Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/managedcode/dotnet-skills/declarativegit clone --depth 1 https://github.com/managedcode/dotnet-skillsWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00014 | $0.01612 |
| Opus 5 | $0.00007 | $0.00806 |
| Sonnet 5 | $0.00003 | $0.00322 |
| Haiku 4.5 | $0.00001 | $0.00161 |
Grade A, and why
declarative 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Declarative Agents
Declarative agents allow you to define agent configuration using YAML or JSON files instead of writing programmatic code. This approach makes agents easier to define, modify, and share across teams.
:::zone pivot="programming-language-csharp"
Prerequisites
To use declarative agents in C#, add the Microsoft.Agents.AI.Declarative NuGet package to your project, alongside the chat client package for your provider (for example, Azure.AI.OpenAI):
dotnet add package Microsoft.Agents.AI.Declarative --prerelease
dotnet add package Azure.AI.OpenAI
dotnet add package Azure.Identity
The Microsoft.Agents.AI.Declarative package provides the ChatClientPromptAgentFactory type and the CreateFromYamlAsync extension method on PromptAgentFactory used in the examples below.
Define an agent inline with YAML
You can define the full YAML specification as a string directly in your code, then create an AIAgent from it with ChatClientPromptAgentFactory:
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// Create the chat client
IChatClient chatClient = new AzureOpenAIClient(
new Uri(endpoint),
new DefaultAzureCredential())
.GetChatClient(deploymentName)
.AsIChatClient();
// Define the agent using a YAML definition.
var yamlDefinition =
"""
kind: Prompt
name: Assistant
description: Helpful assistant
instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format.
model:
options:
temperature: 0.9
topP: 0.95
outputSchema:
properties:
language:
type: string
required: true
description: The language of the answer.
answer:
type: string
required: true
description: The answer text.
""";
// Create the agent from the YAML definition.
var agentFactory = new ChatClientPromptAgentFactory(chatClient);
var agent = await agentFactory.CreateFromYamlAsync(yamlDefinition);
// Invoke the agent and output the text result.
Console.WriteLine(await agent!.RunAsync("Tell me a joke about a pirate in English."));
// Invoke the agent with streaming support.
await foreach (var update in agent!.RunStreamingAsync("Tell me a joke about a pirate in French."))
{
Console.WriteLine(update);
}
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.
- 2d ago First seen · 218 lines · 14 tokens per session scan A c867a59caf2c
declarative is an agent published in the GitHub repository managedcode/dotnet-skills (477 stars, last pushed 2d ago), licensed MIT. It adds 14 tokens to every session and 1,612 once invoked, about $0.0001 per session on Opus 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.
Other agents, from other repositories
RPI Implementor
Implementation subagent for the RPI Orchestrator. Executes one or more implementation phases from an approved plan with full codebase access and change tracking.
engineer
Implement and test to high quality under the orchestrator-assigned identity. Full subagent.
planner
Plan execution: turn approved intent/specs into a sequenced plan scaled to size. Full subagent.
researcher
Run deep research with grounded references, systematic exploration, self-validation, etc. Full subagent.
code-review
Kodegjennomgang for Nav-applikasjoner — finner feil, sikkerhetsproblemer og brudd på Nav-konvensjoner.
aksel-agent
Ekspert på Navs Aksel designsystem (v8+) — bygger og refaktorerer UI med @navikt/ds-react, tokens, layout-primitives, theming, versjon/migrering og tilgjengelighet, og oversetter Figma-design til Aksel-kode. Drevet av aksel-builder-skillen og Aksel MCP som fasit.