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.

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.

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.

Validate and fix n8n expression syntax in workflows. Covers {{}} syntax, $json/$node variables, webhook data access, and common pitfalls like Code nodes and credential fields. Includes practical examples for webhook-to-Slack and HTTP-to-Email integrations.

A remote MCP server designed for AI agents to dynamically discover, inspect, and verify external tools, APIs, and services. It allows agents to search a curated index of AI-ready websites, check for machine-readable endpoints (like OpenAPI or MCP), and confirm if an endpoint responds correctly to JSON-RPC, enabling robust, dynamic agent workflow building.

This skill helps configure and optimize Nx monorepo workspaces. It covers setting up new workspaces, defining project boundaries, optimizing build caching, and implementing affected commands for CI. It includes best practices for library types like feature, ui, and data-access, along with configuration templates for nx.json and project.json to manage dependencies effectively.

This skill guides the creation of structured, importable JSON templates for the Obsidian Web Clipper. It facilitates the analysis of a target website's content structure, including DOM, Schema.org data, and CSS selectors. Users can map complex web elements into a robust template format, incorporating advanced logic like conditionals and loops, ensuring the resulting clipboard data is consistently formatted and ready for immediate use.

Expert guidance on Odoo's external JSON-RPC and XML-RPC APIs. Covers authentication, model calls, CRUD operations, and practical integration examples in Python, JavaScript, and curl. Includes best practices for security and performance.

This powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.