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Agent skill

Langchain4j Testing Strategies

by giuseppe-trisciuoglio in giuseppe-trisciuoglio/developer-kit

Provides unit test, integration test, and mock AI patterns for LangChain4j applications.

MITAuto-check: notesTesting & QA

Install Langchain4j Testing Strategies

skills CLI
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install giuseppe-trisciuoglio/developer-kit langchain4j-testing-strategies --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/developer-kit-java/skills/langchain4j-testing-strategies .claude/skills/langchain4j-testing-strategies && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
langchain4j-testing-strategies
GitHub stars
357
Token cost
~1.8k tokens
SKILL.md length
355 words
Files
6 (incl. references)
Skills in repo
115
Repo updated
First seen
Licence
MIT

At a glance

Provides unit test, integration test, and mock AI patterns for LangChain4j applications.

  • Works in 5 steps: Unit Testing with Mocks → Configure Testing Dependencies → Integration Testing with Testcontainers → …
  • Unit testing AI services
  • SKILL.md covers Overview, When to Use, Instructions and Examples, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Langchain4j Testing Strategies is an agent skill from giuseppe-trisciuoglio/developer-kit. Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/advanced-testing.md`, `references/integration-testing.md` and `references/testing-dependencies.md`).

It sits in Testing & QA, covering Integration testing and Unit testing. It works with Java. The repository describes itself as: Modular plugin marketplace for Claude Code and agentic CLIs, with validated, spec-driven skills, agents, commands, and workflows for Java, TypeScript, Python, PHP, AWS, and AI. The licence is MIT.

When your agent uses it

  • Unit testing AI services
  • Integration testing LangChain4j components
  • Mocking AI models
  • Testing LLM-based Java applications

Example prompts

  • “Use the langchain4j-testing-strategies skill to provide unit test, integration test, and mock AI patterns for LangChain4j applications”
  • “/langchain4j-testing-strategies”

Requirements

  • Docker
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash, Glob, Grep

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Unit Testing with Mocks
  2. Configure Testing Dependencies
  3. Integration Testing with Testcontainers
  4. Advanced Features
  5. Testing Workflow

What it can do on your machine

Read from SKILL.md and the folder at commit fe73fb3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash
    • Glob
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are java).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Langchain4j Testing Strategies loads about 1.8k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 355 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~103
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~13k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Bash, Glob, Grep

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from giuseppe-trisciuoglio/developer-kit at commit fe73fb3, republished under its MIT licence (© giuseppe-trisciuoglio). 355 words, ~1,809 tokens.

Download SKILL.mdSave it as .claude/skills/langchain4j-testing-strategies/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
langchain4j-testing-strategies
description
Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.
allowed-tools
Read, Write, Edit, Bash, Glob, Grep

LangChain4J Testing Strategies

Overview

Patterns for unit testing with mocks, integration testing with Testcontainers, and end-to-end validation of RAG systems, AI Services, and tool execution.

When to Use

  • Unit testing AI services: When you need fast, isolated tests for services using LangChain4j AiServices
  • Integration testing LangChain4j components: When testing real ChatModel, EmbeddingModel, or RAG pipelines with Testcontainers
  • Mocking AI models: When you need deterministic responses without calling external APIs
  • Testing LLM-based Java applications: When validating RAG workflows, tool execution, or retrieval chains

Instructions

1. Unit Testing with Mocks

Use mock models for fast, isolated testing. See references/unit-testing.md.

java
ChatModel mockModel = mock(ChatModel.class);
when(mockModel.generate(any(String.class)))
    .thenReturn(Response.from(AiMessage.from("Mocked response")));

var service = AiServices.builder(AiService.class)
        .chatModel(mockModel)
        .build();
2. Configure Testing Dependencies

Setup Maven/Gradle dependencies. See references/testing-dependencies.md.

