Name: amazon-sagemaker
Rating: 59.2 (1 reviews)
Author: membranedev

amazon-sagemaker

Amazon Sagemaker integration. Manage data, records, and automate workflows. Use when the user wants to interact with Amazon Sagemaker data.

Quality

68%

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tessl review fix ./skills/amazon-sagemaker/SKILL.md

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-structured, actionable integration guide anchored in concrete CLI commands and a state-driven connection workflow. Its main flaws are a truncated opening fragment, a 'Popular actions' section that underdelivers, and a dangling 'Step 2' reference.

Suggestions

Fix the truncated 'Amazon S' fragment at the top of the body and either remove the 'Popular actions' heading or populate it with actual commonly-used SageMaker actions.

Resolve the dangling 'Skip to Step 2' reference by defining Step 2 or relabeling it to match the actual subsequent section (e.g. 'Searching for actions').

Trim marketing-style padding (e.g. 'so you can focus on the integration logic rather than auth plumbing') to tighten token efficiency.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with copy-paste commands and brief prose, but a few padded/marketing lines ('so you can focus on the integration logic rather than auth plumbing', 'This is the fastest way to get a connection') could be trimmed, and a stray truncated 'Amazon S' fragment appears at the top of the body.	4 / 5
Actionability	Provides concrete, executable commands throughout (install, login, connection ensure/get with --wait, action list/run with example JSON input, request proxy with a full flag table), with only minor gaps such as a 'Popular actions' section that promises a list but only restates the discovery command.	4 / 5
Workflow Clarity	The connection workflow is clearly sequenced with explicit state-based checkpoints (READY / CLIENT_ACTION_REQUIRED / CONFIGURATION_ERROR) and a re-poll feedback loop after user action, but a dangling 'Skip to Step 2' reference points to a step that is never defined.	4 / 5
Progressive Disclosure	No bundle files exist and all content lives in one ~160-line file, but it is well-organized into clearly headed sections (Overview, install, auth, connecting, actions, proxy, best practices); the inline proxy-flag table and overview list could arguably be split out, keeping it just below a 5.	4 / 5
	Total	16 / 20 Passed

Description

62%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description successfully pairs a 'what' with an explicit 'Use when' trigger, anchoring it to a distinct product niche. Its weaknesses are generic action verbs and a thin set of natural trigger terms that omit SageMaker-specific vocabulary.

Suggestions

Replace generic verbs ('Manage data, records, and automate workflows') with concrete SageMaker actions such as 'create notebook instances, run training jobs, deploy endpoints, and manage models'.

Broaden trigger terms to include synonyms users actually say: 'SageMaker', 'notebooks', 'training jobs', 'endpoints', and 'ML models'.

Make the 'when' clause more specific, e.g. 'Use when the user wants to create or manage SageMaker notebooks, training jobs, or endpoints.'

Dimension	Reasoning	Score
Specificity	Names the domain and a few actions ('Manage data, records, and automate workflows'), but the actions are generic ('manage', 'automate workflows') rather than the concrete SageMaker-specific operations (notebooks, training jobs, endpoints) a higher score would require.	3 / 5
Completeness	Explicitly answers both 'what' (manage data, records, automate workflows) and 'when' ('Use when the user wants to interact with Amazon Sagemaker data'), though the 'when' clause could be more specific about which SageMaker tasks.	4 / 5
Trigger Term Quality	Includes the natural product keyword 'Amazon Sagemaker' and a trigger clause, but misses common variations, synonyms, or file/term signals (e.g. SageMaker, notebooks, training jobs, models) that users would actually say.	3 / 5
Distinctiveness Conflict Risk	Tied clearly to the distinct 'Amazon Sagemaker' product niche with minimal conflict risk; minor overlap possible with other AWS data skills, keeping it just below a 5.	4 / 5
	Total	14 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: membranedev/application-skills
Path: skills/amazon-sagemaker/SKILL.md
Commit: f484c82

Reviewed: 3 days ago

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