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复制前请先查看来源、License 和安全提示。
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用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
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An opinionated AI-native workflow for evolving modern Java Enterprise SDLC practices through reusable Skills, Agents, Commands & MCP servers.
A
plinthrepresents the solid foundation or platform used to support statues or artworks in art and sculpture. It served as a structural and symbolic foundation for columns, statues, and entire temple podiums. Romans inherited the idea from Greek architecture but expanded its use to emphasize monumentality, hierarchy, and imperial power.
Explore the latest published content on https://jabrena.github.io/plinth/ and follow its evolution through new skills, improvements, and fixes in the CHANGELOG.
Install every skill for your preferred agent:
npx skills add jabrena/plinth --skill '*' --agent cursor -y
npx skills add jabrena/plinth --skill '*' --agent claude-code -y
npx skills add jabrena/plinth --skill '*' --agent codex -y
npx skills add jabrena/plinth --skill '*' --agent github-copilot -y
Install every command for your prefered agent:
install @004-commands-installation cursor
install @004-commands-installation claude-code
install @004-commands-installation codex
install @004-commands-installation github-copilot
Install every agent for your prefered agent:
install @005-agents-installation cursor
install @005-agents-installation claude-code
install @005-agents-installation codex
install @005-agents-installation github-copilot
You can use the project in 2 ways:
Prepare the repository with /onboarding, then identify an issue in your Kanban dashboard from Atlasian Jira, Github Issues or Azure DevOps and apply the following workflow:
/onboarding
|
v
Issue
|
v
/update-issue --> /explore-problem --> /create-acceptance-criteria
|
v
/create-spec --> /explore-design
|
v
/implement-spec --> /close-spec
/onboarding establishes root AGENTS.md and one unambiguous OpenSpec project before issue selection. It preserves existing prerequisites; when OpenSpec is missing, you select its result path with documentation/openspec as the default.
Turn an idea into an actionable change with user stories, GitHub Issues or Jira, ADRs, diagrams, AI plan mode, and OpenSpec.
Functional Specification:
| Command | Explanation |
|---|---|
/onboarding | Establish root repository guidance and one unambiguous OpenSpec project before issue work. |
/update-issue | Update an existing GitHub or Jira issue with a structured user story, acceptance criteria, and resource content. |
/explore-problem | Evaluate an issue from five perspectives and post a Functional Specification comment on the issue. |
/create-acceptance-criteria | Derive Gherkin acceptance criteria from a Functional Specification and post them as a separate issue comment. |
Technical Specification:
| Command | Explanation |
|---|---|
/create-adr (Optional) | Record an architectural decision, its alternatives, rationale, and consequences. |
/create-diagram (Optional) | Create a focused architecture or design diagram from approved artifacts. |
/create-spec (OpenSpec) | Create or update one or more validated OpenSpec changes. |
/explore-design | Compare technical approaches and obtain an approved design direction. |
Implement and improve Java applications with Maven, design, coding, testing, security, documentation, Spring Boot, Quarkus, Micronaut, OpenAPI, and WireMock guidance.
| Command | Explanation |
|---|---|
/implement-spec | Deliver an approved plan or validated OpenSpec task list through framework-aware delegation. |
/close-spec | Archive an OpenSpec change by name using the OpenSpec CLI. |
Measure and improve production behavior through observability, profiling, benchmarking, and performance testing.
| Command | Explanation |
|---|---|
/profile | Coordinate Java profiling from baseline detection through verified optimization. |
/benchmark | Select and coordinate JMeter, Gatling, or JMH performance workflows. |
Review Java systems, AI models, and how GenAI tools are used across applications and delivery pipelines for regulation-aware engineering controls, evidence, and qualified owner handoffs spanning AI, data, security, product, platform, market, and governance. These skills support engineering awareness and do not provide legal advice.
| Regulation | Skill |
|---|---|
| EU AI Act | 801-regulations-eu-ai-act |
| DORA | 802-regulations-dora |
| GDPR | 803-regulations-gdpr |
| NIS2 | 804-regulations-eu-nis2 |
| Cyber Resilience Act | 805-regulations-eu-cyber-resilience-act |
| Data Act | 806-regulations-eu-data-act |
| Digital Services Act | 807-regulations-eu-digital-services-act |
| Digital Markets Act | 808-regulations-eu-digital-markets-act |
| MiFID II | 810-regulations-eu-mifid-ii |
| Market Abuse Regulation | 811-regulations-eu-market-abuse-regulation |
| Product Liability Directive | 812-regulations-eu-product-liability-directive |
Note: This set of skills could be a good complement for the future OWASP EU Compliance MCP.