  • langchain4j-test - Guardrail assertions
  • testcontainers - Containerized testing
  • mockito - Mock external dependencies
  • assertj - Fluent assertions
3. Integration Testing with Testcontainers

Test with real services. See references/integration-testing.md.

java
@Testcontainers
class OllamaIntegrationTest {
    @Container
    static GenericContainer<?> ollama = new GenericContainer<>(
        DockerImageName.parse("ollama/ollama:0.5.4")
    ).withExposedPorts(11434);

    @Test
    void shouldGenerateResponse() {
        // Verify container is healthy
        assertTrue(ollama.isRunning());
        await().atMost(30, TimeUnit.SECONDS)
            .until(() -> ollama.getLogs().contains("API server listening"));

        ChatModel model = OllamaChatModel.builder()
                .baseUrl(ollama.getEndpoint())
                .build();

        // Verify model responds before running tests
        assertDoesNotThrow(() -> model.generate("ping"));

        String response = model.generate("Test query");
        assertNotNull(response);
    }
}
4. Advanced Features

Streaming, memory, error handling patterns in references/advanced-testing.md.

5. Testing Workflow

Follow the testing pyramid from references/workflow-patterns.md:

  • 70% Unit Tests: Fast, isolated with mocks
  • 20% Integration Tests: Real services with health checks
  • 10% End-to-End Tests: Complete workflows
70% Unit Tests ─ Mock ChatModel, guardrails, edge cases
20% Integration Tests ─ Testcontainers, vector stores, RAG
10% End-to-End Tests ─ Complete user journeys
Troubleshooting
  • Container fails to start: Check Docker daemon is running, verify image exists, increase timeout
  • Model not responding: Verify baseUrl is correct, check container logs, ensure model is loaded
  • Test timeout: Increase @Timeout duration for slow models, check container resource limits
  • Flaky tests: Add retry logic or health checks before assertions
Show full SKILL.md (135 more words)Show less

Examples

Unit Test
java
@Test
void shouldProcessQueryWithMock() {
    ChatModel mockModel = mock(ChatModel.class);
    when(mockModel.generate(any(String.class)))
        .thenReturn(Response.from(AiMessage.from("Test response")));

    var service = AiServices.builder(AiService.class)
            .chatModel(mockModel)
            .build();

    String result = service.chat("What is Java?");
    assertEquals("Test response", result);
}
Integration Test with Testcontainers
java
@Testcontainers
class RAGIntegrationTest {
    @Container
    static GenericContainer<?> ollama = new GenericContainer<>(
        DockerImageName.parse("ollama/ollama:0.5.4")
    );

    @BeforeAll
    static void waitForContainerReady() {
        await().atMost(60, TimeUnit.SECONDS)
            .until(() -> ollama.getLogs().contains("API server listening"));
    }

    @Test
    void shouldCompleteRAGWorkflow() {
        assertTrue(ollama.isRunning());

        var chatModel = OllamaChatModel.builder()
                .baseUrl(ollama.getEndpoint())
                .build();

        var embeddingModel = OllamaEmbeddingModel.builder()
                .baseUrl(ollama.getEndpoint())
                .build();

        var store = new InMemoryEmbeddingStore<>();
        var retriever = EmbeddingStoreContentRetriever.builder()
                .chatModel(chatModel)
                .embeddingStore(store)
                .embeddingModel(embeddingModel)
                .build();

        var assistant = AiServices.builder(RagAssistant.class)
                .chatLanguageModel(chatModel)
                .contentRetriever(retriever)
                .build();

        String response = assistant.chat("What is Spring Boot?");
        assertNotNull(response);
        assertTrue(response.contains("Spring"));
    }
}

Best Practices

  • Use @BeforeEach/@AfterEach for test isolation
  • Never call real APIs in unit tests; use mocks
  • Include @Timeout for external service calls
  • Test both success and error handling scenarios
  • Validate response coherence and edge cases

