#skills

Plinth is an AI-native engineering toolkit for the modern Java Enterprise SDLC, built around reusable Commands, Agents, Skills, and MCP Servers. You can use this project to provide an AI-native workflow to your Java Engineering teams, or just use it to refactor aspects of your project with one of the most popular Agent Skill collections for Java Enterprise development in marketplaces like Skills.sh...

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This is the second article in a series that tries to put a number on the value of Plinth's AI-native development workflow for Java. The Part 1 article showed the benchmark results across 4 scenarios: ScenarioWhat the agent gets scenario1A minimal README only — baseline, sparsest possible brief scenario2A full functional-requirements package: user story, Gherkin, OpenAPI, ADRs scenario3An OpenSp...

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Plinth is an AI-native engineering toolkit for modern Java enterprise SDLC, built around reusable Commands, Agents, Skills, and MCP Servers. This release focuses on two things. First, Plinth now treats specification as two explicit phases — Functional and Technical — with new commands and skills that turn a raw issue into agreed requirements and an approved design before any code gets written. Seco...

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The gap AI agents fall into When you ask an AI agent to "add rate limiting to the API" and it will produce something. It might even compile, pass a quick test, and look complete in the diff. But what does "rate limiting" mean here? Per user, per API key, per IP? What happens when the limit is hit — a 429, a queue, a silent drop? Is this a business rule with a specific number att...

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The questions behind the benchmark During the last months, the project has evolved from a complete Skills folder for Java to provide an AI-native development workflow for Java, more or less this project lives in the Third generation (Systemp prompts -> Skills -> AI-Native development Workflow). During this time, any inquisitive user could ask for evidences about what is the real value using t...

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Plinth is an AI-native engineering toolkit for modern Java enterprise SDLC, built around reusable Commands, Agents, Skills, and MCP Servers. This release brings several improvements to the development workflow, but the most visible change is the new project identity: the repository moves from cursor-rules-java to plinth. Thanks to our community members in Urumqi, Singapore, Des Moines, Bengaluru, a...

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The hidden risk in fast code generation AI coding tools are very good at producing a complete-looking change from a short request. That is useful when the change is local. It becomes risky when the change crosses compatibility boundaries: database schemas, public APIs, event contracts, configuration keys, command outputs, generated artifacts, or workflows consumed by other services. A human enginee...

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Compliance as code The first article introduced the EU regulatory skill family and focused on broad digital regulation: AI systems, operational resilience, privacy, cybersecurity, product security, data access, online platforms, and digital markets. This second article explains the remaining regulation and ISO skills added for the future 0.17.0 release, and connects them to a more specialized idea:...

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The shift from generation to engineering The first wave of AI-assisted development was mostly about speed. Ask the model for a class, a test, a migration, a controller, a README section, or a refactoring, then review what came back. That capability is useful, but it is not the same as software engineering. Software engineering is not only the ability to produce code. It is the ability to change a s...

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What is the purpose? An opinionated, AI-native development workflow for Java Enterprise: reusable Skills, Agents, Commands, and third-party MCP servers combined with a human-in-the-loop model to modernize real-world SDLC practices. Starting with this release, the project introduces a simple way to describe any SDLC action through three phases: Plan, Build, and Operate. Software engineers can use th...

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Why validate Skills? Skills are executable guidance for AI agents. They can include instructions, references, scripts, and workflows that influence how an agent reads files, runs commands, edits code, or reasons about a software delivery process. That power is useful, but it also means a generated skill should not be treated as ordinary Markdown. A broken document can confuse an agent. A malformed ...

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Why this topic matters Modern Java Enterprise systems are no longer reviewed only for code quality, performance, and availability. Many systems now process personal data, call AI models, automate decisions, operate critical services, integrate with cloud providers, expose online platform workflows, or participate in digital markets. That means engineering teams need a practical way to translate reg...

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🎯 Learning Objectives By the end of this module, you will: Master Maven best practices using automated Agent Skills Configure quality dependencies for enterprise-grade projects Generate professional documentation automatically Understand the fundamentals of AI-powered development workflows Apply Agent Skills effectively in real-world scenarios 📚 Module Overview Duration: 4 hours Difficulty: Beg...

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🎯 Course Overview Transform your Java development workflow by learning how to select, invoke, and combine Plinth's Java Agent Skills. The course uses progressive, hands-on exercises to improve Maven builds, design, testing, security, modern Java, performance, profiling, and documentation. Rather than treating a skill as a reusable prompt fragment, each module treats it as an executable workflow wi...

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🎯 Learning Objectives By the end of this module, you will: Select skills by engineering outcome instead of by familiar terminology Sequence skills so each phase produces trusted inputs for the next Distinguish independent checks that can run in parallel from dependent changes Preserve human decisions, repository constraints, and validation evidence Design a complete Java delivery workflow without...

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🎯 Learning Objectives By the end of this module, you will: Generate comprehensive project documentation using 170-java-documentation Create professional UML diagrams using 033-architecture-diagrams Master documentation-driven development practices Apply modular, step-based documentation approaches Create maintainable, professional documentation that scales with projects 📚 Module Overview Durati...

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🎯 Learning Objectives By the end of this module, you will: Create JMeter performance tests using 151-java-performance-jmeter Profile applications comprehensively using 161-java-profiling-detect Analyze performance bottlenecks using 162-java-profiling-analyze Compare performance improvements using 164-java-profiling-verify Benchmark code with JMH for micro-optimizations Apply systematic performanc...

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🎯 Learning Objectives By the end of this module, you will: Master Java Generics from basics to advanced patterns using 128-java-generics Apply functional programming techniques effectively using 142-java-functional-programming Implement functional exception handling with monads using 143-java-functional-exception-handling Leverage data-oriented programming with records and sealed types using 144-...

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🎯 Learning Objectives By the end of this module, you will: Implement security best practices using automated Agent Skills Handle concurrency safely with modern Java patterns Apply proper logging strategies for production applications Master exception handling techniques for robust applications Create secure, resilient code that follows industry standards 📚 Module Overview Duration: 4 hours Diff...

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🎯 Learning Objectives By the end of this module, you will: Generate comprehensive unit tests using Java Agent Skills Apply object-oriented design principles systematically Implement type-safe design patterns effectively Create maintainable, testable code following industry standards Understand the relationship between testing and good design 📚 Module Overview Duration: 5 hours Difficulty: Inter...

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