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.
SkillScore82Maintained
Automatic
◆AI Engineer pack
Last verified 8/21/2026
What it changes
Passed 18 of 22 test cases with the skill, and 14 of 22 without it.
Without skill64%
With skill82%
Checks it passed that the model alone missed
Includes dependency org.json:json for JSON payload manipulation.
Catches AwsServiceException to inspect HTTP response status code.
Uses the anthropic.claude-sonnet-4-5-20250929-v1:0 model identifier.
and 3 more in the full report
5 fixed13 passed either way3 still failing1 broke
Pass-rate lift
+18 pp
Tokens
+112%
Agent turns
0%
Response time
-20%
Model gemini-3.6-flashMeasured 8/21/2026v118 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
What is the specific Bedrock model identifier string used when targeting Claude Sonnet 4.5 in AWS Bedrock Java SDK InvokeModelRequest?
What was checked, without and with the skill
Checked
Without skill
With skill
Uses model identifier string anthropic.claude-sonnet-4-5-20250929-v1:0.
Missed
Passed
case-11 · gemini-3.6-flash · 8/21/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md346 lines13 files
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