evaluations
Use when evaluating AI model outputs, prompts, and LLM application quality in .NET. Covers Microsoft.Extensions.AI.Evaluation for scoring, reporting, and automated test pipelines. USE FOR: evaluating LLM response quality, measuring prompt effectiveness, automated AI output scoring, building evaluation pipelines for chat applications, comparing model performance across providers DO NOT USE FOR: unit testing non-AI code (use xunit/nunit), load testing HTTP APIs (use k6 or NBomber), monitoring production metrics (use OpenTelemetry), training ML models (use mlnet)
npx skills add majiayu000/claude-skill-registry --skill evaluations --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AI Evaluations ## Overview Microsoft.Extensions.AI.Evaluation provides a structured framework for scoring and reporting on the quality of AI model outputs in .NET applications. It includes built-in evaluators for relevance, coherence, fluency, and groundedness, as well as support for custom evaluators. The framework integrates with `IChatClient` from Microsoft.Extensions.AI to evaluate chat-based interactions end-to-end. ## NuGet Packages ```bash dotnet add package Microsoft.Extensions.AI.Evaluation dotnet add package Microsoft.Extensions.AI.Evaluation.Quality dotnet add package Microsoft.Extensions.AI.Evaluation.Reporting dotnet add package Microsoft.Extensions.AI ``` ## Built-in Evaluators The Quality package provides pre-built evaluators that use an LLM as a judge to score responses. ```csharp using Microsoft.Extensions.AI; using Microsoft.Extensions.AI.Evaluation; using Microsoft.Extensions.AI.Evaluation.Quality; // Create an evaluator that scores on multiple quality dimensions var evaluators = new IEvaluator[] { new RelevanceEvaluator(), new CoherenceEvaluator(), new FluencyEvaluator(), new GroundednessEvaluator() }; var chatClient = new ChatCompletionsClient(endpoint, crede
- Overview
- NuGet Packages
- Built-in Evaluators
- Evaluation Result Scores
- Custom Evaluators
- Evaluation Reporting
- Integration with Test Frameworks
- Batch Evaluation Pipeline
- Best Practices
dotnet add package Microsoft.Extensions.AI.Evaluation dotnet add package Microsoft.Extensions.AI.Evaluation.Quality dotnet add package Microsoft.Extensions.AI.Evaluation.Reporting dotnet add package Microsoft.Extensions.AI
What does the evaluations skill do?
Use when evaluating AI model outputs, prompts, and LLM application quality in .NET. Covers Microsoft.Extensions.AI.Evaluation for scoring, reporting, and automated test pipelines. USE FOR: evaluating LLM response quality, measuring prompt effectiveness, automated AI output scoring, building evaluation pipelines for chat applications, comparing model performance across providers DO NOT USE FOR: unit testing non-AI code (use xunit/nunit), load testing HTTP APIs (use k6 or NBomber), monitoring production metrics (use OpenTelemetry), training ML models (use mlnet)
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill evaluations --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.