Claude Code Template Repository for Spring Boot Applications
A developer has created a Claude Code template repository for generating Spring Boot applications. The template includes instructions, skills, and subagents for use with Claude Code.
Template Features
The repository contains best practices for several common development scenarios:
- Database integration
- Kubernetes deployment
- Integration testing with Testcontainers
According to the source, developers can clone the repository and immediately start generating desired applications with Claude Code. The template is designed to provide a starting point that incorporates established patterns for these technical areas.
Claude Code templates typically include predefined prompts, configuration settings, and example structures that help guide AI coding assistants to produce consistent, production-ready code. This specific template focuses on Spring Boot applications, which are commonly used for building Java-based web services and microservices.
The repository is available at: https://github.com/piomin/claude-ai-spring-boot
📖 Read the full source: r/ClaudeAI
👀 See Also
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AgentLens: Observability Tool for Multi-Agent AI Workflows
AgentLens provides unified tracing across Ollama, vLLM, Anthropic, and OpenAI, with cost tracking, an MCP server for querying stats from Claude Code, and a CLI for inline checks. It's self-hosted and runs locally via Docker.
AVP Protocol Enables LLM Agents to Share KV-Cache Instead of Text for Token Efficiency
AVP (Agent Vector Protocol) allows LLM agents to pass KV-cache directly between them instead of text, reducing token processing by 73-78% and achieving 2-4x speedups across Qwen, Llama, and DeepSeek models. The protocol works with HuggingFace and vLLM connectors and is available as a Python package.
Fullerenes: Open-source persistent memory layer for coding agents cuts tokens by 64% on SWE-bench
Fullerenes uses a local SQLite knowledge graph built via Tree-sitter to give coding agents like Claude Code persistent memory, reducing token usage by 64% on SWE-bench and up to 96.6% on internal benchmarks.