sagemaker-endpoint-deployer
Sagemaker Endpoint Deployer - Auto-activating skill for ML Deployment. Triggers on: sagemaker endpoint deployer, sagemaker endpoint deployer Part of the ML Deployment skill category.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill sagemaker-endpoint-deployerOverall
score
19%
Does it follow best practices?
Validation for skill structure
Activation
7%This description is severely underdeveloped, essentially only providing the skill name without any meaningful content. It lacks concrete actions, useful trigger terms, and explicit guidance on when to use it. The redundant trigger terms and category label add no value for skill selection.
Suggestions
Add specific capabilities like 'Deploy ML models to SageMaker endpoints, configure auto-scaling, manage inference containers, and monitor endpoint health'
Include a proper 'Use when...' clause with natural triggers: 'Use when deploying models to AWS, creating inference endpoints, setting up real-time predictions, or managing SageMaker infrastructure'
Add natural keyword variations users would say: 'deploy model', 'ML endpoint', 'inference API', 'model hosting', 'AWS deployment', '.tar.gz model artifacts'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the tool ('Sagemaker Endpoint Deployer') without describing any concrete actions. There are no specific capabilities listed like 'deploy models', 'configure endpoints', or 'manage scaling'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and the 'when' clause only restates the skill name rather than describing actual use cases or scenarios. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant ('sagemaker endpoint deployer' listed twice) and only cover the exact skill name. Missing natural variations users would say like 'deploy model', 'ML endpoint', 'inference endpoint', 'deploy to AWS', or 'model serving'. | 1 / 3 |
Distinctiveness Conflict Risk | The SageMaker-specific naming provides some distinctiveness from generic deployment skills, but the lack of specific capabilities means it could conflict with other AWS or ML deployment skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%This skill content is essentially a placeholder template with no actual instructional value. It contains only meta-descriptions of what a skill should do rather than any concrete guidance on deploying SageMaker endpoints. The content fails on all dimensions by providing zero actionable information, no code examples, and no workflow for a complex multi-step deployment process.
Suggestions
Add executable Python code examples using boto3 or SageMaker SDK for creating and deploying endpoints (e.g., `sagemaker.Model().deploy()`)
Define a clear workflow with validation steps: 1) Package model artifacts, 2) Create model in SageMaker, 3) Configure endpoint, 4) Deploy, 5) Test endpoint with sample inference
Include specific configuration examples for instance types, auto-scaling policies, and endpoint variants
Remove all generic boilerplate ('provides automated assistance', 'follows best practices') and replace with actual SageMaker-specific instructions and common pitfalls
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actual information about SageMaker endpoints. | 1 / 3 |
Actionability | There is zero concrete guidance - no code, no commands, no specific steps for deploying SageMaker endpoints. The entire content describes what the skill supposedly does rather than instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow is provided whatsoever. For a deployment task that involves multiple steps (model packaging, endpoint configuration, deployment, testing), there are no sequences, no validation checkpoints, and no actual process defined. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of vague descriptions with no structure for actual learning. There are no references to detailed documentation, no code examples to link to, and no organized sections with real content. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
69%Validation — 11 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 11 / 16 Passed | |
Reviewed
Table of Contents
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