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.

Diffusion-based deep learning tool for predicting 3D binding poses of small-molecule ligands to protein targets from PDB files or sequences plus SMILES/SDF/MOL2. Supports single docking, batch virtual screening, and confidence-score analysis; not for binding affinity prediction.

Run open-source molecular design and structural biology tools on Tamarind Bio's managed cloud GPUs via REST API or MCP. Covers AlphaFold/Boltz/Chai structure prediction, RFdiffusion and ProteinMPNN design, antibody and nanobody engineering, DiffDock docking, binding affinity, MSA generation, and molecular dynamics — no local GPU setup needed.

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.

A comprehensive companion tool for the foundational Deep Learning textbook. It indexes 20 chapters and provides a crucial delta layer, updating core concepts from 2016 to 2026 (e.g., Transformers, Diffusion Models). The tool features four deterministic utilities: a reading-path planner, a training-failure diagnostic, a capacity planner, and a parameter/FLOP calculator. Ideal for students and practitioners who need to study the theory, understand its modern relevance, and translate academic knowledge into actionable engineering decisions.