Skill UI

This advanced workflow executes sophisticated web data gathering tasks. It supports three primary functions: LLM-powered structured data extraction using custom JSON schemas or prompts; efficient batch scraping of multiple known URLs; and rapid site mapping to discover all reachable URLs on a domain. Use this when you require reliable, typed data structures rather than raw markdown content.

A comprehensive guide for migrating web scraping pipelines from tools like Puppeteer, Playwright, and Cheerio to Firecrawl. This skill demonstrates how to replace complex, browser-intensive code with simple API calls, handling single-page scraping, structured data extraction using LLMs, and large-scale site crawling, while eliminating the need for manual browser management and anti-bot workarounds.

Provides a robust, event-driven reference architecture for building meeting intelligence platforms using Fireflies.ai. This guide details the full production pipeline, covering multi-source meeting ingestion (Zoom, Teams), webhook processing, and subsequent data enrichment into structured action items, analytics reports, and database storage. Ideal for designing complex integrations or advanced meeting analytics systems.

This resource provides production-ready, type-safe GraphQL client patterns for integrating with the Fireflies.ai API in both TypeScript and Python. It addresses critical engineering concerns such as robust error handling, managing singletons, implementing multi-tenant support, and ensuring structured data validation using best practices.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

This structured workflow guides the systematic, low-risk modernization of legacy systems using the Strangler Fig pattern. It provides a phased approach, starting with comprehensive assessment (risk, dependency mapping), establishing robust test coverage (contract testing, data management), and implementing incremental migration infrastructure (API gateways, feature flags) to ensure continuous business operation during the transition.

Guidance for building robust GraphQL services: schema design with Relay-style pagination, resolver patterns that leverage per-request DataLoader for N+1 prevention, subscription handling, and structured error payloads to simplify client integrations.

Provides production-ready patterns for integrating with the Guidewire insurance platform. This SDK abstracts the complexity of hybrid APIs (REST/SOAP), offering a structured client, robust error handling (retry logic, conflict detection), and a fluent query builder for consistent data access across policies and claims.

This guide outlines the process of migrating codebase dependencies and logic when upgrading the Ideogram API from legacy versions (V1/V2) to the robust V3 endpoints. It addresses critical breaking changes, including the shift from JSON body to multipart/form-data, changes in model naming, aspect ratio syntax, and the integration of new V3 features like rendering speed and style presets. Provides structured code adapters for smooth transition.

A comprehensive guide to mastering asynchronous programming using Kotlin Coroutines and Flow. Covers advanced topics such as structured concurrency, robust exception handling, and creating reactive data streams with StateFlow and SharedFlow. Essential for developing high-performance, modern Kotlin applications.

This guide provides minimal working examples demonstrating the LangChain Expression Language (LCEL). It shows how to chain together diverse components—such as prompts, LLMs, and output parsers—using simple pipe syntax (e.g., .pipe() or |). Ideal for developers starting a new LangChain project, understanding core AI workflow patterns, or mastering structured data output.

This guide demonstrates the basic workflow for building an AI agent using a webhook trigger. The agent receives structured data (payload), utilizes an LLM to extract key information and generate a concise summary (summarization step), and finally executes an action by posting the summary to Slack or sending an email. Ideal for beginners learning about AI agent architecture, testing data flow, and implementing simple automated system notifications.