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 rrezartprebreza/spring-boot-skills --skill spring-ai-integrationgit clone --depth 1 https://github.com/rrezartprebreza/spring-boot-skillsWrote 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/rrezartprebreza/spring-boot-skills/spring-ai-integration)<a href="https://agentmods.dev/skills/rrezartprebreza/spring-boot-skills/spring-ai-integration"><img src="https://agentmods.dev/badge/skills/rrezartprebreza/spring-boot-skills/spring-ai-integration/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/rrezartprebreza/spring-boot-skills/spring-ai-integration"><img src="https://agentmods.dev/badge/skills/rrezartprebreza/spring-boot-skills/spring-ai-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00066 | $0.01916 |
| Opus 5.5 | $0.00026 | $0.00766 |
| Sonnet 5.5 | $0.00013 | $0.00383 |
| Haiku 4.5 | $0.00007 | $0.00192 |
Grade A, and why
spring-ai-integration 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 9d ago.
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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spring AI Integration
Dependencies
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-bom</artifactId>
<version>1.0.0</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<!-- Choose your model provider — 1.0 GA renamed every starter to spring-ai-starter-* -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-anthropic</artifactId>
</dependency>
<!-- OR -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-openai</artifactId>
</dependency>
<!-- For RAG / vector search -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-vector-store-pgvector</artifactId>
</dependency>
</dependencies>
Watch the artifact names. 1.0 GA dropped the old
spring-ai-<x>-spring-boot-startercoordinates. The pattern is nowspring-ai-starter-model-<provider>(e.g.-model-anthropic,-model-openai) andspring-ai-starter-vector-store-<store>. Agents trained on pre-GA Spring AI will emit the dead names — they resolve to nothing in Maven Central.
ChatClient — Basic Usage
@Service
@RequiredArgsConstructor
public class DocumentSummaryService {
private final ChatClient chatClient;
public String summarize(String conversationId, String content) {
return chatClient.prompt()
.advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
.user(u -> u.text("Summarize the following document in 3 bullet points:\n\n{content}")
.param("content", content))
.call()
.content();
}
// With system prompt
public String analyzeFinancial(String conversationId, String document, String language) {
return chatClient.prompt()
.advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
.system("You are a financial analyst. Respond in {language}.")
.system(s -> s.param("language", language))
.user(document)
.call()
.content();
}
}
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.
- 9d ago Changed · +4 lines 3037d160998e
- 1mo ago First seen · 261 lines · 66 tokens per session scan A 960d046a328c
spring-ai-integration is a skill published in the GitHub repository rrezartprebreza/spring-boot-skills (327 stars, last pushed 5d ago), licensed MIT. It adds 66 tokens to every session and 1,916 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
java-spring-ai
Use when the user asks to add AI features, integrate Spring AI or LangChain4J, build a chatbot, implement RAG (retrieval-augmented generation), use vector stores, stream LLM responses, or call AI tools/functions in a Spring Boot project.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
azure-search-documents-ts
Build search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases.
convex-agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
rag-assistant
Knowledge base assistant that retrieves and cites documents from a curated index. Use when answering questions that must be grounded in specific source material with citations.
wegent-knowledge
Knowledge base management and search tools for Wegent. Provides capabilities to list, create, update, and search knowledge bases and documents using RAG retrieval. Use this skill when the user wants to manage knowledge bases, documents, or search for information programmatically.