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Evaluating a recommender: metrics and protocol v1.0.0
Use this resource to evaluate a recommendation system with ranking metrics
New
- 360 downloads
- 205 likes
- 170 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install recommender-metrics-evaluationWith 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 loginThe agent defines what counts as relevant, splits the data without leakage, computes metrics such as NDCG@k, MAP@k or coverage, then compares against baselines. You get an evaluation protocol and a verified report section.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: protocol steps, from defining relevance to verification
- SKILL.md: formulas for ranking and diversity metrics
- SKILL.md: reference implementations in JavaScript
Good fit for
- You need to report the accuracy of a product recommender
- You are comparing two algorithms and want a fair table
Not for
Tags
- recommendation
- metrics
- ndcg
- evaluation
About
You say
“Evaluate my movie recommender: NDCG@10 and MAP@10 against a popularity baseline.”
Skill published by practicalswan under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to evaluate a recommendation system with ranking metrics
- Review and adjust the result
Content preview
Files
- CHANGELOG.md4.3 KB
- LICENSE1 KB
- NOTICE-PALATEKIT.md0.5 KB
- SKILL.md10.1 KB
Origin and licence
Listed unmodified
- Author
- practicalswan
- Source
- github.com/practicalswan/agent-skills/tree/6cf63517700aa9ba0b80e711a93837e3bea13d0a/recommender-evaluation
- Licence
- MIT
Notice
« recommender-evaluation » (practicalswan, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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