Skill UI

`jq` is the standard and powerful CLI tool for querying, filtering, and reshaping JSON data. This skill provides expert-level practical patterns for working with deeply nested structures, handling complex transformations (like flattening, renaming, and grouping), and integrating JSON logic into shell pipelines. Essential for parsing API outputs (AWS, GitHub), log files, and building robust Bash scripts.

Generates implementation-ready JSON-LD schema markup for 10 types (e.g., FAQPage, Product, Article). It validates all fields against Google rich result eligibility rules, flags missing data with fix instructions, and provides clean, ready-to-paste script blocks to optimize search engine visibility and achieve rich results.

CLI helper that uploads local Apps Script assets (.gs, .js, .html, appsscript.json) to a project via `gws script +push`, replacing every file while skipping hidden folders and node_modules; specify the target script ID and optional directory.

A robust CLI tool for connecting to Lark/Feishu real-time events. It allows streaming, subscribing, and consuming various events (e.g., IM messages, reactions, meeting updates) as NDJSON output. Ideal for developing Lark bots, building real-time message processors, or implementing long-running streaming webhook handlers, especially in AI agent subprocesses.

Provides an end-to-end kit for building Microsoft 365 Copilot declarative agents across basic, enterprise, and validation workflows, with TypeSpec definitions, JSON schema validation, and Agents Toolkit integration for planning, testing, deployment, and optimization.

A comprehensive tool for detecting, validating, and generating Schema.org structured data. It primarily focuses on the JSON-LD format, which is preferred for modern SEO best practices and enabling rich results. Use this when optimizing content markup, checking for validation errors, or generating structured snippets for articles, businesses, or products.

This comprehensive guide details the process of managing BambooHR API changes, addressing breaking changes, and migrating from legacy patterns. It covers critical tasks such as converting XML to JSON, updating deprecated field usages (e.g., 'bestEmail' to 'workEmail'), and implementing robust testing and rollback strategies for seamless integration.

Automatically scaffolds the initial project specification and requirements documents (spec.json and requirements.md). It guides the user through scope definition by checking existing project briefs, clarifying intent (who, current state, desired change), and generating a structured, ready-to-use foundation for development planning. Ideal for starting any new feature or project.

Execute comprehensive document workflows using Adobe PDF Services API. This skill allows you to convert content (HTML/DOCX) into PDFs, extract structured text and tables from existing PDFs using Sensei AI, generate dynamic documents from Word templates, and convert PDFs to Markdown. Ideal for building robust document automation pipelines and advanced content extraction solutions.

This resource provides production-ready architectural patterns for integrating Anthropic's Claude models using TypeScript and Python. It covers robust client wrappers, managing multi-turn conversations, enforcing structured JSON output via prefilling, and implementing multi-tenant architectures. Essential for building reliable, enterprise-grade AI applications.

This workflow is designed for developers building design-to-code pipelines. It connects to Figma via API to extract core design tokens, including colors, typography styles, and spacing values. It converts these raw design assets into structured code formats, such as CSS custom properties, JSON tokens, and Tailwind configurations, ensuring design consistency across development environments.

Generates high-quality, deterministic, and editable vector diagrams (SVG) for system architecture, workflows, and pipelines. By using a structured JSON specification (FigureSpec), it ensures that the same input always yields the identical output, making it ideal for reproducible technical documentation, academic papers, and formal system modeling where precise layout and connections are critical. It is optimized for non-data-plot, formal diagrams.