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.

Quality

72%

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Critical

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tessl review fix ./skills/antigravity-azure-ai-anomalydetector-java/SKILL.md

The canonical home for this skill is azure-ai-anomalydetector-java in sickn33/antigravity-awesome-skills

Quality

Content

57%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 content is a solid, code-rich reference with executable examples covering the SDK's main surface, but it is held back by generic boilerplate sections, a time-sensitive version pin, missing polling/validation feedback loops for batch operations, and no progressive disclosure via separate files.

Suggestions

Replace the generic 'When to Use' and 'Limitations' boilerplate with skill-specific guidance, and move the duplicate 'Trigger Phrases' list out of the body since it already lives in the description.

Add explicit polling loops with status checks for the multivariate training and batch inference long-running operations, including a validate-and-retry feedback loop, so workflow clarity rises above the batch-operation cap.

Isolate the version number (3.0.0-beta.6) in a clearly labeled section or move detailed API examples into a separate reference file to improve both conciseness and progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient code with little prose padding, but includes generic boilerplate sections ('When to Use', 'Limitations', 'Trigger Phrases' duplicating the description) and a specific version number (3.0.0-beta.6) that is time-sensitive and not isolated in a deprecated/old-patterns section.	3 / 5
Actionability	Provides concrete, mostly copy-paste-ready Java examples across client creation, detection, training, inference, and error handling, with minor gaps such as placeholder SAS tokens and a truncated series ('// ... more data points').	4 / 5
Workflow Clarity	The multivariate three-step process (Train -> Inference -> Results) is sequenced, but long-running/batch operations lack real validation feedback loops: training calls getMultivariateModel once without polling, and 'Poll for results' is stated but not implemented as a retry loop, capping this dimension at 3.	3 / 5
Progressive Disclosure	The single ~267-line file is well-organized with clear section headers, but all API reference material and code examples are inlined with no external reference files or signaled one-level-deep navigation, so content that could be split remains inline.	3 / 5
	Total	13 / 20 Passed

Description

87%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 is strong: it clearly answers both what the skill does and when to use it, with concrete trigger phrases and a distinct niche. Minor weakness in specificity and trigger-term synonym coverage keep it just below full marks on those dimensions.

Dimension	Reasoning	Score
Specificity	Names the domain and several concrete actions (univariate/multivariate anomaly detection, time-series analysis, AI-powered monitoring), but the 'Build anomaly detection applications' framing is somewhat generic, leaving minor coverage gaps rather than comprehensive action enumeration.	4 / 5
Completeness	Explicitly states both what it does ('Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java') and when to use it ('Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Includes good natural keywords users would say ('anomaly detection', 'univariate/multivariate anomaly detection', 'time-series analysis'), though common variations like 'detect anomalies' and synonyms are missing.	4 / 5
Distinctiveness Conflict Risk	Targets a clear niche (Azure AI Anomaly Detector SDK for Java) with distinct triggers, making conflict with unrelated skills unlikely.	5 / 5
	Total	18 / 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: boisenoise/skills-collections
Path: skills/antigravity-azure-ai-anomalydetector-java/SKILL.md
Commit: 19c310e

Reviewed: about 17 hours ago

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