azure-ai-anomalydetector-java

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

2.33x

Quality

73%

Does it follow best practices?

Impact

98%

2.33x

Average score across 3 eval scenarios

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

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

Quality

Discovery

89%

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 is a solid skill description that clearly identifies its niche (Azure AI Anomaly Detector SDK for Java) and provides explicit trigger guidance. The main weakness is that the 'what' portion could be more specific about concrete actions beyond 'build applications'. The trigger terms and distinctiveness are strong due to the specific technology stack.

Suggestions

Add more concrete actions to the capability description, e.g., 'detect point anomalies, identify change points, train multivariate detection models, analyze time-series data streams' to improve specificity.

Dimension	Reasoning	Score
Specificity	Names the domain (Azure AI Anomaly Detector SDK for Java) and mentions some actions (univariate/multivariate anomaly detection, time-series analysis), but doesn't list multiple concrete actions like 'detect anomalies in streaming data, train multivariate models, configure detection sensitivity'.	2 / 3
Completeness	Clearly answers both what ('Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java') and when ('Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring') with an explicit 'Use when' clause.	3 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'anomaly detection', 'Azure AI', 'Anomaly Detector SDK', 'Java', 'univariate', 'multivariate', 'time-series analysis', 'monitoring'. These cover the main terms a developer working in this space would use.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive due to the specific combination of Azure AI Anomaly Detector + SDK + Java. This is unlikely to conflict with other skills unless there are multiple Azure Anomaly Detector skills for different languages.	3 / 3
	Total	11 / 12 Passed

Implementation

57%

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

The skill excels at actionability with comprehensive, executable Java code examples covering all major Anomaly Detector SDK operations. However, it suffers from being a monolithic reference document that could benefit from splitting into separate files for univariate vs multivariate patterns. The multivariate workflow lacks explicit validation checkpoints between training and inference steps, and some sections (Trigger Phrases, When to Use, Key Concepts) add little value.

Suggestions

Split univariate and multivariate patterns into separate reference files (e.g., UNIVARIATE.md, MULTIVARIATE.md) and keep SKILL.md as a concise overview with links

Add explicit validation checkpoints to the multivariate workflow: check model training status in a polling loop before proceeding to inference, and verify batch detection completion before reading results

Remove the 'Trigger Phrases', 'When to Use', and 'Key Concepts' sections as they waste tokens on information Claude doesn't need

Add a feedback loop for error recovery in the multivariate training section (e.g., what to do if training fails, how to diagnose data format issues)

Dimension	Reasoning	Score
Conciseness	The content is mostly efficient with executable code examples, but includes some unnecessary sections like 'Trigger Phrases' and 'When to Use' which add no value for Claude. The 'Key Concepts' section explains things Claude already knows (e.g., what batch vs streaming detection means). The best practices section is somewhat useful but partially obvious.	2 / 3
Actionability	The skill provides fully executable Java code examples for every major operation: client creation, univariate batch/streaming detection, change point detection, multivariate training/inference, model management, and error handling. Code is copy-paste ready with proper imports and realistic usage patterns.	3 / 3
Workflow Clarity	The multivariate workflow (train → inference → results) is presented across separate code blocks but lacks explicit validation checkpoints. There's no feedback loop for checking training status before proceeding to inference, and the polling for batch results doesn't show a retry/wait pattern. For a multi-step process involving long-running operations, this is a notable gap.	2 / 3
Progressive Disclosure	The content is a monolithic wall of text with all API patterns inline. At ~200 lines, the multivariate and univariate sections could be split into separate reference files. There are no references to external files for advanced topics, and the document tries to cover everything in one place without clear navigation structure.	1 / 3
	Total	8 / 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

Repository: sickn33/antigravity-awesome-skills
Commit: 33fc69f

Reviewed: 3 days ago

Table of Contents

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