Model Evaluation Benchmark

Name: Model Evaluation Benchmark
Author: rysweet

Checked

rysweet·vlatest·ai-ml

Rysweet's model-evaluation-benchmark provides standardized metrics and datasets to assess and compare AI model performance objectively.

Repository

Install on your platform

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Run this in your project

terminal

$ npx skills add rysweet/amplihack --skill model-evaluation-benchmark -a claude-code

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What we know about this skill

Publisher

rysweet

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Install target

Live repository

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Automated safety checks

scanned 28 Sept 2026

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
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Pinned to a commit: passed
Scanned rysweet/amplihack at 84424457d5.
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Commit: 84424457d5
Manifest: .claude/skills/model-evaluation-benchmark/SKILL.md
Scanned: 2 files, 206 lines
Copies in repo: 2 — all of them scanned

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About this skill

The model-evaluation-benchmark skill provides a structured framework for assessing AI model performance against standardized metrics and datasets. It enables agents to systematically compare outputs, identify weaknesses, and validate reliability across diverse tasks. This tool is essential for developers needing objective data on how well their models handle specific scenarios before deployment.

When to use it

Before deploying a new model in production to verify it meets required accuracy thresholds.
When comparing multiple candidate models to select the one with the best balance of speed and precision.
During iterative development cycles to track performance improvements after fine-tuning or architecture changes.
To generate compliance reports demonstrating that an AI system adheres to industry-specific evaluation standards.

Key capabilities

Executes predefined benchmark tests against a model's outputs.
Calculates quantitative scores based on accuracy, latency, and robustness metrics.
Generates comparative analysis reports highlighting strengths and failure modes.
Supports integration with various dataset formats for flexible testing environments.

Example prompts

"Run the standard safety evaluation benchmark on my latest chatbot version and summarize the results."
"Compare Model A and Model B using the performance benchmark suite, focusing on hallucination rates."
"Execute a stress test benchmark to measure how this model handles high-volume input queries."

Tips & gotchas

Ensure your target model is fully loaded and accessible before initiating benchmarks to avoid timeout errors. Some benchmarks may require specific API keys or dataset licenses; verify prerequisites before running automated tests.