Skill UI

This guide outlines essential security protocols for integrating Fireflies.ai. It covers best practices for managing sensitive API keys (e.g., environment variables, pre-commit hooks), implementing robust HMAC-SHA256 webhook signature verification in multiple languages (TypeScript, Python), and correctly configuring transcript privacy levels to ensure data confidentiality. Ideal for developers setting up production-ready integrations.

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 skill acts as a specialized expert for transitioning codebases across various frameworks, languages, versions, and platforms. It generates detailed migration plans, automated migration scripts, comprehensive risk assessments, and rollback strategies. Use it when upgrading technology stacks or moving code between disparate systems to ensure minimal disruption and maintain core functionality.

This skill generates robust, type-safe client Software Development Kits (SDKs) for multiple languages (including Python, TypeScript, Go, and Java) directly from OpenAPI 3.0+ specifications. It automatically handles complex API patterns such as authentication (Bearer, OAuth2), automatic retries with exponential backoff, and sophisticated pagination helpers. Use this when you need to create standardized, idiomatic client libraries to consume a defined API contract across diverse development stacks.

A production-grade framework for executing high-performance, robust browser automation and web scraping using Playwright with the Go language. This skill specializes in handling complex Single Page Applications (SPAs), emulating human behavior (e.g., random typing delays, Bezier mouse movements), and implementing advanced stealth techniques to bypass anti-bot systems like Cloudflare. It emphasizes architectural best practices, such as using contexts for isolation and structured logging.

A comprehensive guide for migrating Large Language Model (LLM) integrations from providers like OpenAI or Anthropic to Groq. It details technical steps including SDK replacement, model ID mapping, building provider abstraction layers, and implementing zero-downtime traffic shifting using feature flags. This ensures developers can leverage Groq's superior speed while maintaining service continuity.

Automatically assists backend developers with gRPC service generation, offering best-practice patterns, multi-language scaffolds for Node.js, Python, Go, and validating configs for databases, caching, and messaging to stay production-ready.

Automate Gumroad storefront tasks via the Composio MCP integration, managing product listings, sales filters, license checks, and webhook subscriptions through natural language.

A comprehensive guide covering Internationalization (i18n) and Localization (L10n) best practices. It details how to build applications that can support multiple languages, regions, and text directions (RTL). Covers implementation patterns for React, Next.js, and Python, along with essential guidelines for handling date/number formats and managing translation keys.

Master the art of building robust, production-grade LLM chains using LangChain Expression Language (LCEL). This guide covers advanced composition patterns, including sequential, parallel, and conditional branching chains. It also details prompt templating, structured output parsing (JSON/Zod), and context injection, enabling the creation of complex, multi-step AI pipelines.

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.

A comprehensive runbook detailing standard operating procedures (SOPs) for managing production incidents involving LangChain and large language models (LLMs). It provides step-by-step guides for detecting, diagnosing, and mitigating common issues such as provider outages, high error rates, latency spikes, and excessive cost overruns. This skill is essential for DevOps and MLOps engineers maintaining LLM-powered applications.