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Model performance evaluation v1.0.0

Use this resource to evaluate a model's performance and detect drift

New

  • 351 downloads
  • 127 likes
  • 141 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install model-performance-evaluation

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent runs a static analysis of LLM calls and prompts, detects the ML environment, compares metrics to the baseline and checks for data drift. You get a diagnosis and the points to fix.

Mostly picked by

Updated November 2, 2026

Card for the “Model performance evaluation” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md: static analysis, comparison and drift check steps
  • .claude-plugin/plugin.json: plugin metadata

Good fit for

  • A model's accuracy dropped since the last deployment
  • You want to check the prompt quality of an LLM application

Not for

    Tags

    • evaluation
    • drift
    • model
    • llm

    About

    You say

    “Evaluate our recommendation model: has accuracy dropped since last month?”

    Card for the “Model performance evaluation” skill

    Skill published by tonone-ai <hello@tonone.ai> under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to evaluate a model's performance and detect drift
    3. Review and adjust the result

    Content preview

    Files

    • .claude-plugin/plugin.json0.5 KB
    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • SKILL.md6.7 KB

    Origin and licence

    Listed unmodified

    Author
    tonone-ai <hello@tonone.ai>
    Source
    github.com/jeremylongshore/tons-of-skills-marketplace/tree/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/ai-agency/tonone/skills/cortex-eval
    Licence
    MIT

    Notice

    « cortex-eval » (tonone-ai <hello@tonone.ai>, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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