Skill UI

This comprehensive guide helps developers safely migrate between different versions of the Kling AI video generation model (e.g., v1.x to v2.x). It details breaking changes, parameter differences, feature availability matrices (including native audio and camera control), and provides code examples for parallel A/B testing and robust rollback strategies, ensuring seamless API adoption.

Track running experiments by inspecting remote screen sessions, gathering logs, parsing JSON outputs, and optionally pulling WandB metrics before summarizing baselines, deltas, and costs for Vast.ai or Modal runs.

This autonomous skill orchestrates the entire scientific research lifecycle, guiding users from a vague research direction to a polished, review-ready document. The pipeline systematically executes Idea Discovery (Workflow 1), full-scale Implementation, Experiment Deployment (Workflow 2), automated Review Loops, and optional Paper Writing (Workflow 3). It handles everything from preliminary literature review and novelty checks to generating high-quality, reproducible code and final academic drafts.

This skill creates high-quality videos from structured JSON prompts and optional reference images, then runs the prescribed Python script to produce MP4 output tailored to the requested scene, style, and technical specs.

This comprehensive guide details the necessary steps and breaking changes when migrating from Cohere API v1 to v2. It covers critical updates for SDKs (TypeScript/Python), including changes in chat message formats, role naming conventions, and required parameters for key endpoints like Embed, Rerank, and Tool Use, ensuring smooth API adoption.

Provides comprehensive intelligence access across news, finance, and media. Users can analyze news articles for media bias and balanced perspectives across thousands of sources. It also delivers live data for stocks, ETFs, and crypto, offering AI-generated bull/bear cases, advanced options pricing, and semantic meme searching capabilities.

This guide helps determine the optimal deployment strategy for Qdrant, a vector database. It compares four main options—Local Mode, Self-Hosted (Docker), Qdrant Cloud (Zero-Ops), and Qdrant EDGE—based on project requirements such as control needs, operational overhead, data residency, and required latency. Use it to decide whether to prototype, manage full infrastructure, or prioritize minimal latency.