Agent Skills
Agent Skills teach AI coding assistants how to build webforJ applications using the correct APIs, design tokens, and component patterns. Instead of guessing at framework conventions, the assistant loads a skill and follows a structured workflow to produce code that compiles and follows best practices on the first attempt.
The skills below ship inside the webforJ AI plugin together with the MCP server. One install gives your assistant both pieces.
Skills follow the open Agent Skills standard and work across many AI assistants, including Claude Code, GitHub Copilot, Cursor, Gemini CLI, OpenAI Codex, and more. A skill tells the assistant what kind of task it handles; the assistant loads it automatically when your prompt matches. For example, asking "theme this app with a blue palette" triggers the webforj-styling-apps skill, which walks the assistant through looking up valid DWC tokens, writing scoped CSS, and validating every variable name before writing anything to disk.
Why use skills?
The MCP server makes accurate webforJ information available on demand, but on its own it doesn't tell the assistant when to look something up, which approach fits the task, or what order to do things in. That's where skills come in.
Skills give the assistant a task-specific playbook: how to classify the work in front of it, which webforJ patterns fit, which MCP tools to consult at each step, and how to validate the output before handing it back. The result is consistent, convention-following webforJ code rather than a collection of technically valid but stylistically mismatched snippets.
How skills differ from MCP
Skills and the webforJ MCP server serve complementary roles. The MCP server provides live tools the assistant can call to fetch information or generate output. Skills provide the workflow that tells the assistant when to reach for those tools, what order to do things in, and how to validate the result.
| MCP server | Agent Skills | |
|---|---|---|
| What it provides | Tools the assistant calls on demand (doc search, scaffolding, theme generation, token validation) | Workflows and decision tables that guide how the assistant approaches a task |
| When it acts | When the assistant decides to call a tool | Automatically, when the assistant detects a matching task |
| Best for | Answering specific questions, generating artifacts | End-to-end tasks that need a consistent webforJ approach |
In practice the two work best together - and the webforJ AI plugin ships them as one install.