model-evaluation
SkillGenerates python code that evaluates SageMaker models. Supports two evaluation types: LLM-as-Judge and Custom Scorer. Use when the user says "evaluate my model", "run a benchmark", "test model performance", "how did my model perform", "compare models", or other similar requests.
Install
git clone https://github.com/awslabs/agent-plugins.git ~/.claude/skills/model-evaluationWhat is model-evaluation?
Generates python code that evaluates SageMaker models. Supports two evaluation types: LLM-as-Judge and Custom Scorer. Use when the user says "evaluate my model", "run a benchmark", "test model performance", "how did my model perform", "compare models", or other similar requests.
What this can do
Capabilities declared in this component's own frontmatter — not inferred.
Inherit all session tools
Declares no tool restrictions — inherits every session tool
~70 tokens of context used while enabled, before you invoke anything
Documentation
README · ~3 min readModel Evaluation
Generate code that evaluates a SageMaker model.
Prerequisites
- The SDK environment has been verified (SDK version, region, execution role). If not done, activate the
sdk-getting-startedskill first.
Principles
- One thing at a time. Each response advances exactly one decision. Never combine multiple questions in a single turn.
- Confirm before proceeding. Wait for the user to agree before moving to the next step.
- Don't read files until you need them. Only read reference files when you've reached the step that requires them.
- Don't ask what you already know. If the answer is in conversation history, workflow_state.json, plan.md, or any file you've already read — use it. Confirm if unsure, but don't re-ask.
- No narration. Share outcomes and ask questions. Keep responses short.
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