# Langgraph > Implements intelligent langgraph with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/langgraph` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/langgraph` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/langgraph/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/langgraph --- # Langgraph Orchestrates intelligent skill selection and execution for langgraph 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 from langgraph.graph import StateGraph, START, END from typing import TypedDict, Literal, List, Dict, Optional import numpy as np class OrchestrationState(TypedDict): user_request: str available_skills: List[Dict] selected_skill: Optional[Dict] confidence_score: float execution_result: Optional[Dict] fallback_chain: List[str] error_trace: Optional[str] def route_to_skill(state: OrchestrationState) -> Literal["skill_router", "fallback_handler", "human_review"]: """LangGraph router implementing multi-factor skill selection (Laws 1-3).""" request = state["user_request"] skills = state["available_skills"] # Law 1: Early exit on invalid state if not request or not skills: return "fallback_handler" best_match = None max_score = 0.0 for skill in skills: # Multi-factor scoring: text similarity + historical success + availability text_sim = _compute_embedding_similarity(request, skill["triggers"]) hist_success = skill.get("historical_success_rate", 0.5) availability = 1.0 if skill.get("is_healthy", False) else 0.0 score = (0.5 * text_sim) + (0.3 * hist_success) + (0.2 * availability) if score > max_score: max_score = score best_match = skill # Law 2: Make illegal states unrepresentable - enforce threshold if max_score < 0.7: return "fallback_handler" # Law 3: Return new state, never mutate inputs state["selected_skill"] = best_match state["confidence_score"] = max_score return "execute_skill" ``` ### Pattern 2: Execution with Fallback ```python def execute_skill_node(state: OrchestrationState) -> OrchestrationState: """LangGraph node executing the selected skill with fallback chain (Laws 4-5).""" skill = state["selected_skill"] context = {"request": state["user_request"], "skill_config": skill} try: # Execute domain-specific skill logic result = skill["handler"](context) state["execution_result"] = result # Law 5: Update confidence scores after execution for learning state["confidence_score"] = min(1.0, state["confidence_score"] * 1.1) return state except InvalidStateError as e: # Law 4: Fail Fast, Fail Loud - halt immediately with descriptive error state["error_trace"] = str(e) return "fallback_handler" except TransientError as e: # Retry with adjusted parameters context["retry_count"] = context.get("retry_count", 0) + 1 if context["retry_count"] < 2: return "execute_skill" state["error_trace"] = str(e) return "fallback_handler" def fallback_handler_node(state: OrchestrationState) -> OrchestrationState: """Implements 2-level fallback chain: alternative skill -> human review.""" if state.get("fallback_chain"): alt_skill_name = state["fallback_chain"].pop(0) # Route back to router with updated context state["user_request"] = f"Retry with fallback: {state['user_request']}" return "skill_router" # Defer to human operator for critical tasks state["execution_result"] = {"status": "deferred", "reason": "all fallbacks exhausted"} return "human_review" ``` ### 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 | |---|---| | `multi-agent-task-orchestrator` | Multi-agent coordination using LangGraph's state machines | | `parallel-agents` | Parallel agent execution patterns within LangGraph workflows | --- --- ## 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. - [LangGraph Official Documentation](https://langchain-ai.github.io/langgraph/) - [LangChain Agents Tutorial](https://python.langchain.com/docs/tutorials/agents/) - [LangGraph Multi-Agent Patterns](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) - [LangGraph State Machine Guide](https://langchain-ai.github.io/langgraph/concepts/high_level/) - [Building Agent Workflows — LangChain Blog](https://blog.langchain.dev/)