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.
64
77%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/antigravity-azure-ai-anomalydetector-java/SKILL.mdQuality
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. Its main weakness is that the 'what' portion could be more specific about concrete actions beyond 'build applications'. The trigger terms and completeness are strong, making it easy for Claude to select appropriately.
Suggestions
Add more specific concrete actions such as 'detect spikes and changepoints', 'train multivariate models', 'batch detect anomalies in time-series data' 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 specific concrete actions like 'detect spikes', 'train models', 'configure detection windows', or 'process streaming data'. | 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. Unlikely to conflict with other skills unless there are multiple Azure anomaly detection skills for different languages. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid API reference skill with excellent actionability—every pattern includes complete, executable Java code with proper imports. However, it's somewhat verbose for a skill file, includes boilerplate sections that waste tokens, and the multivariate long-running operations lack proper polling/validation feedback loops. The content would benefit from being split into overview + detailed reference files.
Suggestions
Add explicit polling/validation loops for long-running operations (model training, batch inference) with retry logic and status checks before proceeding to next steps.
Remove the 'Trigger Phrases', 'When to Use', and 'Limitations' boilerplate sections—these waste tokens and provide no actionable guidance.
Consider splitting detailed multivariate and univariate patterns into separate reference files, keeping SKILL.md as a concise overview with quick-start examples and links.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes unnecessary sections like 'Trigger Phrases', 'When to Use', and 'Limitations' boilerplate that add no value. The 'Key Concepts' section explains things Claude already knows (e.g., what batch vs streaming detection means). The 'Best Practices' section is useful but some items are somewhat obvious. | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste ready Java code for every major operation: client creation, univariate batch/streaming detection, change point detection, multivariate training/inference, model management, and error handling. Import statements and concrete method calls are included throughout. | 3 / 3 |
Workflow Clarity | The multivariate workflow (train → inference → results) is presented but lacks explicit validation checkpoints. For example, after training a model, the skill shows checking status but doesn't include a polling loop or explicit 'wait until ready before proceeding' gate. The batch inference similarly shows getting results but no retry/validation loop for when results aren't ready yet. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and logical sections, but it's a monolithic document (~200 lines of code examples) with no references to external files. Some content like the full multivariate workflow or detailed API patterns could be split into separate reference files for better organization. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%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
- boisenoise/skills-collections
- Commit
8ac11ab
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.