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gamussa/langchain4j-agentic

Build and demo Java AI agent systems with langchain4j-agentic: workflow patterns, supervisor, custom Planner strategies (incl. the flagship typed-verdict / CriticResult-style critic pattern), plus MCP tools, A2A remote agents, build setup, and conference-demo storylines. Pinned to 1.15.0 / 1.15.0-beta25.

84

4.76x
Quality

89%

Does it follow best practices?

Impact

100%

4.76x

Average score across 2 eval scenarios

SecuritybySnyk

Passed

No known issues

Overview
Quality
Evals
Security
Files

Quality

Content

72%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strong, well-structured skill that provides highly actionable guidance with excellent progressive disclosure through its pattern-selection table and doc references. Its main weakness is the lack of an explicit end-to-end workflow with validation checkpoints for scaffolding a project, and some sections could be slightly more concise. The version pinning guidance and API-correctness notes are particularly valuable additions.

Suggestions

Add a numbered end-to-end 'Getting Started' workflow (e.g., 1. Run check_versions.sh → 2. Copy build template → 3. Create first agent → 4. Build & verify → 5. Run & check HTML report) with explicit validation checkpoints at each step.

Tighten the flagship pattern description — the parenthetical clarifications ('application code, not a framework type, and need not be generic') could be moved to the referenced doc file to save tokens in the overview.

DimensionReasoningScore

Conciseness

Generally efficient and assumes Claude's competence with Java/AI concepts, but some sections are slightly verbose — e.g., the flagship pattern description could be tighter, and the 'Core mental model' section includes some explanatory framing that could be trimmed. The version table and pattern-selection table are well-structured and earn their tokens.

2 / 3

Actionability

Provides concrete version pins, specific builder names, exact annotation syntax (@Agent, @Tool, @V, @P), mandatory configuration values (.maxSequentialToolsInvocations(10)), compiler flags (-parameters), and references to runnable assets (App.java, build.gradle.kts). The pattern-selection table maps needs directly to specific builders and doc files. The 'API-correctness notes' section gives precise corrections against stale examples.

3 / 3

Workflow Clarity

The flagship pattern describes a clear conceptual workflow (Identify → Fix → Verify → Adjust with pass/fail branching), and the 'First action — pin versions' step is explicit. However, there's no step-by-step numbered workflow for actually scaffolding a project end-to-end (e.g., 1. run check_versions.sh, 2. copy build template, 3. create agents, 4. validate, 5. generate report). The skill describes patterns but doesn't provide explicit validation checkpoints for the build/run process.

2 / 3

Progressive Disclosure

Excellent structure: concise overview in SKILL.md with a well-organized pattern-selection table that maps each need to a specific doc file (custom-strategy-critic-result.md, workflow-patterns.md, pure-agentic.md, etc.). References are one level deep, clearly signaled, and cover the full feature surface. Bundled assets are listed with clear descriptions.

3 / 3

Total

10

/

12

Passed

Description

100%

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 an excellent skill description that excels across all dimensions. It provides highly specific capabilities, comprehensive trigger terms spanning both natural language and API-level identifiers, explicit 'Use when' guidance, and a very distinct niche that minimizes conflict risk. The version pinning information adds practical operational context, though it slightly increases verbosity.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions and concepts: build/scaffold/demo, workflow patterns (sequential, loop, parallel, conditional), supervisor, custom Planner strategies, typed-verdict critic pattern, MCP tool servers, A2A remote agents. Very detailed and concrete.

3 / 3

Completeness

Clearly answers both 'what' (build/scaffold/demo Java AI agent systems with specific patterns and strategies) and 'when' (explicit 'Use whenever the user mentions...' clause with comprehensive trigger list). Also includes version pinning context which adds operational completeness.

3 / 3

Trigger Term Quality

Excellent coverage of natural terms users would say: 'LangChain4j', 'langchain4j-agentic', 'Java AI agents', '@Agent / @Tool', 'AgenticScope', 'sequenceBuilder / loopBuilder / supervisorBuilder / plannerBuilder', 'MCP or A2A in Java', 'conference demo', 'workshop', 'POC', 'autonomous Java agents'. Covers both high-level and specific API-level terms.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive — targets a very specific niche (langchain4j-agentic module in Java) with unique API names, specific version numbers, and domain-specific patterns like typed-verdict critic. Extremely unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Validation

100%

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

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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