Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
WHAT YOU BECOME
Perfect for these scenarios
Quickly upload photos to Snap.as for sharing via API integration
Automatically categorize and tag images using Snap.as API
Connect Snap.as to social platforms for seamless photo posting
Create and manage photo galleries programmatically with Snap.as API
MEASURED GAIN
Proven benefits and measurable impact
Reduce time spent on manual uploads by 5x with automation
Streamline photo management tasks, boosting efficiency by 3x
Cut manual photo organization efforts by half using API
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
Recommended based on tags and category
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
Generate and maintain AGENTS.md files following the public agents.md convention. Use when creating documentation for AI agent workflows, onboarding guides, or when standardizing agent interaction patterns across projects.
Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.