azure-ai-projects-java

Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.

Install with Tessl CLI

npx tessl i github:boisenoise/skills-collections --skill azure-ai-projects-java

What are skills?

1.59x

Quality

60%

Does it follow best practices?

Impact

97%

1.59x

Average score across 3 eval scenarios

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/antigravity-azure-ai-projects-java/SKILL.md

SKILL.md

Review

Evals

Discovery

32%

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 identifies the technology domain and lists high-level capabilities but lacks the explicit trigger guidance ('Use when...') that is critical for skill selection. The capabilities listed are category-level rather than specific actions, and the description would benefit from more natural user-facing trigger terms.

Suggestions

Add an explicit 'Use when...' clause specifying triggers like 'when the user asks about Azure AI Foundry projects in Java', 'managing AI project connections', or 'working with Azure AI datasets'.

Make capabilities more concrete by specifying actions like 'create and manage project connections', 'upload and version datasets', 'configure search indexes', 'run model evaluations'.

Include additional natural trigger terms users might say such as 'Azure ML', 'AI project setup', 'model metrics', or 'Java AI development'.

Dimension	Reasoning	Score
Specificity	Names the domain (Azure AI Projects SDK for Java) and lists some actions (project management, connections, datasets, indexes, evaluations), but these are high-level categories rather than concrete specific actions like 'create connection', 'upload dataset', or 'run evaluation'.	2 / 3
Completeness	Describes what the skill does (SDK for project management with various features) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill.	1 / 3
Trigger Term Quality	Includes relevant keywords like 'Azure AI', 'SDK', 'Java', 'Azure AI Foundry', 'connections', 'datasets', 'indexes', 'evaluations', but missing common variations users might say like 'AI project', 'Azure ML', 'model evaluation', or file extensions.	2 / 3
Distinctiveness Conflict Risk	The combination of 'Azure AI Projects SDK' and 'Java' provides some distinctiveness, but 'project management', 'datasets', and 'evaluations' are generic terms that could overlap with other Azure or ML-related skills.	2 / 3
	Total	7 / 12 Passed

Implementation

87%

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

This is a well-crafted SDK reference skill that efficiently presents Azure AI Projects SDK usage with executable Java examples and clear organization. The content excels at conciseness and actionability, providing copy-paste ready code. The main weakness is the lack of explicit workflow guidance for multi-step operations like creating and validating indexes.

Suggestions

Add a workflow example showing a complete operation sequence (e.g., create index -> verify creation -> handle failures -> retry) with explicit validation checkpoints

Include guidance on verifying successful operations, such as checking index status after creation or confirming connection validity before use

Dimension	Reasoning	Score
Conciseness	The content is lean and efficient, presenting only necessary information without explaining concepts Claude already knows. Each section delivers actionable content without padding or unnecessary context.	3 / 3
Actionability	Provides fully executable Java code examples that are copy-paste ready, including imports, proper client initialization, and concrete operations like listing connections, creating indexes, and error handling.	3 / 3
Workflow Clarity	The content presents individual operations clearly but lacks explicit multi-step workflow sequences with validation checkpoints. For SDK operations that could fail (like creating indexes), there's no guidance on verification or retry patterns.	2 / 3
Progressive Disclosure	Well-organized with clear sections progressing from installation to authentication to operations. Reference links table provides one-level-deep navigation to external resources for deeper exploration.	3 / 3
	Total	11 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

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

	Total	10 / 11 Passed

Reviewed: about 19 hours ago

Table of Contents

Discovery Implementation Validation

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