skunit: Testing tool for AI units, such as IChatClient, MCP Servers and agents

by mehrandvd · MCP Server · ★ 180

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 68/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentagentic-aicsharpdotnetgpt-pluginsmcpmodelcontextprotocolopenaisemantic-kerneltest

Quick Facts

Stars180
Forks21
LanguageC#
CategoryMCP Server
LicenseMIT
Quality Score68/100
Open Issues4
Last Updated2026-07-26
Created2023-12-18
Platformscli, dotnet, mcp
Est. Tokens~20k

About skunit

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...

Compatible Skills

These tools work well together with skunit for enhanced workflows:

  • Agent-ChatRoom — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • qaskills — semantic(0.25)+complementary+rare_topics+similar_pop (48%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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