Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Architects high-performance system instructions and prompt patterns to optimize Large Language Model outputs and consistency.
Builds machine learning models and unsupervised embeddings for genomic interval data and BED file collections.
Implements high-performance systems and complex algorithms with absolute scientific rigor and formal correctness.
Transforms initial software concepts into comprehensive, well-architected technical designs and implementation roadmaps.
Builds low-latency, production-grade voice applications using real-time APIs, advanced synthesis, and streaming transcription services.
Generates and updates professional PyTorch-style docstrings using Sphinx and reStructuredText conventions.
Generates publication-quality scientific diagrams and technical schematics with automated AI quality review and smart iteration.
Indexes and manages external reference documents to power Retrieval-Augmented Generation (RAG) for domain-specific AI workflows.
Orchestrates complex mathematical computations by deterministically routing natural language requests to specialized CLI tools for symbolic math, logic, and geometry.
Builds scalable, production-grade data pipelines and ETL/ELT systems using the modern data stack.
Connects Claude to cloud laboratory services for automated protein testing, sequence optimization, and wet-lab validation.
Enables development and training of Graph Neural Networks (GNNs) using the PyTorch Geometric library.
Searches and retrieves life sciences preprints from the bioRxiv database with advanced filtering and PDF download capabilities.
Executes real-time, AI-powered web searches with cited sources and scientific literature access using Perplexity models.
Queries the NHGRI-EBI GWAS Catalog to retrieve SNP-trait associations, genetic variant data, and genome-wide association study summary statistics.
Transforms vague research interests into concrete, actionable, and tractable research questions with a systematic feasibility analysis.
Accesses the Human Metabolome Database (HMDB) to retrieve metabolite data, chemical properties, and clinical biomarkers for metabolomics research.
Generates rigorous experimental frameworks for scientific research and machine learning projects to ensure statistically significant and defensible results.
Deploys machine learning models to Hugging Face Spaces using optimized configurations for Gradio, ZeroGPU, and LoRA adapters.
Transforms vague research interests into concrete, measurable, and tractable research questions through systematic refinement and feasibility analysis.
Simplifies querying the openFDA API to analyze regulatory data, drug safety profiles, medical device clearances, and food recalls.
Provides architectural patterns and implementation guides for building reliable autonomous AI agent systems.
Implements a systematic methodology for diagnosing, refining, and validating trading strategies to improve win rates and returns.
Provides structured methodologies and frameworks for market research, competitor analysis, and professional data synthesis.
Architects and implements sophisticated, stateful multi-agent LLM applications using LangGraph and Python.
Simplifies text analysis and processing using modern NLP techniques including embeddings, tokenization, and transformer models.
Implements granular skip-existing checks in Snakemake wrapper scripts to resume interrupted HPC jobs without re-processing completed channels.
Optimizes KINTSUGI batch processing by enforcing GPU-only SLURM scheduling to achieve up to 25x speedups over CPU fallback.
Enhances multiplex immunofluorescence images by applying range-specific weights to remove background noise while preserving delicate biological signals.
Integrates the Google Gemini CLI into Claude to provide large-context analysis, safe sandbox execution, and structured code modifications.
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