Skill UI

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 acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.

This skill generates a comprehensive, executable plan for paid social advertising campaigns. It guides users through defining campaign objectives (e.g., lead generation, brand awareness), structuring the marketing funnel (TOFU, MOFU, BOFU), and performing detailed audience targeting on platforms like Meta, LinkedIn, and TikTok. The output includes ad set architecture, budget allocation, and specialized ad copy/creative briefs for various formats, making it immediately actionable for media buyers and marketing teams.

This skill offers a comprehensive, framework-agnostic SEO workflow designed for sites with numerous tool, product, or feature pages. It addresses critical issues like duplicate content, generic meta tags, poor heading structure, and weak internal linking. By implementing strategies like the Content Registry pattern and boosting E-E-A-T, it helps large sites (50-500 pages) improve overall search rankings.

This meta-skill provides a comprehensive framework for the entire lifecycle of AI agent skills. It supports creating, refactoring, rigorously evaluating (e.g., Skill OS 2.0 Gates), and securely packaging skills from various sources like workflows, prompts, and documents. It ensures standardized, team-ready, and governed deployment of advanced AI capabilities.

This skill guides developers through the complex process of integrating an iOS App Clip target into an existing Expo project. It covers setting up the necessary bundle identifiers, configuring associated domains (AASA file), and deploying the required metadata to allow the lightweight clip to be invoked directly from a URL alongside the parent application.

A comprehensive guide and skill for deploying large language models (LLMs) locally using the llama.cpp framework and GGUF quantization format. It covers searching the Hugging Face Hub, selecting optimal quantizations (e.g., Q4_K_M), running local servers, and achieving OpenAI-compatible API endpoints. Supports various hardware backends including CPU, Mac Metal, CUDA, and ROCm. Ideal for developers needing offline, private LLM inference.

A systematic skill designed to investigate data incidents, including freshness delays, volume anomalies, schema changes, and ETL failures. It guides the agent through a comprehensive process using Monte Carlo's observability metadata and optional direct data querying to pinpoint the root cause.

Riffkit analyzes the emotional structure, pacing, and successful 'formula' of a winning short video (like a viral TikTok). It then regenerates an entirely new, original video using this proven formula, customized with your product, character, and desired language (English or Spanish). This skill is ideal for creating high-quality, scalable User-Generated Content (UGC) ad creatives or marketing promos for platforms like TikTok and Meta.

A meta-skill designed to capture hard-won, non-obvious knowledge—such as complex debugging steps, multi-step operational workflows (e.g., deployment, migrations), or project-specific facts—and convert them into reusable Agent Skills. It ensures that future sessions, regardless of the user or team member, start with proven procedures, preventing the need to rediscover solutions from scratch.

This meta-skill acts as a routing layer for complex UI development tasks. It guides the process of selecting the smallest, most specific, and useful skill context using the ui-skills CLI. Use it when starting UI work to analyze goals, identify the correct category, and pinpoint the precise skill set needed for implementation, ensuring development efficiency and accuracy.

This skill guides the creation of comprehensive, store-compliant metadata and visual assets for mobile app listings (App Store/Google Play). It covers writing optimized copy (names, descriptions, keywords) adhering to strict character/byte limits, and preparing all required visual assets (icons, screenshots, feature graphics, videos) to exact pixel specifications. Essential for launching a market-ready app.