Skill UI

A framework-agnostic discipline ensuring type safety at the network edge of React apps. It establishes a single typed API client as the fetch boundary, enforcing parse-don't-validate rules to transform wire JSON into trusted domain types. Unified error handling and branded identifiers prevent silent bugs, creating a robust foundation for frontend architecture.

This skill provides a portable, framework-agnostic SEO system for React or React Native-for-web frontends. It centralizes site metadata in a constants module, derives canonical URLs, and generates per-route metadata including titles, descriptions, Open Graph, and Twitter cards. It also produces sitemaps, robots rules, RSS feeds, and typed JSON-LD without requiring manual meta tag copy-pasting.

A lightweight CRM for managing GEO agency prospects and clients through the full sales lifecycle. Track pipeline stages, run quick audits, add notes, and forecast revenue. All data persisted locally in JSON.

Audit, validate, and generate Schema.org JSON-LD markup to boost AI discoverability. Detects existing structured data, flags missing entity schemas, and outputs ready-to-use JSON-LD code with a GEO-SCHEMA-REPORT.md file.

Maps, explains, and lints repository-scoped coding-agent instruction files such as AGENTS.md, CLAUDE.md, Copilot and Cursor rules. Run local read-only scans to inventory instructions, audit recursive imports, and export JSON or SARIF diagnostics before changing code.

`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.

Aggregates official auctioneer information from all 27 Brazilian Commercial Boards into a SQLite store, exposes a FastAPI REST interface, and enables CSV/JSON exports plus customizable scraping per state.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

Removes Lovable.dev scaffolding from Vite + React projects including dependencies, branding, placeholder assets, and generated documentation. Helps developers ship clean, ownable codebases after exporting from Lovable. Audits package.json, vite.config.ts, index.html, and source files for Lovable fingerprints.

Use the mmx CLI to generate text, images, video, speech, and music via the MiniMax AI platform, and perform web searches and image understanding from the terminal. Supports async tasks, JSON output, and various models.

This skill provides expert guidance for writing JavaScript code within n8n Code nodes. It covers using $input, $json, and $node syntax, making HTTP requests with $helpers, and working with dates using DateTime. It helps with troubleshooting Code node errors and choosing between Code node modes. Use this for complex transformations, custom calculations, recursive operations, and data aggregation that simple nodes cannot handle.