Ask your agent:
Use @110-java-maven-best-practices to review this Maven project located in examples/@maven/maven-demo
Explain the findings, apply the approved improvements, and validate the build.
The skill guides the agent through a structured Maven review while keeping you in control of proposed changes.
Learn to use this project following the quick guide Getting Started in 5 minutes.
Explore the complete Commands, Agents, Skills, and MCP Servers inventories.
The project generates a set of deliverables at the end of any iteration.
| Inventory | Installation | Getting Started |
|---|---|---|
| 1. Commands | @004-commands-installation Install Commands in project | Commands |
| 2. Agents | @005-agents-installation Install Agents in Cursor/Claude | Agents |
| 3. Skills | npx skills add jabrena/plinth --skill '*' --agent cursor -y | Skills |
This project is compatible with any tool that supports Commands, Agents, Skills, MCP Servers and AGENTS.md.
Every push runs the following validation checks in the Skill Scanners as part of the CI Pipeline to keep documentation and generated skills correct, consistent, and secure:
| Name | Purpose |
|---|---|
| 1. MarkdownValidator | Protects the documentation layer by catching Markdown parsing drift and remote link failures before skill-specific checks run. |
| 2. skill-check | Confirms every generated skill follows the expected packaging contract, complementing scanners that focus on behavior or security risk. |
| 3. cisco-ai-skill-scanner by Cisco | Adds behavior-oriented security coverage by looking for risky skill flows that structural validation cannot see. |
| 4. SkillSpector by NVIDIA | Provides an independent static quality and security review, useful for comparing findings against the other scanners. |
| 5. Snyk Agent Scan by SNYK | Focuses on agent-skill supply-chain and prompt-risk signals, adding another security perspective alongside Cisco and SkillSpector. |
From the outset, be aware that results from interactions with these Skills and agents are not deterministic because of how the models behave, but you can mitigate that with clear goals and validation checkpoints.
Some interactive skills require Premium models for interactive use; otherwise they follow a fixed sequence of steps.
Models can generate code, but they cannot execute it against your local data. To bridge that gap, some Skills include scripts you run locally.
This project supports software engineering work; it does not replace engineering judgment. A software engineer must review, guide, and validate AI-generated decisions, code, and outcomes before they are used.
Use caution when a problem involves corporate databases or other sensitive organizational data. Before granting an AI-assisted workflow access, assess authorization, privacy, data leakage, retention, and unintended modification risks. Apply least-privilege access, human review, validation, and monitoring. See OWASP GenAI Data Security Risks & Mitigations 2026, and the new set of skills about EU regulation.
See CONTRIBUTING.md for ways to support and improve the project.
Java uses JEPs (JDK Enhancement Proposals) to describe new language and platform features. This repository tracks which JEPs could improve the Skills and guidance here.
Talks, articles, reference links, skill portals, and related projects live in Project references.
Developed by humans with support from Cursor and Codex, with ❤️ from Madrid
name: 023-assumption-analysis
description: Use when a framed and root-caused problem needs its assumptions made explicit before design or planning begins — explicit Assumptions, Unknowns, and a Validation plan. This should trigger when an issue's Assumption Analysis point of view needs evaluation, or when a maintainer directly asks to surface hidden assumptions and unknowns before committing to an approach. Part of Plinth Toolkit
license: Apache-2.0
metadata:
author: Juan Antonio Breña Moral
version: 0.18.0Guide production of explicit Assumptions, a list of Unknowns, and a Validation plan for a problem under exploration. This is an interactive SKILL.
What is covered in this Skill?
024-context-mapping and the remaining Functional Specification lensesMake assumptions and unknowns explicit before they become undiscussed risk. When this technique is orchestrated by another workflow, the orchestrator owns clarifying-question sequencing; when applied standalone, ask directly.
references/023-assumption-analysis.md before applying Assumption Analysis guidanceRead references/023-assumption-analysis.md, then review the problem frame and root-cause findings for implicit beliefs.
State each assumption as a falsifiable claim believed true but not yet verified.
List facts that are not yet known either way, distinct from assumptions.
Rank assumptions and unknowns by impact if wrong and by current confidence, prioritizing high-impact, low-confidence items.
Name how and when each high-priority assumption or unknown will be validated.
Report the Assumptions, Unknowns, and Validation plan, and flag any item left open pending a clarifying answer.
For detailed guidance, examples, and constraints, see references/023-assumption-analysis.md.
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