Agent skill

AWS SDK Java V2 Bedrock

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

Provides Amazon Bedrock patterns using AWS SDK for Java 2.x.

MITAuto-check: notesAI & LLM Engineering

Install AWS SDK Java V2 Bedrock

skills CLI
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill aws-sdk-java-v2-bedrock -a claude-code

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

GitHub CLI
$ gh skill install giuseppe-trisciuoglio/developer-kit aws-sdk-java-v2-bedrock --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/aws-sdk-java-v2-bedrock .claude/skills/aws-sdk-java-v2-bedrock && 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
aws-sdk-java-v2-bedrock
GitHub stars
356
Token cost
~3.2k tokens
SKILL.md length
492 words
Files
13 (incl. references)
Skills in repo
115
Repo updated
First seen
Licence
MIT

At a glance

Provides Amazon Bedrock patterns using AWS SDK for Java 2.x.

  • Works in 8 steps: Configure AWS Credentials - Set up IAM… → Enable Model Access - Request access to… → Initialize Clients - Create reusable… → …
  • Asking about Bedrock integration
  • SKILL.md covers Overview, When to Use, Quick Start and Instructions, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AWS SDK Java V2 Bedrock is an agent skill from giuseppe-trisciuoglio/developer-kit. Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. Invokes foundation models (Claude, Llama, Titan), generates text and images, creates embeddings for RAG, streams real-time responses, and configures Spring Boot integration. Use when asking about Bedrock integration, Java SDK for AI models, AWS generative AI, Claude/Llama invocation, embeddings for RAG, or Spring Boot AI setup.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including reference files (for example `references/advanced-model-patterns.md`, `references/advanced-topics.md` and `references/aws-bedrock-api-reference.md`).

It sits in AI & LLM Engineering, covering Backend development, Embeddings and Retrieval-augmented generation. It works with Amazon Web Services, Java, Spring Boot and Amazon Bedrock. 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

  • Asking about Bedrock integration
  • Java SDK for AI models
  • AWS generative AI
  • Claude/Llama invocation

Example prompts

  • “Use the aws-sdk-java-v2-bedrock skill to provide Amazon Bedrock patterns using AWS SDK for Java 2.x”
  • “/aws-sdk-java-v2-bedrock”

Requirements

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

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Configure AWS Credentials - Set up IAM roles with Bedrock permissions (avoid access keys)
  2. Enable Model Access - Request access to specific foundation models in AWS Console
  3. Initialize Clients - Create reusable BedrockClient and BedrockRuntimeClient instances
  4. Validate Model Availability - Test with a simple invocation before production use
  5. Build Payloads - Create model-specific JSON payloads with proper format
  6. Handle Responses - Parse response structure and extract content
  7. Implement Streaming - Use response stream handlers for real-time generation
  8. Add Error Handling - Implement retry logic with exponential backoff

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 and xml).

    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

AWS SDK Java V2 Bedrock loads about 3.2k tokens when it runs, and up to ~130k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 492 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~104
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~130k

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). 492 words, ~3,227 tokens.

Download SKILL.mdSave it as .claude/skills/aws-sdk-java-v2-bedrock/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
aws-sdk-java-v2-bedrock
description
Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. Invokes foundation models (Claude, Llama, Titan), generates text and images, creates embeddings for RAG, streams real-time responses, and configures Spring Boot integration. Use when asking about Bedrock integration, Java SDK for AI models, AWS generative AI, Claude/Llama invocation, embeddings for RAG, or Spring Boot AI setup.
allowed-tools
Read, Write, Edit, Bash, Glob, Grep

AWS SDK for Java 2.x - Amazon Bedrock

Overview

Invokes foundation models through AWS SDK for Java 2.x. Configures clients, builds model-specific JSON payloads, handles streaming responses with error recovery, creates embeddings for RAG, integrates generative AI into Spring Boot applications, and implements exponential backoff for resilience.

When to Use

  • Invoke Claude, Llama, Titan, or Stable Diffusion for text/image generation
  • Configure BedrockClient and BedrockRuntimeClient instances
  • Build and parse model-specific payloads (Claude, Titan, Llama formats)
  • Stream real-time AI responses with async handlers and error recovery
  • Create embeddings for retrieval-augmented generation
  • Integrate generative AI into Spring Boot microservices
  • Handle throttling with exponential backoff retry logic

Quick Start

Dependencies
xml
<!-- Bedrock (model management) -->
<dependency>
    <groupId>software.amazon.awssdk</groupId>
    <artifactId>bedrock</artifactId>
</dependency>

<!-- Bedrock Runtime (model invocation) -->
<dependency>
    <groupId>software.amazon.awssdk</groupId>
    <artifactId>bedrockruntime</artifactId>
</dependency>

