Skill UI

Learn how to build advanced, server-side business logic for Shopify using Shopify Functions and WebAssembly (WASM). This guide covers implementing custom discount rules, payment customizations, delivery option filtering, and cart transformations directly at the checkout. Designed for developers, it leverages the WASM sandbox for highly constrained, powerful e-commerce functionality.

The official SDK for interacting with the Webiny Headless CMS and File Manager from external applications (Next.js, Vue, Node.js). It provides a robust TypeScript interface for performing CRUD operations (list, get, create, update, publish), advanced filtering, sorting, and ensuring type safety across complex content models. Use this when building frontends or backends that need to consume Webiny content data.

A comprehensive, single-screen Kanban board prototype designed for visualizing agile project management and sprint tracking. It features distinct columns (Backlog, In Progress, Review, Done), draggable cards, assignee avatars, and advanced filtering options, making it ideal for managing product backlogs, simulating tools like Trello, or visualizing complex workflows.

A comprehensive, advanced playbook for conducting deep network traffic analysis and PCAP forensics. Covers essential skills like PCAP repair, advanced Wireshark filtering (HTTP, DNS, TLS), data extraction (file carving, credential harvesting), and detecting covert channels. It guides users through sophisticated techniques, including TLS decryption workflows and DNS tunneling identification.

Provides essential database abstractions and utility classes for robust API development. This module includes components like FilterUtil, ValueFilter, and ValueFilterRegistry, allowing developers to implement standardized, efficient, and complex data querying and filtering logic across various database contexts.

This guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

This skill parses raw, verbose Simplicité log files from TXT format, filtering out noise and extracting only essential fields (e.g., timestamp, level, body). By converting logs into structured JSON, it significantly reduces the token size required for AI context optimization, making troubleshooting efficient and reliable.

This skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).

A comprehensive guide to authoring, optimizing, and validating Power Query M expressions used within semantic model partitions. This resource covers crucial topics such as maintaining query folding for performance, applying early filtering and column pruning, and leveraging the Power Query API for robust data validation and debugging.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

This skill analyzes your Gmail inbox over a specified time window, filtering out noise such as newsletters, receipts, and automated notifications. It surfaces only actionable items—emails requiring a direct reply, a decision, or follow-up—and even drafts suggested replies for efficiency.

This skill enforces a rigorous data validation protocol by establishing hard boundaries at every point data enters the system (API requests, user input, environment variables, webhooks, etc.). It mandates a four-phase process: detection, trust classification, mandatory filtering (schema validation, sanitization, type checking), and final verification. This prevents untrusted data from reaching core business logic, mitigating risks like SQL injection and XSS.