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Evaluate ai quality monitoring

Skill alexe-ev/product-plugins/ai-product/skills/evaluate-ai-quality-monitoring

Skill library for AI agents — 15 product domains, 121 skills. Tells the agent what to ask, how to reason, and what to output.

Install
npx -y skills add alexe-ev/product-plugins --skill evaluate-ai-quality-monitoring

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Define quality criteria for AI features and set up monitoring, evaluation, and iteration loops. Use this skill when a team needs to ensure their AI feature maintains acceptable quality in production.

SKILL.md

2.5 KB, 447 tokens by cl100k_base, as published. Nobody here has run it

Evaluate AI Quality & Monitoring

Purpose

Help teams define quality standards for AI-powered features and design monitoring and evaluation systems that detect degradation and support continuous improvement.

Skill type

Conceptual skill

Use this skill when

  • An AI feature is going into production and needs quality monitoring
  • AI output quality has degraded and needs diagnosis
  • Quality criteria for an AI feature are undefined
  • An evaluation framework is needed for iterating on AI features

Do not use this skill when

  • The goal is model selection (use assess-model-capabilities)
  • The goal is AI feature ideation (use ideate-ai-features)

Required inputs

  • AI feature description and expected output
  • User-facing quality implications (what does "bad" look like to users?)

Optional inputs

  • Model being used
  • Current quality data or error samples
  • Regulatory or compliance requirements
  • Evaluation dataset availability

Upstream context

Works best when:

  • AI feature is deployed or in late-stage development
  • Human-in-the-loop design is defined

Downstream handoff

Output can feed:

  • detect-performance-signals (AI quality signals feed product monitoring)
  • design-experiment-plan (improve AI quality through experiments)

Instructions

  1. Define the quality dimensions relevant to this AI feature (accuracy, relevance, safety, tone, etc.).
  2. Define quality thresholds: acceptable / needs review / unacceptable.
  3. Design evaluation methodology: human eval, automated metrics, golden set.
  4. Design monitoring: what signals indicate quality degradation?
  5. Define alerting and escalation for quality failures.
  6. Design the iteration loop: how do quality findings drive model/prompt/data improvements?

Output

Provide:

  • Quality dimensions and definitions
  • Quality thresholds
  • Evaluation methodology
  • Monitoring plan (signals, frequency, tooling)
  • Alerting and escalation design
  • Iteration loop: from finding to fix
  • Open questions and risks

Risks / caveats

  • Automated metrics rarely capture full quality — always include human evaluation
  • Quality thresholds that are never enforced create false confidence
  • AI quality degrades over time as the world changes — monitoring must be continuous

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