chunking-strategy
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages…
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,
Claude Code
$ npx -y skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies --agent claude-code
Codex
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a codex -y
Cursor
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a cursor -y
Gemini CLI
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a gemini-cli -y
GitHub Copilot
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a github-copilot -y
OpenCode
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a opencode -y
Windsurf
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies -a windsurf -y
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
Patterns for unit testing with mocks, integration testing with Testcontainers, and end-to-end validation of RAG systems, AI Services, and tool execution.
Use mock models for fast, isolated testing. See `references/unit-testing.md`.
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();Setup Maven/Gradle dependencies. See `references/testing-dependencies.md`.
Test with real services. See `references/integration-testing.md`.
@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);
}
}Streaming, memory, error handling patterns in `references/advanced-testing.md`.
Follow the testing pyramid from `references/workflow-patterns.md`:
70% Unit Tests ─ Mock ChatModel, guardrails, edge cases 20% Integration Tests ─ Testcontainers, vector stores, RAG 10% End-to-End Tests ─ Complete user journeys
@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);
}@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"));
}
}ChatModel mockModel = mock(ChatModel.class);
when(mockModel.generate(anyString())).thenReturn(Response.from(AiMessage.from("Mocked")));
when(mockModel.generate(eq("Hello"))).thenReturn(RespoModular 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.
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages…
Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought…
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation…
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups,…
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails,…
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors,…
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.