claude

evaluation

Evaluate agentic experiences: behaviour evals, trust calibration, failure analysis.

3,339 on GitHubby MC Deanv1.0.0MITupdated 2 months agoai
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Plugin evaluation from marketplace designer-skills in Owl-Listener/designer-skills. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (7)

  • comparative-evaluation

    A/B testing, side-by-side comparison, and preference ranking for AI outputs.

  • failure-taxonomy

    Classifying AI failures — hallucination, refusal, irrelevance, tone mismatch, latency.

  • heuristic-evaluation-ai

    Adapting Nielsen's heuristics and new AI-specific heuristics for AI interfaces.

  • longitudinal-measurement

    Tracking AI product quality over time — drift, degradation, and improvement.

  • output-quality-rubrics

    Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.

  • task-success-metrics

    Measuring whether the AI actually helped users accomplish their goals.

  • user-satisfaction-signals

    Interpreting implicit and explicit feedback — edits, regenerations, abandonment.

Slash commands (3)

  • /evaluation:create-rubric

    Build a scoring rubric for evaluating AI output quality.

  • /evaluation:design-benchmark

    Design a benchmark suite to measure AI product performance over time.

  • /evaluation:run-evaluation

    Execute a structured evaluation of an AI feature against defined criteria.

View source on GitHub

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