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.
34
3%
Does it follow best practices?
Impact
87%
0.98xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/08-ml-deployment/sagemaker-endpoint-deployer/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is essentially a template placeholder with no substantive content. It names the tool (SageMaker) and category (ML Deployment) but provides zero concrete actions, no meaningful trigger terms, and no 'when to use' guidance. It would be nearly useless for Claude to distinguish this skill from others in a large skill library.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Deploys ML models to SageMaker real-time or serverless endpoints, configures instance types, sets up auto-scaling, and manages endpoint lifecycle.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user wants to deploy a model to SageMaker, create an inference endpoint, host a model for real-time predictions, or configure SageMaker endpoint settings.'
Replace the duplicated trigger term with diverse natural keywords users would say, such as 'deploy model', 'inference endpoint', 'SageMaker hosting', 'real-time prediction', 'model serving', 'endpoint configuration'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('SageMaker Endpoint Deployer', 'ML Deployment') but lists no concrete actions. There are no specific capabilities described such as 'deploy models', 'configure instances', 'create endpoints', etc. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself, and there is no explicit 'when to use' clause. The 'Triggers on' line is just the skill name repeated, not meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'sagemaker endpoint deployer' repeated twice. There are no natural user keywords like 'deploy model', 'inference endpoint', 'hosting', 'real-time prediction', or 'SageMaker' alone that users would naturally say. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'SageMaker' and 'endpoint' provides some specificity to a particular AWS service, which reduces conflict with generic deployment skills. However, the lack of concrete actions means it could still overlap with other SageMaker-related or ML deployment skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is an empty shell with no actionable content whatsoever. It consists entirely of generic boilerplate that describes what the skill would theoretically do without providing any actual instructions, code, commands, or SageMaker-specific knowledge. It adds zero value beyond what Claude already knows.
Suggestions
Add concrete, executable code examples for deploying a SageMaker endpoint (e.g., using boto3 or the SageMaker Python SDK to create a model, endpoint configuration, and endpoint).
Define a clear multi-step workflow with validation checkpoints: package model artifacts → create Model → create EndpointConfig → create Endpoint → validate with test inference → monitor with CloudWatch.
Include specific configuration examples (instance types, auto-scaling policies, model data S3 paths) and common error handling patterns for SageMaker deployments.
Remove all generic boilerplate sections (Purpose, When to Use, Example Triggers) and replace with actionable technical content covering real deployment scenarios.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler with no substantive information. It explains what the skill does in abstract terms without providing any actual knowledge or instructions that Claude doesn't already know. Every section is padded boilerplate. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no specific SageMaker API calls, no configuration examples, no deployment steps. The content only describes what the skill would do rather than instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. Deploying a SageMaker endpoint is inherently a multi-step process (model packaging, endpoint configuration, deployment, validation), yet none of these steps are outlined or sequenced. | 1 / 3 |
Progressive Disclosure | The content has section headers but they contain no meaningful content—just generic descriptions. There are no references to detailed guides, no links to examples or configuration templates, and no structured navigation to deeper material. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
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- Reviewed
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