<!-- For JSON processing -->
<dependency>
    <groupId>org.json</groupId>
    <artifactId>json</artifactId>
    <version>20231013</version>
</dependency>
Client Setup
java
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrock.BedrockClient;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

// Model management client
BedrockClient bedrockClient = BedrockClient.builder()
    .region(Region.US_EAST_1)
    .build();

// Model invocation client
BedrockRuntimeClient bedrockRuntimeClient = BedrockRuntimeClient.builder()
    .region(Region.US_EAST_1)
    .build();

Instructions

Follow these steps for production-ready Bedrock integration:

  1. Configure AWS Credentials - Set up IAM roles with Bedrock permissions (avoid access keys)
  2. Enable Model Access - Request access to specific foundation models in AWS Console
  3. Initialize Clients - Create reusable BedrockClient and BedrockRuntimeClient instances
  4. Validate Model Availability - Test with a simple invocation before production use
  5. Build Payloads - Create model-specific JSON payloads with proper format
  6. Handle Responses - Parse response structure and extract content
  7. Implement Streaming - Use response stream handlers for real-time generation
  8. Add Error Handling - Implement retry logic with exponential backoff

Validation Checkpoint: Always test with a simple prompt (e.g., "Hello") before production use to verify model access and response parsing.

Examples

Text Generation with Claude
java
public String generateWithClaude(BedrockRuntimeClient client, String prompt) {
    JSONObject payload = new JSONObject()
        .put("anthropic_version", "bedrock-2023-05-31")
        .put("max_tokens", 1000)
        .put("messages", new JSONObject[]{
            new JSONObject().put("role", "user").put("content", prompt)
        });

    InvokeModelResponse response = client.invokeModel(InvokeModelRequest.builder()
        .modelId("anthropic.claude-sonnet-4-5-20250929-v1:0")
        .body(SdkBytes.fromUtf8String(payload.toString()))
        .build());

    JSONObject responseBody = new JSONObject(response.body().asUtf8String());
    return responseBody.getJSONArray("content")
        .getJSONObject(0)
        .getString("text");
}
Model Discovery
java
import software.amazon.awssdk.services.bedrock.model.*;

public List<FoundationModelSummary> listFoundationModels(BedrockClient bedrockClient) {
    return bedrockClient.listFoundationModels().modelSummaries();
}
Multi-Model Invocation
java
public String invokeModel(BedrockRuntimeClient client, String modelId, String prompt) {
    JSONObject payload = createPayload(modelId, prompt);

    InvokeModelResponse response = client.invokeModel(request -> request
        .modelId(modelId)
        .body(SdkBytes.fromUtf8String(payload.toString())));

    return extractTextFromResponse(modelId, response.body().asUtf8String());
}

private JSONObject createPayload(String modelId, String prompt) {
    if (modelId.startsWith("anthropic.claude")) {
        return new JSONObject()
            .put("anthropic_version", "bedrock-2023-05-31")
            .put("max_tokens", 1000)
            .put("messages", new JSONObject[]{
                new JSONObject().put("role", "user").put("content", prompt)
            });
    } else if (modelId.startsWith("amazon.titan")) {
        return new JSONObject()
            .put("inputText", prompt)
            .put("textGenerationConfig", new JSONObject()
                .put("maxTokenCount", 512)
                .put("temperature", 0.7));
    } else if (modelId.startsWith("meta.llama")) {
        return new JSONObject()
            .put("prompt", "[INST] " + prompt + " [/INST]")
            .put("max_gen_len", 512)
            .put("temperature", 0.7);
    }
    throw new IllegalArgumentException("Unsupported model: " + modelId);
}
Streaming Response with Error Handling
java
public String streamResponseWithRetry(BedrockRuntimeClient client, String modelId, String prompt, int maxRetries) {
    int attempt = 0;
    while (attempt < maxRetries) {
        try {
            JSONObject payload = createPayload(modelId, prompt);
            StringBuilder fullResponse = new StringBuilder();

            InvokeModelWithResponseStreamRequest request = InvokeModelWithResponseStreamRequest.builder()
                .modelId(modelId)
                .body(SdkBytes.fromUtf8String(payload.toString()))
                .build();

            client.invokeModelWithResponseStream(request,
                InvokeModelWithResponseStreamResponseHandler.builder()
                    .onEventStream(stream -> stream.forEach(event -> {
                        if (event instanceof PayloadPart) {
                            String chunk = ((PayloadPart) event).bytes().asUtf8String();
                            fullResponse.append(chunk);
                        }
                    }))
                    .onError(e -> System.err.println("Stream error: " + e.getMessage()))
                    .build());

