jabrena/plinth

Plinth is an AI-native engineering toolkit for modern Java enterprise SDLC, built around reusable Commands, Agents, Skills, and MCP Servers.

What it solves

Plinth for Java provides a structured, AI-native workflow for the Software Development Life Cycle (SDLC) of modern Java Enterprise applications. It aims to replace fragmented AI interactions with a consistent foundation of reusable skills, agents, and commands that guide AI assistants through analysis, design, implementation, and production operation.

How it works

The project provides a library of 13 commands, 9 agents, and 125 skills that can be installed into AI agents like Cursor, Claude Code, Codex, and GitHub Copilot. It establishes a standardized development pipeline:

  1. Analysis & Design: Uses commands like /explore-problem and /create-spec to turn ideas into functional and technical specifications (OpenSpec).
  2. Build: Implements these specifications using framework-aware delegation for Java environments (e.g., Spring Boot, Quarkus, Micronaut).
  3. Operate: Coordinates performance tuning and observability using commands like /profile and /benchmark.
  4. Compliance: Provides specialized skills to help engineers align Java systems with regulations like the EU AI Act and GDPR.

Who it’s for

Java Enterprise developers and software architects who use AI coding assistants and want a rigorous, professional engineering process rather than ad-hoc prompting.

Highlights

  • Comprehensive Tooling: Includes a vast library of skills and agents tailored for Java ecosystems (Maven, Spring Boot, etc.).
  • Structured Workflow: Defines a clear path from issue identification (Jira/GitHub) to implementation and closure.
  • OpenSpec Integration: Uses a validated specification format to ensure AI-generated code matches design requirements.
  • Regulation Awareness: Includes alpha-stage skills for engineering controls related to major EU digital regulations.
  • Multi-Agent Support: Compatible with Cursor, Claude Code, Codex, and GitHub Copilot.

Related

  • Project
    HoangNguyen0403/agent-skills-standard

    A portable SDLC standards layer for AI coding agents that turns engineering rules into modular, version-controlled skills to ensure consistent code quality while reducing token waste.

  • Project
    borghei/Claude-Skills

    A comprehensive open-source library of AI skills, personas, and Python tools that enables professional workflows across engineering, product management, and regulated compliance domains.

  • Project
    MengTo/Skills

    A curated library of procedural agent skills and prompt playbooks designed to help AI coding agents build high-quality UIs, games, and frontend systems consistently.

  • Project
    steipete/agent-scripts

    A centralized management system for shared AI agent instructions, reusable skills, and portable helper scripts across local workspaces.

  • Project
    microsoft/hve-core

    A prompt library and agentic SDLC framework for GitHub Copilot that provides specialized agents, reusable prompts, and coding instructions to make AI-assisted development repeatable and standards-aligned.