Common Patterns

Mock Strategy
java
ChatModel mockModel = mock(ChatModel.class);
when(mockModel.generate(anyString())).thenReturn(Response.from(AiMessage.from("Mocked")));
when(mockModel.generate(eq("Hello"))).thenReturn(Response.from(AiMessage.from("Hi")));
when(mockModel.generate(contains("Java"))).thenReturn(Response.from(AiMessage.from("Java")));
Assertion Helpers
java
assertThat(response).isNotNull().isNotEmpty();
assertThat(response).containsAll(expectedKeywords);
assertThat(response).doesNotContain("error");

Reference Documentation

Constraints and Warnings

  • AI responses are non-deterministic; use mocks for reliable unit tests
  • Avoid real API calls in tests to prevent costs and rate limiting
  • Integration tests require Docker; use container health checks
  • RAG tests need properly seeded embedding stores
  • Mock-based tests cannot guarantee actual LLM behavior; supplement with integration tests
  • Use test-specific configuration profiles; never affect production data

© giuseppe-trisciuoglio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in plugins/developer-kit-java/skills/langchain4j-testing-strategies of giuseppe-trisciuoglio/developer-kit.

  • SKILL.md
  • references/advanced-testing.md
  • references/integration-testing.md
  • references/testing-dependencies.md
  • references/unit-testing.md
  • references/workflow-patterns.md

Open the folder on GitHubat commit fe73fb3

Compare with similar skills

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Works with

Categories

Questions about Langchain4j Testing Strategies

What does Langchain4j Testing Strategies do?

Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Langchain4j Testing Strategies is an agent skill from giuseppe-trisciuoglio/developer-kit. Provides unit test, integration test, and mock AI patterns for LangChain4j applications.

When should I use Langchain4j Testing Strategies?

Langchain4j Testing Strategies fits situations like: unit testing AI services; integration testing LangChain4j components; mocking AI models; testing LLM-based Java applications.

How do I install Langchain4j Testing Strategies in Claude Code?

Run `npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a claude-code`. Or copy the skill folder (plugins/developer-kit-java/skills/langchain4j-testing-strategies in giuseppe-trisciuoglio/developer-kit) into .claude/skills/langchain4j-testing-strategies in your project. Claude Code loads it when a task matches its description.

How do I install Langchain4j Testing Strategies in Codex?

Run `npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a codex`. Or copy the skill folder (plugins/developer-kit-java/skills/langchain4j-testing-strategies in giuseppe-trisciuoglio/developer-kit) into .agents/skills/langchain4j-testing-strategies in your project. Codex loads it when a task matches its description.

Can I use Langchain4j Testing Strategies in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langchain4j-testing-strategies, .gemini/skills/langchain4j-testing-strategies, .github/skills/langchain4j-testing-strategies and .opencode/skills/langchain4j-testing-strategies in your project.

What does Langchain4j Testing Strategies need to run?

SKILL.md names no scripts, command-line tools or credentials: Langchain4j Testing Strategies is instructions for the agent only. Our summary lists: Docker. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Glob, Grep.

Does Langchain4j Testing Strategies access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Langchain4j Testing Strategies safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Langchain4j Testing Strategies use?

Langchain4j Testing Strategies is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Langchain4j Testing Strategies use?

About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Langchain4j Testing Strategies?

Skills that share tags, products or a category with Langchain4j Testing Strategies: Validation (josstei/maestro-orchestrate, 465 stars), Junit 5 Skill (sickn33/agentic-awesome-skills, 47k stars), Test Case Reducer (ArabelaTso/Skills-4-SE, 253 stars) and Test Writing Workflow (iOfficeAI/AionUi, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Langchain4j Testing Strategies?

giuseppe-trisciuoglio (a GitHub user) maintains it in giuseppe-trisciuoglio/developer-kit, which has 357 GitHub stars. The repository holds 115 skills in this directory. The repository was last updated on September 10, 2026.

Source: giuseppe-trisciuoglio/developer-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.