azure-ai-formrecognizer-java

Build document analysis applications using the Azure AI Document Intelligence SDK for Java.

Quality

48%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

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

Quality

Discovery

40%

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 a clear and specific technology niche (Azure AI Document Intelligence SDK for Java) which makes it distinctive, but it lacks concrete action verbs describing what the skill enables and completely omits trigger guidance ('Use when...'). Adding specific capabilities and explicit trigger conditions would significantly improve skill selection accuracy.

Suggestions

Add a 'Use when...' clause with trigger terms like 'Use when building Java applications that need to extract text, tables, or key-value pairs from documents using Azure AI Document Intelligence, Form Recognizer, or Azure cognitive services.'

List specific concrete actions such as 'extract text, analyze forms, process receipts and invoices, read tables, detect document layout'

Include common alternative names and terms users might use: 'Form Recognizer', 'Azure OCR', 'cognitive services', '.pdf analysis', 'document extraction'

Dimension	Reasoning	Score
Specificity	Names the domain (document analysis applications) and the technology (Azure AI Document Intelligence SDK for Java), but doesn't list specific concrete actions like extracting text, analyzing forms, processing receipts, etc.	2 / 3
Completeness	Describes what (build document analysis applications using Azure AI Document Intelligence SDK for Java) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per rubric guidelines, missing 'Use when' caps completeness at 2, and the 'what' is also not very detailed, warranting a 1.	1 / 3
Trigger Term Quality	Includes relevant keywords like 'Azure AI Document Intelligence', 'SDK', 'Java', and 'document analysis', but misses common user variations like 'form recognizer', 'OCR', 'extract text from documents', '.pdf', 'receipt processing', or the older product name 'Azure Form Recognizer'.	2 / 3
Distinctiveness Conflict Risk	The combination of 'Azure AI Document Intelligence SDK' and 'Java' creates a very specific niche that is unlikely to conflict with other skills. This is clearly distinguishable from general document processing or other cloud provider SDKs.	3 / 3
	Total	8 / 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.

This skill provides comprehensive, executable Java code examples for the Azure Document Intelligence SDK, covering all major features from prebuilt models to custom model building and classification. However, it suffers from being a monolithic reference document that would benefit significantly from progressive disclosure — splitting detailed examples into separate files and keeping SKILL.md as a concise overview. The workflow clarity could be improved with explicit validation steps, especially around custom model training and deployment.

Suggestions

Restructure as a concise overview (quick start + prebuilt model table + client creation) with links to separate files like CUSTOM_MODELS.md, CLASSIFICATION.md, and EXAMPLES.md for detailed code

Add explicit workflow steps for custom model building: prepare training data → validate data format → build model → check model accuracy/fields → test with sample document → deploy

Remove the 'Trigger Phrases' and 'When to Use' boilerplate sections which add no actionable value to the skill content

Add error recovery guidance for the SyncPoller pattern — what to do when polling times out or returns errors during long-running operations

Dimension	Reasoning	Score
Conciseness	The skill is mostly efficient with executable code examples and a useful reference table, but it's quite long (~300 lines) and includes some patterns that could be consolidated or referenced externally. The 'Trigger Phrases' and 'When to Use' sections add no value. Some code examples are verbose with repetitive patterns (e.g., multiple SyncPoller usages that follow identical structure).	2 / 3
Actionability	All code examples are fully executable Java with proper imports, concrete method calls, and realistic usage patterns. The examples cover client creation, prebuilt models, custom models, classification, error handling, and model management — all copy-paste ready with clear placeholders.	3 / 3
Workflow Clarity	Individual operations are clear, but there's no explicit multi-step workflow showing the full process (e.g., build model → validate → analyze → handle errors). The custom model building section lacks validation checkpoints — no guidance on verifying training data quality, checking model accuracy, or handling build failures before using the model in production.	2 / 3
Progressive Disclosure	The entire skill is a monolithic document with no references to external files. At ~300 lines, the custom models section, classification section, and detailed code examples for each prebuilt model could be split into separate reference files. There's no quick-start overview followed by links to detailed content.	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: boisenoise/skills-collections
Commit: f1697b6

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

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