Story Diagnostic Agent

Uses Semantic Kernel and an LLM to analyze a story outline and produce a diagnostic report identifying structural issues. This is the first of two samples that demonstrate AI-powered story analysis.

[!NOTE] This sample requires an OpenAI API key. Set the OPENAI_API_KEY environment variable before running.

View source on GitHub

What It Demonstrates

The sample creates an outline with intentional structural weaknesses: a passive protagonist with no goals or motives, front-loaded action with filler scenes at the end, missing reversals and turning points, and unresolved plot threads. It then serializes every element via GetElement and sends the full outline to an LLM along with a system prompt explaining StoryCAD concepts.

The LLM returns a diagnostic report identifying issues like pacing problems, passive characters, and missing story beats. This demonstrates how to combine StoryCADLib’s data extraction APIs with Semantic Kernel’s chat completion to build AI-powered analysis tools.

The key pattern is straightforward: extract structured data from the story graph, format it as context for the LLM, and let the model apply narrative craft knowledge that would be impractical to encode as rules.

API Methods Used

Method	Purpose
CreateEmptyOutline	Create a new story from template
AddElement	Add elements with properties
UpdateElementProperties	Set protagonist, antagonist, outcomes
AddCastMember	Link characters to scenes
GetAllElements	List all elements for serialization
GetElement	Get full serialized data for each element

Semantic Kernel Components

Component	Purpose
Kernel.CreateBuilder().AddOpenAIChatCompletion()	Configure SK with OpenAI
IChatCompletionService	Single-shot chat completion
ChatHistory	System + user message for structured prompting

How to Run

export OPENAI_API_KEY="your-key-here"

cd samples/StoryDiagnosticAgent
dotnet build -f net10.0-desktop
dotnet run -f net10.0-desktop

You can optionally set the model (defaults to gpt-4o-mini):

export OPENAI_MODEL="gpt-4o-mini"