Loading...
Loading...
Found 21 Skills
Create beautiful, performant SVG animations and illustrations. Use this skill when the user asks to create SVG graphics, icons, illustrations, animated logos, path animations, morphing shapes, loading spinners, or any animated SVG content. Covers SMIL animations, CSS-driven SVG animation, path drawing effects, shape morphing, motion paths, gradients, masks, and filters.
Provides brand naming frameworks, evaluation criteria, and templates for startup naming work. Auto-activates during brand name development, name evaluation, domain checking, and trademark research. Use when discussing brand name, company name, product name, naming strategy, SMILE SCRATCH framework, domain availability, trademark, name evaluation, sound symbolism, or naming matrix.
This skill should be used when the user asks to "animate an SVG", "make a line draw itself on", "do a stroke draw-on / signature animation", "morph one shape into another", "move an element along a path", "animate an icon/logo", or "animate an SVG gradient or filter". Covers stroke-dashoffset draw-on, path morphing, motion-along-path, and animated icons/gradients/filters via CSS, SMIL, and GSAP.
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Builds figures that move or respond to the reader, in three tiers: SMIL animations and slider widgets inside lessons, interactive mechanism figures that show the named parts of a real system with a computed verdict, and ordered dither bands in the course palette. Triggers: "animated figure", "add a slider widget", "interactive figure", "show how this system works", "make this diagram move", "dither masthead", "mechanism figure", "figure fence".
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
Implied volatility analysis for options via Longbridge — IV vs HV comparison, IV percentile rank, volatility smile and skew, options pricing assessment, strategy selection guidance. Triggers: "隐含波动率", "IV", "期权波动率", "波动率偏斜", "波动率微笑", "HV", "历史波动率", "IV百分位", "期权定价", "隱含波動率", "期權波動率", "波動率偏斜", "波動率微笑", "歷史波動率", "IV百分位", "期權定價", "implied volatility", "IV percentile", "volatility smile", "volatility skew", "HV vs IV", "options pricing", "vol surface", "TSLA.US implied vol".
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Model, forecast, and interpret volatility using time-series models and options-implied measures. Use when the user asks about EWMA, GARCH models, implied volatility, volatility surfaces, volatility term structure, or the VIX. Also trigger when users mention 'volatility smile', 'volatility skew', 'realized vs implied vol', 'volatility risk premium', 'vol clustering', 'mean-reverting volatility', 'options pricing inputs', 'RiskMetrics', 'decay factor', or ask how to forecast future volatility for risk management.
Cheminformatics pipeline for small-molecule property calculation, filtering, and similarity analysis using RDKit. Use when the user asks to compute molecular descriptors, filter compounds by Lipinski or Veber rules, detect PAINS, calculate fingerprint similarity, run matched molecular pair analysis, generate ADMET descriptors, or process SMILES. Triggers include "RDKit", "molecular descriptors", "Lipinski", "rule of five", "Veber", "PAINS", "pan-assay interference", "Morgan fingerprint", "Tanimoto", "fingerprint similarity", "matched molecular pair", "MMP", "mmpdb", "ADMET", "druglikeness", "SMILES", "cheminformatics", "compound filtering", "chemical similarity".
Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.
Generate novel drug-like molecules using the GenMol NIM microservice. Use for de novo generation, scaffold decoration, motif extension, lead optimization, SAFE notation, QED or LogP ranking, hosted NVIDIA API calls, or local Docker deployment. GenMol takes SAFE notation in the smiles field, not ordinary SMILES.