LangGraph Skill Pack

LangGraph Skill Pack introduces two skills for developers building agents with LangGraph: langgraph-scaffold, which generates a StateGraph from a plain-language description, and langgraph-graph-review, which checks for structural mistakes like unreachable nodes and missing END paths. The pack, part of a production agent skills engineering course, emphasizes the parallel between multi-agent skill systems and LangGraph's graph API.

· 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.