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azure-ai-document-intelligence-java

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

58

Quality

68%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/azure-ai-document-intelligence-java/SKILL.md

The canonical home for this skill is azure-ai-document-intelligence-java in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

82%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 strong, well-structured SDK reference dominated by concrete, executable Java examples that cover the major Document Intelligence workflows. Its main weaknesses are missing validation feedback loops for build/delete operations and minor conciseness padding.

Suggestions

Add a validate-then-retry feedback loop for long-running or destructive admin operations (e.g., check model build status/errors and retry, verify a model exists before deleting).

Remove the redundant repeated intro line and replace the generic 'When to Use' boilerplate with concrete trigger guidance.

Consider moving the prebuilt-model table and extended custom-model/classifier details into a reference file to better separate overview from deep reference material.

DimensionReasoningScore

Conciseness

The body is mostly executable code with minimal prose and does not explain concepts Claude already knows; minor padding remains in the repeated intro line, the boilerplate 'When to Use' filler, and the time-sensitive version pin (1.0.1).

4 / 5

Actionability

Complete, copy-paste-ready Java snippets with imports cover the common cases end-to-end: client creation, layout/URL/receipt/document analysis, custom model build/analyze/compose/manage, classification, and error handling.

5 / 5

Workflow Clarity

A consistent poller sequence (begin -> getFinalResult -> process) is demonstrated across all long-running operations and an error-handling section exists, but there are no explicit validate/retry feedback loops for build or delete operations.

4 / 5

Progressive Disclosure

No bundle files exist; the single SKILL.md is well-organized with clear section headers and no dead or nested references, though a file this long could split some detailed reference material into separate files.

4 / 5

Total

17

/

20

Passed

Description

53%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 clearly identifies the SDK and language niche but stops at a single generic action and lacks any 'when to use' trigger guidance. It is distinct yet incomplete and light on natural trigger terms.

Suggestions

Add a 'Use when ...' clause listing concrete trigger situations (e.g., extracting text/tables from PDFs, OCR, analyzing invoices/receipts, building custom document models in Java).

Expand the action beyond 'Build document analysis applications' to specific capabilities like extract text and tables, analyze receipts/invoices, build and compose custom models, and classify documents.

Include natural user phrasing and file extensions (PDF, OCR, .pdf, invoice, receipt) to improve trigger term coverage.

DimensionReasoningScore

Specificity

Names the domain ('Azure AI Document Intelligence SDK for Java') and one action ('Build document analysis applications'), but offers only a single high-level action rather than a list of concrete capabilities.

3 / 5

Completeness

Provides a clear 'what' but no 'when'/'Use when' clause; per the rubric a missing explicit trigger clause caps completeness at 3.

3 / 5

Trigger Term Quality

Includes relevant natural terms ('document analysis', 'Java') but omits common variations users would say such as PDF, OCR, invoice, receipt, or 'extract text'.

3 / 5

Distinctiveness Conflict Risk

Naming a specific Azure SDK plus the Java language carves a clear niche with minimal overlap risk, though the lack of explicit trigger phrases keeps it just below a 5.

4 / 5

Total

13

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

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