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Modernize Java enterprise development workflows with AI-native, human-in-the-loop automation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "plinth" yet — see the docs or source repo.
Design a plinth-based AI-native workflow for a Java enterprise application team. Cover requirement breakdown, code generation, review, testing, release, and human approval checkpoints, and explain which Skills, Agents, Commands, or MCP servers fit each step.
A phased Java enterprise workflow plan with clear AI-human responsibilities and reusable components.
Using the plinth workflow approach, generate an implementation plan for an "order approval" module, including Spring Boot layered architecture, key interfaces, entity design, testing strategy, and which steps should be automated by Agents versus confirmed by developers.
An actionable module blueprint with code structure guidance, automation steps, and human review checkpoints.
Using plinth principles, design pre-release quality gates for a Java enterprise project. Combine code checks, test execution, change summaries, risk warnings, and human approval flow, and provide reusable command or Agent task templates.
A release-focused quality control design with automation task templates and approval recommendations.
Create objective-driven plans for agent-led development with an audit trail.
Lets AI agents control app windows for cross-platform UI automation tasks.
Give AI coding agents IDE intelligence and safe transactional code edits.
Build controllable, stateful, reusable AI workflows with a graph-native language.
Trace code from goals to requirements and surface orphaned code.
Use modular, validated skills and workflows to accelerate AI-assisted software development.