# LangGraph Skill Pack > Source: > Published: 2026-07-26 00:00:00+00:00 · Agentic AI · 5 min read ### 📋 Prerequisites - AWS Skill Pack (previous lesson) ### 🎯 What You'll Learn - Build a Workflow skill that scaffolds a LangGraph StateGraph from a described flow - Build a Validator skill that catches common LangGraph structural mistakes - Recognize the parallel between a multi-agent skill system and a LangGraph graph ## What This Pack Covers Two skills for teams building agents with LangGraph, rather than just using pre-built ones: one scaffolds a new `StateGraph` from a plain-language description of a flow, and one reviews an existing graph for common structural mistakes. This pack is a slightly different kind of skill than the rest of this course — the “user” is a developer building an agent, and the domain knowledge being packaged is about agent construction itself, which makes it worth noticing how directly the [multi-agent design](/courses/production-agent-skills-engineering/multi-agent-skill-systems) ideas from the companion course map onto LangGraph’s actual API. ## Skill 1: `langgraph-scaffold` ``` --- name: langgraph-scaffold description: Scaffolds a LangGraph StateGraph from a plain-language description of an agent's flow — nodes, edges, and conditional routing. Use when the user describes an agent workflow and asks to build, scaffold, or set up a LangGraph graph for it. metadata: version: "1.0.0" --- ## Generate the scaffold 1. From the description, identify each distinct step as a node — the same node-identification discipline as picking design patterns: one node should do one job, not several. 2. Identify whether the flow is linear (fixed sequence) or branches (the next node depends on the previous node's output). A linear flow uses `add_edge`; a branching flow needs `add_conditional_edges` with a routing function. 3. Generate the scaffold: \`\`\` python from langgraph.graph import StateGraph, END from typing import TypedDict class AgentState(TypedDict): # TODO: define the fields this graph actually needs to carry pass graph = StateGraph(AgentState) # TODO: implement each node function below graph.add_node("research", research_node) graph.add_node("draft", draft_node) graph.add_node("review", review_node) graph.set_entry_point("research") graph.add_edge("research", "draft") graph.add_conditional_edges( "review", route_after_review, # TODO: implement — returns next node name {"revise": "draft", "done": END} ) graph.add_edge("draft", "review") app = graph.compile() \`\`\` 4. Leave every node function and routing function as a `TODO` — this skill's job is the graph's shape, not the logic inside each node, which needs the same human judgment the free course's capstone [transformation-skill guidance](/courses/agent-skills-mastery/agent-skills-capstone) applies to any generated scaffold. 5. Always include an explicit path to `END` — a graph with no reachable `END` will run indefinitely on any input that hits that path. ``` **Pattern:** Workflow, generating consistent structure — and notice the parallel: a LangGraph node is close kin to an agent *role* from [Multi-Agent Skill Systems](/courses/production-agent-skills-engineering/multi-agent-skill-systems) — a research node, a draft node, a review node map directly onto researcher, executor, and reviewer roles, just expressed as graph nodes instead of separate agents. ## Skill 2: `langgraph-graph-review` ``` --- name: langgraph-graph-review description: Reviews a LangGraph StateGraph definition for structural mistakes — unreachable nodes, missing END paths, and state schema issues. Use when reviewing LangGraph code, debugging a graph that won't terminate, or before shipping a new graph. metadata: version: "1.0.0" --- ## Review checklist 1. **Unreachable nodes.** Every node added via `add_node` should have at least one incoming edge (or be the entry point). Flag any node that's defined but never targeted by `add_edge` or `add_conditional_edges`. 2. **Missing END path.** Trace every path from the entry point. Flag any path that has no way to reach `END` — this is the most common cause of a graph that runs forever or hits a recursion limit. 3. **Conditional edges with incomplete routing.** For every `add_conditional_edges` call, check that the routing function's possible return values all appear as keys in the routing dict. A routing function that can return a value with no matching edge will fail at runtime, not at graph-definition time — which makes this easy to miss without a specific check for it. 4. **State schema drift.** If a node function reads or writes a state key not declared in the `TypedDict` (or equivalent) schema, flag it — this works today because Python doesn't enforce it, but it's a latent bug waiting for someone to rename a field elsewhere. Report findings with the specific node or edge involved, not a general "the graph has issues" summary. ``` **Pattern:** Validator, applied to a domain where several of the most serious mistakes (no `END` reachable, an unhandled conditional routing value) are silent at definition time and only surface as a runtime failure or an infinite loop — exactly the kind of thing worth a dedicated review pass rather than trusting it’ll be caught by running the graph once and having it happen to hit the working path. ## Testing Both Skills For `langgraph-scaffold` , test against a purely linear description (“first do X, then Y, then Z”) and a description that clearly branches (“check the result, and if it fails, try again”) — confirm the skill correctly picks `add_edge` versus `add_conditional_edges` rather than defaulting to one regardless of the description. For `langgraph-graph-review` , the most valuable test case is a graph with a routing function that can return a value not covered by any edge — this is the failure mode most likely to slip through a casual code review, since it’s invisible until that specific branch actually executes at runtime. ## Summary `langgraph-scaffold` is a Workflow skill turning a plain-language flow description into`StateGraph` structure, deliberately leaving node and routing logic as`TODO` s for a human to implement`langgraph-graph-review` is a Validator catching structural mistakes that are silent at definition time — unreachable nodes, missing`END` paths, incomplete conditional routing, state schema drift- LangGraph nodes map directly onto the agent-role thinking from [Multi-Agent Skill Systems](/courses/production-agent-skills-engineering/multi-agent-skill-systems)— the same design vocabulary, expressed as graph structure instead of separate agents - The highest-value test case for the review skill is a routing function with an uncovered return value, since that failure mode is invisible until runtime Next, one deep enterprise domain pack — banking and financial services, where regulatory and compliance concerns shape almost every skill decision.