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model-evaluation-reporting | machine-learning-ops

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machine-learning-ops
model-evaluation-reporting

Skill

model-evaluation-reporting

From machine-learning-ops

Standardize the reporting of model metrics to ensure statistical rigor and business relevance.

Popularity

Parent stars

5

Parent forks

1

Invocation

How this skill is triggered — by the user, by Claude, or both

Slash command

/machine-learning-ops:model-evaluation-reporting

User invocable

Model invocable

Inline context

Default effort

Context Preview

The summary Claude sees in its skill listing — used to decide when to auto-load this skill

Raw accuracy metrics are not enough. Evaluation must reflect the actual business impact and failure modes of the model.

SKILL.md

15 lines · ~231 tokens

Stats

LanguageTypeScript

Parent stars5

Parent forks1

MaintenanceExcellent

Last CommitMay 6, 2026

Actions

View Source View Plugin View on GitHub View README

Stats

LanguageTypeScript

Parent stars5

Parent forks1

MaintenanceExcellent

Last CommitMay 6, 2026

Actions

View Source View Plugin View on GitHub View README

Model Evaluation Reporting

Raw accuracy metrics are not enough. Evaluation must reflect the actual business impact and failure modes of the model.

Reporting Standards

Beyond Accuracy: Demand the Confusion Matrix. Demand Precision, Recall, and F1. Explain the cost of a False Positive vs. a False Negative in the business context.
Slice Analysis: Report performance on key segments. A model might be 95% accurate overall, but only 40% accurate on new users.
Calibration: If the model outputs probabilities, verify if they are calibrated. A prediction of 0.8 should mean it happens 80% of the time.
Action: Format the output as a Markdown report that a non-technical stakeholder can read, highlighting trade-offs and worst-case scenarios.

$

npx claudepluginhub yeaight7/agent-powerups --plugin machine-learning-ops

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