Fhir Developer Skill

Build secure WordPress plugins with hooks, database interactions, Settings API, custom post types, and REST API. Covers Simple, OOP, and PSR-4 architecture patterns plus the Security Trinity. Includes WordPress 6.7-6.9 breaking changes. Use when creating plugins or troubleshooting SQL injection, XSS, CSRF, REST API vulnerabilities, wpdb::prepare errors, nonce edge cases, or WordPress 6.8+ bcrypt migration.

Premium FIFA World Cup 2026 market & match intelligence — a hosted, read-only layer that fuses official match truth (fixtures, standings, squads, injuries, player performance) with live prediction markets (Kalshi + Polymarket: prices, order books, price history, movers, cross-venue edges) and AI-grounded context (prematch briefs, move explanations, fan/social pulse). Every entity carries a canonical machina URN cross-walked across api-football, sportradar, opta, entain and ESPN, so a market resolves to a fixture resolves to two teams. This skill is prompt-only and premium: it routes the agent to the hosted World Cup Intelligence project (a per-project Machina MCP server) via `machina-cli`. It runs no code locally and ships no API keys. Use when: the user wants World Cup 2026 odds + match context together, asks "what moved and why", wants a grounded market brief or fan-sentiment read on a fixture, or needs one stable id that joins markets ↔ fixtures ↔ teams across providers. Don't use w

Create Convex queries, mutations, and actions with proper validation, authentication, and error handling. Use when implementing new API endpoints.

NHL data via ESPN public endpoints and the official NHL API — scores, standings, rosters, schedules, game summaries, injuries, futures, leaders, and news, plus an analytics backend: play-by-play with on-ice coordinates, cross-league career rows, skater/goalie leaders, and history to 1917. Zero config, no API keys. Use when: user asks about NHL scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, team/player statistics, NHL news, shot locations, career stats, skater/goalie leaders, or historical NHL seasons. Don't use when: user asks about other hockey leagues (AHL, KHL, college hockey). For other sports use: nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), mlb-data (MLB), football-data (soccer), tennis-data (tennis), golf-data (golf), cricket-data (cricket), cfb-data (college football), cbb-data (college basketball), fastf1 (F1). For betting odds use polymarket or kalshi. For news use sports-news.

Use this skill when you develop, design, build, deploy, debug, migrate, or troubleshoot CloudBase (腾讯云开发, 云开发, TCB, 微信云开发) projects. Covers Web, 微信小程序, 小程序, uni-app, mobile (iOS, Android, Flutter, React Native). UI (页面, 界面, 表单, form, dashboard, prototype, 原型); auth (登录, 注册, OAuth, 微信登录, publishable key); databases (NoSQL 文档数据库, MySQL 关系型数据库, PostgreSQL/CloudBase PG, app.rdb(), queryPgDatabase/managePgDatabase, CRUD, 查询, security rules); 云函数/cloud functions (serverless, scf_bootstrap); CloudRun (云托管, Dockerfile); 云存储. Built-in AI (内置大模型, AI 对话, streaming, 流式输出, 图片生成, generateText, streamText, createModel, generateImage, TokenHub, Hunyuan, hunyuan-exp, DeepSeek, deepseek, GLM, Kimi, Token Credits 资源包, 小程序成长计划). 第三方大模型, 大模型接入, 大模型调用, LLM API, AI agent, 智能体, AI Agent, AG-UI, LangGraph. Ops (巡检, 诊断, health check, 日志, troubleshooting). Spec (需求文档, 技术方案, requirements, tasks.md). Do NOT use for non-CloudBase projects, pure frontend without CloudBase, or self-hosted backends without CloudBase.

Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage AI Gateway rate limits; discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).