feat(java-spring): add java-spring-ai skill

README.md

@@ -7,7 +7,7 @@ A Claude Code plugin marketplace with 3 focused plugins for Java developers. All
 | Plugin | Skills | Commands | Agents | Install when |
 |---|---|---|---|---|
 | `java-core` | 14 | 2 | `java-architect`, `java-build-resolver` | Every Java project |
-| `java-spring` | 6 | 2 | `java-spring-expert` | Spring Boot projects |
+| `java-spring` | 7 | 2 | `java-spring-expert` | Spring Boot projects |
 | `java-quality` | 3 | 1 | `java-security-reviewer`, `java-performance-reviewer`, `java-test-engineer` | Quality enforcement |
 
 ## Quick Setup (5 minutes)
@@ -75,6 +75,7 @@ Skills activate automatically based on context, or invoke them explicitly.
 | `/java-spring:java-crud` | Generate a complete CRUD feature in an existing project |
 | `/java-spring:java-security` | Review or generate Spring Security config — JWT, OAuth2, method security, CORS (Boot 2.x & 3.x) |
 | `/java-spring:java-openapi` | Generate or review OpenAPI/Swagger docs — `@Tag`, `@Operation`, `@Schema`, JWT auth scheme (springdoc v1/v2) |
+| `/java-spring:java-spring-ai` | Add AI features to Spring Boot — ChatClient, RAG, tool calling, memory (Spring AI 1.x / LangChain4J) |
 
