accordant-overview

accordant-overview is a skill for Claude Code, Codex from microsoft/accordant. It costs 33 tokens per session (963 once invoked), scanned A, original, MIT.

An introduction to Accordant, a .NET model-based testing framework. You describe expected system behavior as executable code, and it generates tests for stateful systems and sequences of operations.

In plain words
What is it for?
Use it to learn Accordant's core ideas, define system state and operations, test operation sequences, and decide when model-based testing fits.
Why use it?
It gives a single behavioral reference for systems whose operations affect shared state, such as databases, caches, queues, or sessions. This can expose bugs that isolated tests overlook.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one.

Good fit Use it to learn Accordant's core ideas, define system state and operations, test operation sequences, and decide when model-based testing fits.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/microsoft/accordant/overview
About the project

Accordant is a .NET framework that turns executable behavioral specifications into generated tests for checking software implementations. It helps developers verify that real systems follow defined rules across many operations and state changes.

microsoft/accordant · 69 stars · on GitHub · microsoft.github.io

Install

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.

Any agent
npx skills add microsoft/accordant --skill overview
Clone the repo
git clone --depth 1 https://github.com/microsoft/accordant

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 accordant-overview

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/accordant/overview/github.svg)](https://agentmods.dev/skills/microsoft/accordant/overview)
Your own site
<a href="https://agentmods.dev/skills/microsoft/accordant/overview"><img src="https://agentmods.dev/badge/skills/microsoft/accordant/overview/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 accordant-overview

Your own site · 80×15
<a href="https://agentmods.dev/skills/microsoft/accordant/overview"><img src="https://agentmods.dev/badge/skills/microsoft/accordant/overview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 963 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00033 $0.00963
Opus 5.5 $0.00013 $0.00385
Sonnet 5.5 $0.00007 $0.00193
Haiku 4.5 $0.00003 $0.00096

Measured 1mo ago against content hash e57696d7dea3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

accordant-overview 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.

agent/skills/overview/SKILL.md · 122 lines

How it starts

The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Accordant Overview

Accordant is a model-based testing framework for .NET. You write a spec — executable code that captures what your system should do — and Accordant generates tests, validates responses, and finds bugs.

When to Use Accordant

Accordant excels when:

  • Your system is stateful (databases, caches, queues, sessions)
  • You have many operations that interact with shared state
  • You want to test operation sequences (not just individual calls)
  • You need to find race conditions in concurrent code
  • You want a single source of truth for behavior (not scattered assertions)

Core Concepts

The Spec

A spec is executable code defining what your system should do:

var spec = new Spec<BankState>();

spec.Operation<string, ApiResult<decimal>>("CreateAccount", (accountId, state) =>
{
    if (state.Accounts.ContainsKey(accountId))
        return Expect.That<ApiResult<decimal>>(r => r.IsConflict).SameState();

    return Expect.That<ApiResult<decimal>>(r => r.IsSuccess && r.Data == 0)
           .ThenState<BankState>(s => s.Accounts[accountId] = 0);
});

State

State is what the system "remembers" between operations. Keep it minimal — only what's needed to define correct behavior:

[State]
public partial class BankState
{
    public Dictionary<string, decimal> Accounts { get; set; } = new();
}

Operations

An operation represents an atomic action — API call, method invocation, command. Each operation has:

  • Apply: What should happen (the spec logic)
  • Execute: What actually happens (calls the real system)

Test Generation

Accordant explores operation sequences by simulating the spec, then runs generated tests against your real system:

var testCases = spec.GenerateTests(initialState, inputs, options);
var results = await spec.RunTests(context, initialState, testCases, executionOptions);

The Typical Workflow

  1. Define State — What does the system track?
  2. Define Operations — What can happen to that state?
  3. Bind to Real System — Connect spec operations to actual API calls
  4. Generate & Run Tests — Let Accordant explore and validate

Read the full file on GitHub · 122 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 · 122 lines · 33 tokens per session scan A e57696d7dea3

Subscribe to this mod's changes

accordant-overview is a skill published in the GitHub repository microsoft/accordant (69 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 963 once invoked, about $0.0001 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

Other skills, from other repositories

implementing-api-security-testing-with-42crunch

Implement comprehensive API security testing using the 42Crunch platform to perform static audit and dynamic conformance scanning of OpenAPI specifications.

xalgorix/xalgorix · 36 tokens

API Test Suite Generator

Automatically generate comprehensive API test suites from OpenAPI specifications covering CRUD operations, error handling, authentication, pagination, and edge cases.

PramodDutta/qaskills · 29 tokens

AI Test Generation Patterns

Systematic patterns for prompting AI coding agents to generate high-quality tests including prompt engineering for test creation, coverage-driven generation, mutation-aware testing, and review checklists for AI-generated test code.

PramodDutta/qaskills · 43 tokens

Cursor AI Testing Patterns

Effective test automation patterns with Cursor AI IDE including Composer for test suite generation, Cmd+K for inline test edits, Chat for test debugging, codebase-aware test generation, and rules configuration for testing conventions.

PramodDutta/qaskills · 46 tokens

GitHub Copilot Testing Patterns

Effective patterns for using GitHub Copilot to generate, refactor, and maintain test code including prompt engineering for test generation, Copilot Chat for debugging, inline suggestions for assertions, and workspace context optimization.

PramodDutta/qaskills · 48 tokens

e2e-test-writer

Generate end-to-end tests for web applications using Playwright. Create test files from user flow descriptions, page snapshots, or existing page analysis.

JansenAnalytics/claudex · 35 tokens