            return fullResponse.toString();
        } catch (Exception e) {
            attempt++;
            if (attempt >= maxRetries) {
                throw new RuntimeException("Stream failed after " + maxRetries + " attempts", e);
            }
            try {
                Thread.sleep((long) Math.pow(2, attempt) * 1000); // Exponential backoff
            } catch (InterruptedException ie) {
                Thread.currentThread().interrupt();
                throw new RuntimeException("Interrupted during retry", ie);
            }
        }
    }
    throw new RuntimeException("Unexpected error in streaming");
}
Exponential Backoff for Throttling
java
import software.amazon.awssdk.awscore.exception.AwsServiceException;

public <T> T invokeWithRetry(Supplier<T> invocation, int maxRetries) {
    int attempt = 0;
    while (attempt < maxRetries) {
        try {
            return invocation.get();
        } catch (AwsServiceException e) {
            if (e.statusCode() == 429 || e.statusCode() >= 500) {
                attempt++;
                if (attempt >= maxRetries) throw e;
                long delayMs = Math.min(1000 * (1L << attempt) + (long) (Math.random() * 1000), 30000);
                Thread.sleep(delayMs);
            } else {
                throw e;
            }
        }
    }
    throw new IllegalStateException("Should not reach here");
}
Text Embeddings
java
public double[] createEmbeddings(BedrockRuntimeClient client, String text) {
    String modelId = "amazon.titan-embed-text-v1";

    JSONObject payload = new JSONObject().put("inputText", text);

    InvokeModelResponse response = client.invokeModel(request -> request
        .modelId(modelId)
        .body(SdkBytes.fromUtf8String(payload.toString())));

    JSONObject responseBody = new JSONObject(response.body().asUtf8String());
    JSONArray embeddingArray = responseBody.getJSONArray("embedding");

    double[] embeddings = new double[embeddingArray.length()];
    for (int i = 0; i < embeddingArray.length(); i++) {
        embeddings[i] = embeddingArray.getDouble(i);
    }
    return embeddings;
}
Spring Boot Integration
java
@Configuration
public class BedrockConfiguration {

    @Bean
    public BedrockClient bedrockClient() {
        return BedrockClient.builder()
            .region(Region.US_EAST_1)
            .build();
    }

    @Bean
    public BedrockRuntimeClient bedrockRuntimeClient() {
        return BedrockRuntimeClient.builder()
            .region(Region.US_EAST_1)
            .build();
    }
}

@Service
public class BedrockAIService {

    private final BedrockRuntimeClient bedrockRuntimeClient;
    private final ObjectMapper mapper;

    @Value("${bedrock.default-model-id:anthropic.claude-sonnet-4-5-20250929-v1:0}")
    private String defaultModelId;

    public BedrockAIService(BedrockRuntimeClient bedrockRuntimeClient, ObjectMapper mapper) {
        this.bedrockRuntimeClient = bedrockRuntimeClient;
        this.mapper = mapper;
    }

    public String generateText(String prompt) {
        Map<String, Object> payload = Map.of(
            "anthropic_version", "bedrock-2023-05-31",
            "max_tokens", 1000,
            "messages", List.of(Map.of("role", "user", "content", prompt))
        );

        InvokeModelResponse response = bedrockRuntimeClient.invokeModel(
            InvokeModelRequest.builder()
                .modelId(defaultModelId)
                .body(SdkBytes.fromUtf8String(mapper.writeValueAsString(payload)))
                .build());

        return extractText(response.body().asUtf8String());
    }
}

See examples directory for comprehensive usage patterns.

Best Practices

Model Selection
  • Claude 4.5 Sonnet: Complex reasoning, analysis, and creative tasks
  • Claude 4.5 Haiku: Fast and affordable for real-time applications
  • Llama 3.1: Open-source alternative for general tasks
  • Titan: AWS native, cost-effective for simple text generation
Show full SKILL.md (203 more words)Show less
Performance
  • Reuse client instances (avoid creating new clients per request)
  • Use async clients for I/O operations
  • Implement streaming for long responses
  • Cache foundation model lists
Security
  • Never log sensitive prompt data
  • Use IAM roles for authentication
  • Sanitize user inputs to prevent prompt injection
  • Implement rate limiting for public applications

Constraints and Warnings

  • Cost Management: Bedrock API calls incur charges per token; implement usage monitoring and budget alerts.
  • Model Access: Foundation models must be enabled in AWS Console; verify region availability.
  • Rate Limits: Implement exponential backoff for throttling; check per-model limits.
  • Payload Size: Maximum payload size varies by model; use chunking for large documents.
  • Streaming Complexity: Handle partial content and error recovery carefully.
  • Data Privacy: Prompts and responses may be logged by AWS; review data policies.
  • Credentials: Never embed credentials in code; use IAM roles for EC2/Lambda.

