with Fallback ```python def execute_lambda_with_fallback( routing_result: Dict, execution_context: Dict, max_retries: int = 2 ) -> Dict: """Execute a selected lambda function with a serverless-aware fallback chain. Implements Law 4 (Fail Fast) by immediately rejecting invalid execution contexts and Law 1 by exiting early on transient network failures before exhausting retries. """ if not routing_result or not execution_context.get("payload"): raise ValueError("Missing routing result or execution payload") target_arn = routing_result["selected_function"] payload = execution_context["payload"] for attempt in range(max_retries + 1): try: # Law 3: Pass immutable payload copy to avoid side effects result = _invoke_lambda(target_arn, payload.copy()) return { "status": "success", "function_arn": target_arn, "output": result.get("Payload"), "latency_ms": result.get("ResponseMetadata", {}).get("HTTPHeaders", {}).get("x-amzn-RequestId"), "attempts": attempt + 1 } except LambdaColdStartError: # Law 4: Fail fast on known cold start, trigger fallback immediately if attempt == 0: return _fallback_to_provisioned_concurrency(target_arn, payload) continue except TransientNetworkError: if attempt == max_retries: return _fallback_to_edge_function(target_arn, payload) continue # All retries exhausted raise LambdaExecutionError(f"Failed to invoke {target_arn} 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 | | --- --- ## 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. - [Lambda Calculus (Wikipedia)]() - [Haskell Programming Language Guide]() - [Functional Programming Concepts (OCaml)]() - [Rust Ownership and Lambda Closures]() - [Category Theory for Programmers (Bartosz Milewski)]()