Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Facilitates the retrieval and analysis of over 200 million AI-predicted protein structures from the AlphaFold DB for biological research and drug discovery.
Accesses global statistical data from the Data Commons knowledge graph to analyze demographics, economics, health, and environmental trends.
Optimizes LLM inference request grouping and scheduling to minimize operational costs while satisfying latency and padding constraints.
Facilitates solving complex pattern recognition tasks by combining git workflow management with mathematical grid transformation analysis and implementation.
Migrates legacy Python 2 scientific computing code to Python 3 using modern libraries like pandas, numpy, and pathlib.
Analyzes and fits peaks in Raman spectroscopy data using physically-constrained models like Lorentzian, Gaussian, and Voigt functions.
Reorganizes large-scale datasets into hierarchical directory structures while enforcing strict file size and item count constraints.
Optimizes data processing workflows using the high-performance Polars DataFrame library and expression API.
Designs and optimizes multi-component fusion protein sequences for FRET biosensors and gene synthesis.
Upgrades legacy Python 2 scientific computing code and analysis pipelines to modern Python 3 standards using contemporary libraries like NumPy and pandas.
Implements PyTorch pipeline parallelism to distribute large language model training across multiple GPUs using All-Forward-All-Backward (AFAB) scheduling.
Optimizes semantic similarity retrieval tasks through expert guidance on document preprocessing, embedding model selection, and similarity ranking.
Reconstructs PyTorch model architectures from weight files and state dictionaries by analyzing tensor shapes and naming patterns.
Performs advanced biological data analysis including sequence manipulation, phylogenetic tree construction, and microbial diversity metrics.
Accesses the world's largest somatic mutation database for cancer research and precision oncology data retrieval.
Provides specialized guidance and implementation patterns for analyzing and curve-fitting peaks in Raman spectroscopy data.
Reconstructs PyTorch model architectures from saved state dictionaries, enables selective layer fine-tuning, and facilitates TorchScript conversion for deployment.
Implements SAM-based biological image segmentation pipelines, converting binary masks to polygon coordinates for microscopy data processing.
Extracts internal weight matrices and biases from black-box ReLU neural networks using input-output query strategies and functional equivalence testing.
Designs specialized primers for inserting DNA sequences into circular plasmids using Q5 site-directed mutagenesis and inverse PCR techniques.
Queries and retrieves genomic data from NCBI Gene databases using E-utilities and the modern Datasets API.
Deploys pre-trained HuggingFace Transformer models as robust REST API inference services using Flask or FastAPI.
Translates Bayesian inference workflows and Stan model implementations from RStan to PyStan with high precision and numerical stability.
Finds probability distributions that satisfy specific statistical constraints like KL divergence targets through mathematical analysis and optimized parameterization.
Designs optimized DNA gBlock sequences for fusion proteins by combining sequences from multiple databases with precise linker and codon constraints.
Decodes and interprets text content from G-code files by analyzing geometric toolpath data and coordinate patterns.
Merges heterogeneous data sources into unified datasets with automated field mapping and priority-based conflict resolution.
Converts PyTorch neural networks into standalone C/C++ command-line tools by extracting weights and reimplementing inference without Python dependencies.
Implements Bayesian Markov Chain Monte Carlo sampling workflows using RStan for complex hierarchical modeling and statistical inference.
Trains and optimizes FastText text classification models while balancing accuracy requirements against model size constraints through systematic hyperparameter tuning and quantization strategies.
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