Skill UI

This guide provides a comprehensive technical deep dive for developers migrating image generation workflows from services like DALL-E, Midjourney, and Stable Diffusion to Ideogram. It covers API parameter mapping, architectural patterns (like the Adapter pattern), and provides code examples to ensure a stable, gradual, and feature-rich transition to Ideogram's platform.

This skill simulates a complete, multi-agent software development lifecycle following the principles of Agile and Scrum. It guides the project through six distinct, iterative phases: Architectural Planning, Parallel Implementation, Comprehensive Testing (Unit/E2E), Review, and Finalization. The process uses a convergent diffusion model, refining specifications and resolving conflicts across multiple specialized agents until the project converges and is deemed complete.

A comprehensive guide to leveraging Stable Diffusion models via the HuggingFace Diffusers library. This skill facilitates state-of-the-art image creation, including Text-to-Image generation from prompts, Image-to-Image translation, inpainting, outpainting, and advanced workflow building. Supports various models like SDXL and SD 3.0, ideal for professional creative and computational vision tasks.

DiffDock uses diffusion-based deep learning to predict protein-ligand binding poses, confidence, and batch virtual screening workflows for structure-based drug design.

Collection of battle-tested PyTorch training recipes covering architecture selection, optimizers, LR scheduling, precision tips, and debugging to train or fine-tune LLMs, vision, diffusion, and biomedical models with high GPU throughput.

DiffDock is a state-of-the-art, diffusion-based deep learning tool for predicting 3D binding poses of drug-like ligands to protein targets. It supports both PDB files and amino acid sequences for proteins, and accepts diverse ligand formats (SMILES, SDF, MOL2). It is designed for structure-based drug discovery, lead optimization, and high-throughput virtual screening campaigns, providing predicted poses and confidence scores.

Tamarind Bio is a comprehensive cloud platform providing access to state-of-the-art open-source tools for structural biology and molecular design. It supports complex tasks including protein structure prediction (AlphaFold, ESMFold), molecular design (RFdiffusion, ProteinMPNN), protein-ligand docking, and binding affinity prediction. Users submit sequences or structures and receive predicted results without requiring local GPU hardware.

This guide assists researchers in scoping their academic questions to ensure alignment with specialized journals like Research Policy. It provides detailed criteria for determining if a study focuses on the emergence, diffusion, or governance of innovation systems, rather than purely economic or strategy-focused outcomes. Use it to frame your work to speak directly to innovation scholars and policymakers.