No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by mehrandvd · MCP Server · ★ 180
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is skunit safe to install? View the security audit →
skUnit skUnit is a semantic testing framework for .NET that makes it easy to test AI-powered applications using simple, readable Markdown scenarios. Test anything that talks to AI: IChatClient implementations (Azure OpenAI, OpenAI, Anthropic, etc.) SemanticKernel applications and plugins MCP (Model Context Protocol) servers Custom AI integrations Write your tests in Markdown, run them with any test framework (xUnit, NUnit, MSTest), and get live, readable results. Quick Start Here's a simple test scenario in Markdown: And here's how to test it with just a few lines of C#: csharp [Fact] public async Task SimpleTest() { var markdown = File.ReadAllText("mountain-chat.md"); var scenarios = ChatScenario.LoadFromText(markdown); await ScenarioRun
| Stars | 180 |
| Forks | 21 |
| Language | C# |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.1610085397925/100 |
| Open Issues | 4 |
| Last Updated | 2026-07-26 |
| Created | 2023-12-18 |
| Platforms | cli, dotnet, mcp |
| Est. Tokens | ~20k |
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skunit is skUnit is a testing tool for AI units, such as IChatClient, MCP Servers and agents.. It is categorized as a MCP Server with 180 GitHub stars.
skunit is primarily written in C#. It covers topics such as agent, agentic-ai, csharp.
You can find installation instructions and usage details in the skunit GitHub repository at github.com/mehrandvd/skunit. The project has 180 stars and 21 forks, indicating an active community.
skunit is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to skunit on Agent Skills Hub include oreilly-ai-agents, octocode-mcp, mcpcan. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.