Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use this skill for Owl Admin development tools, code generator, dynamic API templates, relationships, visual pages, generated CRUD code, app/ApiTemplates, Support/Apis, Support/CodeGenerator, Controllers/DevTools, or questions about “代码生成器”, “动态 API”, “API 模板”, “关系管理”, “可视化页面”, and “生成 CRUD”.
Use this skill for Owl Admin extensions, modules, multi-admin setup, extension service providers, module routes, module config, menus, permissions, admin-module:init, or extensions under the configured extension directory. Trigger on 扩展, 插件, 多模块, module, extension, ServiceProvider, 菜单导入, or 模块后台.
Use this skill for Owl Admin frontend assets, admin-views, public/admin-assets, resource publishing, custom React frontend work, amis-editor frontend, layout/theme frontend files, pnpm commands, or questions about “前端资源”, “admin-views”, “自定义前端”, “发布 assets”, “前端源码”, and “打包后台前端”.
Tool lifecycle UI components for React/Next.js from ui.inference.sh. Display tool calls: pending, progress, approval required, results. Capabilities: tool status, progress indicators, approval flows, results display. Use for: showing agent tool calls, human-in-the-loop approvals, tool output. Triggers: tool ui, tool calls, tool status, tool approval, tool results, agent tools, mcp tools ui, function calling ui, tool lifecycle, tool pending
Declarative UI widgets from JSON for React/Next.js from ui.inference.sh. Render rich interactive UIs from structured agent responses. Capabilities: forms, buttons, cards, layouts, inputs, selects, checkboxes. Use for: agent-generated UIs, dynamic forms, data display, interactive cards. Triggers: widgets, declarative ui, json ui, widget renderer, agent widgets, dynamic ui, form widgets, card widgets, shadcn widgets, structured output ui
Build and run the Gridcoin Qt wallet GUI on Windows using WSL2 + WSLg, for iterating on the src/qt UI. Use when a Windows contributor asks to build, run, launch, or screenshot the Gridcoin GUI (gridcoinresearch). Windows-only — Linux/macOS contributors should build natively per doc/build.md.
Set up a rank_llm development environment. Use when someone is onboarding, setting up a fresh clone, choosing extras such as cloud, api, local, or pyserini, or troubleshooting whether the packaged rank-llm CLI is ready.
Best practices for polars data processing with dataframely. Covers definitions of Schema and Collection, usage of .validate() and .filter(), type hints, and testing. Use when writing or modifying code involving dataframely or polars data frames.
Add, remove, or adjust Markuplint rules for specific files or elements. Analyzes violations, proposes scope-appropriate configuration changes, and confirms with the user.