 ### java-quality
 

plugins/java-spring/skills/java-spring-ai/SKILL.md

@@ -0,0 +1,140 @@
+---
+name: java-spring-ai
+description: 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.
+version: 1.0.0
+authors: [java-plugins contributors]
+tags: [java, spring-boot, spring-ai, langchain4j, llm, rag, vector-store, ai]
+allowed-tools: [Read, Glob, Grep, Edit, Write]
+---
+
+# Spring AI / LangChain4J Skill
+
+Detect the framework in use, then apply the correct patterns.
+
+## Step 1 — Detect framework and version
+
+Check `pom.xml` or `build.gradle`:
+- `spring-ai-*` dependency → **Spring AI** (note version: 1.0.x GA or 0.8.x milestone)
+- `langchain4j-*` dependency → **LangChain4J** (note version: 0.x or 1.x)
+- Neither present → offer to add one (recommend Spring AI for Spring Boot 3.x, LangChain4J for Boot 2.x)
+
+Check Spring Boot version:
+- Boot 3.x → Spring AI 1.x preferred, LangChain4J 0.35+
+- Boot 2.x → LangChain4J 0.30.x (Spring AI requires Boot 3.x)
+
+---
+
+## Mode: `review`
+
+User asks to review existing AI code. Check for:
+
+**Spring AI:**
+- [ ] `ChatClient` built via `ChatClient.Builder` (not raw `ChatModel`) for fluent API
+- [ ] Prompt templates use `PromptTemplate` with variables — no string concatenation
+- [ ] Streaming uses `stream().content()` or `Flux<String>` — not blocking `.call()` for real-time responses
+- [ ] `@Retryable` or Spring AI retry config on ChatClient calls — LLMs are flaky
+- [ ] Secrets (`spring.ai.openai.api-key`) come from env vars or Vault, never hardcoded
+- [ ] `VectorStore` queries use `SearchRequest.query(text).withTopK(n)` — not raw SQL
+- [ ] RAG advisor (`QuestionAnswerAdvisor`) attached to ChatClient — not manual context injection
+- [ ] Token usage logged at DEBUG, not INFO (avoid log noise)
+
+**LangChain4J:**
+- [ ] AI services use `@AiService` interface — not `ChatLanguageModel.generate()` directly
+- [ ] System prompts in `@SystemMessage` annotation — not hardcoded strings
+- [ ] Memory uses `MessageWindowChatMemory` or `TokenWindowChatMemory` — not unlimited history
+- [ ] Streaming via `StreamingChatLanguageModel` with `TokenStream` — not blocking
+- [ ] Embeddings via `EmbeddingModel` + `EmbeddingStore` for RAG — not in-memory list search
+- [ ] Tools annotated with `@Tool` on service methods — not manual function dispatch
+- [ ] API key from `@Value("${langchain4j.openai.api-key}")` — never literal
+
+---
+
+## Mode: `chat`
+
+User asks to add a basic chatbot or chat endpoint.
+
+### Spring AI
+1. Add dependency (see `references/patterns.md` → Spring AI Setup)
+2. Inject `ChatClient.Builder`, build a `ChatClient` bean
+3. Create `ChatController` with `@PostMapping("/chat")`
+4. Use `chatClient.prompt().user(message).call().content()` for simple response
+5. For streaming: return `Flux<String>` with `chatClient.prompt().user(message).stream().content()`
+6. Add `ANTHROPIC_API_KEY` / `OPENAI_API_KEY` to `application.yml` via `${env-var}`
+
+### LangChain4J
+1. Add `langchain4j-spring-boot-starter` + provider dependency
+2. Define `@AiService` interface with `@SystemMessage`
+3. Register as Spring bean via `AiServices.builder(MyAssistant.class).chatLanguageModel(model).build()`
+4. Expose via `@RestController`
+
+---
+
+## Mode: `rag`
+
+User asks to implement RAG (chat over documents, knowledge base, semantic search).
+
+### Spring AI RAG
+1. Choose vector store: PgVector (PostgreSQL), Chroma, Redis, Weaviate, Qdrant (see `references/patterns.md`)
+2. Add `spring-ai-{store}-store-spring-boot-starter`
+3. Ingest pipeline:
+   - `DocumentReader` (PDF, text, web) → `TokenTextSplitter` → `VectorStore.add()`
+   - Run at startup via `ApplicationRunner` or dedicated `@PostMapping("/ingest")`
+4. Query pipeline:
+   - Attach `QuestionAnswerAdvisor(vectorStore)` to `ChatClient`
+   - Spring AI auto-retrieves context and injects into prompt
+5. Tune: `SearchRequest.withTopK(5).withSimilarityThreshold(0.7)`
+
+### LangChain4J RAG
+1. Add `EmbeddingStore` (Chroma, Qdrant, in-memory for dev)
+2. `EmbeddingStoreIngestor` with `DocumentSplitter` and `EmbeddingModel`
+3. `EmbeddingStoreContentRetriever` → `RetrievalAugmentor` → `AiServices` builder
+
+---
+
+## Mode: `tools`
+
+User asks to give the AI the ability to call Java methods (function/tool calling).
+
+### Spring AI
+1. Define a `@Bean` of type `Function<Input, Output>` — Spring AI auto-registers it
+2. Or use `@Description` on a `record` parameter for rich schema
+3. Pass function names to `ChatClient`: `.options(OpenAiChatOptions.builder().withFunction("myFunction").build())`
+4. Spring AI handles the tool call loop automatically
+
+### LangChain4J
+1. Annotate service methods with `@Tool("description of what this tool does")`
+2. Register the service as a tool: `AiServices.builder(...).tools(myToolService).build()`
+3. The model decides when to call — no manual dispatch needed
+
+---
+
+## Mode: `memory`
+
+User asks to add conversation memory / chat history.
+
+### Spring AI
+- `MessageChatMemoryAdvisor` with `InMemoryChatMemory` for single-instance apps
+- `JdbcChatMemory` for persistent / multi-instance memory (requires `spring-ai-jdbc` store)
+- Key: pass `conversationId` (e.g., session ID or user ID) to scope memory per user
+
+### LangChain4J
+- `MessageWindowChatMemory.withMaxMessages(20)` — keeps last N messages
+- `TokenWindowChatMemory` — keeps messages within token budget
+- For persistence: implement `ChatMemoryStore` backed by Redis or JDBC
+
+---
+
+## Output format
+
+For **review mode**: list findings as `[CRITICAL] / [HIGH] / [MEDIUM] / [LOW]` with file:line references.
+
+For **implementation modes** (chat, rag, tools, memory):
+1. Show exact Maven/Gradle dependencies with versions
+2. Show full working code snippets (not pseudocode)
+3. Show `application.yml` configuration
+4. Note: state the minimum Spring Boot and Java version required
+
+Always note version-specific differences:
+- Spring AI 1.0.x (GA) vs 0.8.x (milestone) — API changes between these
+- LangChain4J 1.x vs 0.x — `AiServices` API changed in 1.x
+- Spring Boot 3.x required for Spring AI; Boot 2.x → use LangChain4J