Common Model IDs

  • Claude Sonnet 4.5: anthropic.claude-sonnet-4-5-20250929-v1:0
  • Claude Haiku 4.5: anthropic.claude-haiku-4-5-20251001-v1:0
  • Llama 3.1 70B: meta.llama3-1-70b-instruct-v1:0
  • Titan Embeddings: amazon.titan-embed-text-v1

See Model Reference for complete list.

References

  • aws-sdk-java-v2-core - Core AWS SDK patterns
  • langchain4j-ai-services-patterns - LangChain4j integration
  • spring-boot-dependency-injection - Spring DI patterns

© 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 12 other files (references) in plugins/developer-kit-java/skills/aws-sdk-java-v2-bedrock of giuseppe-trisciuoglio/developer-kit.

  • SKILL.md
  • references/advanced-model-patterns.md
  • references/advanced-topics.md
  • references/aws-bedrock-api-reference.md
  • references/aws-bedrock-user-guide.md
  • references/aws-sdk-examples.md
  • references/aws-sdk-java-bedrock-api.md
  • references/bedrock-code-examples.md
  • references/bedrock-models-supported.md
  • references/bedrock-runtime-code-examples.md
  • references/model-reference.md
  • references/models-lookup.md
  • references/testing-strategies.md

Open the folder on GitHubat commit fe73fb3

Compare with similar skills

AWS SDK Java V2 Bedrock next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AWS SDK Java V2 Bedrock compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AWS SDK Java V2 Bedrock this skillgiuseppe-trisciuoglio/developer-kit356—~3.2kAutomated safety check: NotesMIT
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Spring AI Integrationrrezartprebreza/spring-boot-skills298—~2.1kAutomated safety check: PassMIT
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AI SDK Developmenttrypostit/trypost6851 repos~3.5kAutomated safety check: PassMIT
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Questions about AWS SDK Java V2 Bedrock

What does AWS SDK Java V2 Bedrock do?

Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. AWS SDK Java V2 Bedrock is an agent skill from giuseppe-trisciuoglio/developer-kit.x.

When should I use AWS SDK Java V2 Bedrock?

AWS SDK Java V2 Bedrock fits situations like: asking about Bedrock integration; java SDK for AI models; AWS generative AI; Claude/Llama invocation.

How do I install AWS SDK Java V2 Bedrock in Claude Code?

Run `npx skills add giuseppe-trisciuoglio/developer-kit --skill aws-sdk-java-v2-bedrock -a claude-code`. Or copy the skill folder (plugins/developer-kit-java/skills/aws-sdk-java-v2-bedrock in giuseppe-trisciuoglio/developer-kit) into .claude/skills/aws-sdk-java-v2-bedrock in your project. Claude Code loads it when a task matches its description.

How do I install AWS SDK Java V2 Bedrock in Codex?

Run `npx skills add giuseppe-trisciuoglio/developer-kit --skill aws-sdk-java-v2-bedrock -a codex`. Or copy the skill folder (plugins/developer-kit-java/skills/aws-sdk-java-v2-bedrock in giuseppe-trisciuoglio/developer-kit) into .agents/skills/aws-sdk-java-v2-bedrock in your project. Codex loads it when a task matches its description.

Can I use AWS SDK Java V2 Bedrock 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 aws-sdk-java-v2-bedrock -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aws-sdk-java-v2-bedrock, .gemini/skills/aws-sdk-java-v2-bedrock, .github/skills/aws-sdk-java-v2-bedrock and .opencode/skills/aws-sdk-java-v2-bedrock in your project.

What does AWS SDK Java V2 Bedrock need to run?

SKILL.md names no scripts, command-line tools or credentials: AWS SDK Java V2 Bedrock is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Glob, Grep.

Does AWS SDK Java V2 Bedrock 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 AWS SDK Java V2 Bedrock 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 AWS SDK Java V2 Bedrock use?

AWS SDK Java V2 Bedrock 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 AWS SDK Java V2 Bedrock use?

About 3.2k tokens (SKILL.md is roughly 13k 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 126k tokens, read only when the agent opens those files.

What are the alternatives to AWS SDK Java V2 Bedrock?

Skills that share tags, products or a category with AWS SDK Java V2 Bedrock: Spring AI Integration (rrezartprebreza/spring-boot-skills, 298 stars), Spring AI Integration (rrezartprebreza/spring-boot-skills, 298 stars), Solon Development Skill (opensolon/soloncode, 197 stars) and AI SDK Development (trypostit/trypost, 685 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AWS SDK Java V2 Bedrock?

giuseppe-trisciuoglio (a GitHub user) maintains it in giuseppe-trisciuoglio/developer-kit, which has 356 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.