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langchain4j-ai-services-patterns

Provides patterns to build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered features with minimal boilerplate code in Java applications.

65

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Passed

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The canonical home for this skill is giuseppe-trisciuoglio/developer-kit

SKILL.md
Quality
Evals
Security

Quality

Content

71%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.

Well-structured skill with concrete, executable Java examples and exemplary progressive disclosure into two real reference files. Its main weakness is redundancy: the Quick Start section repeats the Instructions section almost line-for-line, and the Overview restates the description, costing tokens without adding guidance.

Suggestions

Merge the Quick Start section into the Instructions section (or vice versa) - the Assistant, CustomerSupportBot, MultiUserAssistant, and Calculator examples currently appear twice nearly verbatim.

Replace the Overview paragraph, which restates the frontmatter description, with a short sentence or remove it entirely.

Show how to construct the chatModel once (e.g., OpenAiChatModel.builder()) or point to where it is defined, so the inline examples are fully runnable.

DimensionReasoningScore

Conciseness

The Quick Start section (~60 lines) duplicates Instructions steps 1-5 nearly verbatim (Assistant, CustomerSupportBot, MultiUserAssistant, Calculator examples), and the Overview paragraph restates the frontmatter description. Per-line content is efficient, but the duplication is clear padding that should be merged.

3 / 5

Actionability

Concrete Java code blocks for every pattern plus exact Maven/Gradle dependencies. Minor gaps: chatModel/model variables are never constructed inline and the Sentiment return type is referenced but undefined, so examples are not fully copy-paste runnable without the reference files.

4 / 5

Workflow Clarity

Instructions 1-5 give a clear, coherent sequence (interface, annotations, build, memory, tools). This is a code-pattern skill rather than a destructive/batch operation, so the validation cap does not apply, but no verification or testing step for the built services keeps it below a 5.

4 / 5

Progressive Disclosure

SKILL.md is a genuine overview: bulk practical examples and the full API reference are split into real one-level-deep files (references/examples.md, references/references.md), clearly signaled with descriptive link text and content bullet lists. Navigation is easy and nothing that belongs in a separate file is inlined.

5 / 5

Total

16

/

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.

A strong description: third-person voice, concrete capability list, explicit 'Use when...' trigger tied to Java, and a distinctive LangChain4j niche. Main improvement would be replacing the vague 'advanced application patterns' with concrete actions and adding a few more synonym triggers.

DimensionReasoningScore

Specificity

Lists several specific capability areas ('interface-based patterns, annotations, memory management, tools integration') within the named LangChain4j domain, but 'advanced application patterns' remains abstract, falling short of the fully concrete action list of a 5.

4 / 5

Completeness

Explicitly answers both: what ('Provides patterns to build declarative AI Services with LangChain4j...') and when ('Use when implementing type-safe AI-powered features with minimal boilerplate code in Java applications') with a concrete trigger scenario.

5 / 5

Trigger Term Quality

Good coverage of natural terms users would say ('LangChain4j', 'AI Services', 'Java', 'type-safe', 'boilerplate', 'tools', 'memory management'), though common variations like 'function calling', 'chat model', or 'Spring' are missing.

4 / 5

Distinctiveness Conflict Risk

Clear niche: LangChain4j declarative AI Services in Java. The named library and language make it highly distinguishable from generic AI or Java skills with minimal conflict risk.

5 / 5

Total

18

/

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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Repository

fernandezbaptiste/developer-kit

Path

plugins/developer-kit-java/skills/langchain4j-ai-services-patterns/SKILL.md

Reviewed

Table of Contents

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