PrompterOne: Agent for Claude Code

.codex/skills/dotnet-microsoft-agent-framework/references/official-docs/tutorials/agents/structured-output.md

structured-output is an agent for Claude Code, Codex from managedcode/PrompterOne. It costs 10 tokens per session (1,686 once invoked), scanned A, original, MIT.

A tutorial showing how to configure an AI agent to return structured output instead of free-form text. Structured output follows a defined response format, such as fields that a program can read.

In plain words
What is it for?
Use it when building a C# agent that must return data in a specified response format.
Why use it?
It helps developers make agent responses predictable for software to process. The example uses a C# ChatClientAgent with Azure OpenAI Chat Completion support.

Agent for Claude CodeCodex

Written for Codex and Claude Code: installed under .codex/, but also a Claude Code subagent (agents/*.md).

This is managedcode/PrompterOne's own configuration. It tells Claude Code and Codex how to work on PrompterOne itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything PrompterOne configures →

Reuse

Borrowing it

Nothing to install: this file belongs to managedcode/PrompterOne. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/managedcode/PrompterOne/main/.codex/skills/dotnet-microsoft-agent-framework/references/official-docs/tutorials/agents/structured-output.md
Clone the repo
git clone --depth 1 https://github.com/managedcode/PrompterOne

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for structured-output

README.md
[![agentmods](https://agentmods.dev/badge/agents/managedcode/prompterone/structured-output/github.svg)](https://agentmods.dev/agents/managedcode/prompterone/structured-output)
Your own site
<a href="https://agentmods.dev/agents/managedcode/prompterone/structured-output"><img src="https://agentmods.dev/badge/agents/managedcode/prompterone/structured-output/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for structured-output

Your own site · 80×15
<a href="https://agentmods.dev/agents/managedcode/prompterone/structured-output"><img src="https://agentmods.dev/badge/agents/managedcode/prompterone/structured-output.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 10 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,686 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00010 $0.01686
Opus 5.5 $0.00004 $0.00674
Sonnet 5.5 $0.00002 $0.00337
Haiku 4.5 $0.00001 $0.00169

Measured 1mo ago against content hash 8e3d571a9af0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

structured-output scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.codex/skills/dotnet-microsoft-agent-framework/references/official-docs/tutorials/agents/structured-output.md · 204 lines

How it starts

The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Producing Structured Output with Agents

::: zone pivot="programming-language-csharp"

This tutorial step shows you how to produce structured output with an agent, where the agent is built on the Azure OpenAI Chat Completion service.

[!IMPORTANT] Not all agent types support structured output. This step uses a ChatClientAgent, which does support structured output.

Prerequisites

For prerequisites and installing NuGet packages, see the Create and run a simple agent step in this tutorial.

Create the agent with structured output

The ChatClientAgent is built on top of any xref:Microsoft.Extensions.AI.IChatClient implementation. The ChatClientAgent uses the support for structured output that's provided by the underlying chat client.

When creating the agent, you have the option to provide the default xref:Microsoft.Extensions.AI.ChatOptions instance to use for the underlying chat client. This ChatOptions instance allows you to pick a preferred xref:Microsoft.Extensions.AI.ChatResponseFormat.

Various options for ResponseFormat are available:

This example creates an agent that produces structured output in the form of a JSON object that conforms to a specific schema.

The easiest way to produce the schema is to define a type that represents the structure of the output you want from the agent, and then use the AIJsonUtilities.CreateJsonSchema method to create a schema from the type.

using System.Text.Json;
using System.Text.Json.Serialization;
using Microsoft.Extensions.AI;

public class PersonInfo
{
    public string? Name { get; set; }
    public int? Age { get; set; }
    public string? Occupation { get; set; }
}

JsonElement schema = AIJsonUtilities.CreateJsonSchema(typeof(PersonInfo));

Read the full file on GitHub · 204 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 204 lines · 10 tokens per session scan A 8e3d571a9af0

Subscribe to this mod's changes

structured-output is an agent published in the GitHub repository managedcode/PrompterOne (45 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 1,686 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

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