azure-ai-ml-py

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

1.06x

Quality

77%

Does it follow best practices?

Impact

100%

1.06x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/azure-ai-ml-py/SKILL.md

The canonical home for this skill is azure-ai-ml-py in sickn33/agentic-awesome-skills

Quality

Content

76%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 highly actionable and concise, with copy-paste-ready examples across all major Azure ML resources, but it lacks sequenced workflows with validation checkpoints for its destructive and batch operations.

Suggestions

Add an end-to-end example workflow (register data → submit job → monitor → register model) with explicit validation/verification steps between phases.

Document validation or confirmation steps before destructive/batch operations (delete workspace/compute, cancel job).

Fold the near-duplicate data-registration blocks into one example showing both URI_FILE and URI_FOLDER variants.

Dimension	Reasoning	Score
Conciseness	The body is dominated by executable code with little concept explanation and assumes Claude's competence, but near-duplicate data-registration blocks and a generic closing "When to Use" line leave minor trim opportunities.	4 / 5
Actionability	Every example is complete, executable Python with imports, real class/method names, and concrete values (locations, VM sizes, environment names), covering the common cases across all major resources.	5 / 5
Workflow Clarity	Content is organized by resource category rather than as a sequenced end-to-end workflow, and destructive/batch operations (delete workspace/compute, cancel job) appear without validation checkpoints, capping this dimension per the rubric.	3 / 5
Progressive Disclosure	A single well-sectioned file with a compact operations-summary table and no nested references provides good structure and navigation, though the full reference could be split out for even tighter disclosure.	4 / 5
	Total	16 / 20 Passed

Description

78%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 clearly identifies the domain and an explicit Use-for trigger with a concrete resource list, but frames triggers as resource nouns rather than user-facing scenarios, which keeps most dimensions at 4 rather than 5.

Suggestions

Reframe the trigger clause around user-facing scenarios, e.g. "Use when managing Azure ML workspaces, jobs, models, datasets, compute, or pipelines."

Add a synonym or two (e.g., "AML" or "AzureML") to broaden natural trigger coverage.

Dimension	Reasoning	Score
Specificity	"ML workspaces, jobs, models, datasets, compute, and pipelines" lists several concrete resource types, but states them as nouns rather than explicit actions, leaving minor coverage gaps vs. the comprehensive-action anchor.	4 / 5
Completeness	Explicit "what" ("Azure Machine Learning SDK v2 for Python") and explicit "when" ("Use for ML workspaces, jobs, models, datasets, compute, and pipelines") are both present, but the when lists resource nouns rather than user-facing scenarios, fitting just below the concrete-trigger-phrases anchor.	4 / 5
Trigger Term Quality	Good natural keywords ("Azure Machine Learning SDK v2", "ML workspaces", "models", "pipelines") but missing synonym variants and file/asset extensions users might say, so just below the comprehensive-coverage anchor.	4 / 5
Distinctiveness Conflict Risk	Naming the specific SDK and version ("Azure Machine Learning SDK v2 for Python") carves a clear niche with distinct triggers and minimal overlap with generic ML skills.	5 / 5
	Total	17 / 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: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/azure-ai-ml-py/SKILL.md
Commit: debb2ec

Reviewed: 1 day ago

Table of Contents

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