Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategiesgit clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/giuseppe-trisciuoglio/developer-kit/langchain4j-testing-strategies)<a href="https://agentmods.dev/skills/giuseppe-trisciuoglio/developer-kit/langchain4j-testing-strategies"><img src="https://agentmods.dev/badge/skills/giuseppe-trisciuoglio/developer-kit/langchain4j-testing-strategies/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/giuseppe-trisciuoglio/developer-kit/langchain4j-testing-strategies"><img src="https://agentmods.dev/badge/skills/giuseppe-trisciuoglio/developer-kit/langchain4j-testing-strategies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.01566 |
| Opus 5 | $0.00041 | $0.00783 |
| Sonnet 5 | $0.00016 | $0.00313 |
| Haiku 4.5 | $0.00008 | $0.00157 |
Grade A, and why
langchain4j-testing-strategies scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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 assertionstestcontainers- Containerized testingmockito- Mock external dependenciesassertj- Fluent assertions
3. Integration Testing with Testcontainers
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);
}
}
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 210 lines · 81 tokens per session scan A 1ceb073d7c28
langchain4j-testing-strategies is a skill published in the GitHub repository giuseppe-trisciuoglio/developer-kit (344 stars, last pushed 2d ago), licensed MIT. It adds 81 tokens to every session and 1,566 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-10.
Other skills, from other repositories
loom-test-strategy
Test strategy guidance covering pyramid design, coverage, test categorization, flaky tests, infrastructure, and risk-based prioritization.
loom-testing
Test implementation across unit, integration, e2e, security, infrastructure, data pipeline, and ML domains.
continuous-testing
Continuous test-driven development loop — after every code change, builds the project, starts the server, and runs Unit Tests, Integration Tests, and System Tests. Applies on top of microprofile-server skill. Use during development when you want full verification after each change. Triggers on "continuous testing"…
data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
dbt-transformation-patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
prompt-set-qa
Gate a prompt universe for schema and provenance completeness, target or campaign contamination, evidence entailment, naturalness, one-concept clarity, architecture consistency, aided status, answer leakage, and semantic duplicates. Use after realistic prompt generation and before human panel selection.