data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Simplifies geospatial data manipulation and spatial analysis using Python's GeoPandas library.
Queries the NHGRI-EBI GWAS Catalog to retrieve genetic variant associations, traits, and comprehensive genomic summary statistics.
Orchestrates autonomous multi-agent teams to collaborate on complex, multi-step workflows using the CrewAI framework.
Analyzes complex networks and implements graph algorithms for data science and relational modeling.
Deploys and optimizes large language models across diverse hardware environments using industry-leading inference engines and quantization techniques.
Implements pre-trained machine learning models for NLP, vision, and audio tasks using the HuggingFace ecosystem.
Interfaces with the Ollama API to perform high-performance text completions and clinical analysis using the Phi-4 language model.
Optimizes the deployment, operation, and performance of Grail miners on Bittensor Subnet 81 for verifiable language model post-training.
Architects and implements retrieval-augmented generation applications using LangChain, LlamaIndex, and Sentence Transformers.
Deploys production-grade machine learning models and implements robust MLOps infrastructure for scalable AI systems.
Architects sophisticated LLM applications using autonomous agents, retrieval-augmented generation (RAG), and complex memory management patterns.
Generates professional data visualizations and plots using industry-standard Python libraries like Matplotlib, Seaborn, and Plotly.
Streamlines the creation, sampling, and diagnostics of Bayesian statistical models using PyMC and ArviZ.
Explains machine learning model predictions using Shapley values to provide interpretable feature importance and attribution.
Optimizes data processing and analysis using the high-performance Polars DataFrame library for lightning-fast execution.
Empowers researchers and developers with specialized Python expertise for astronomy, bioinformatics, symbolic mathematics, and advanced statistical modeling.
Implements advanced agent memory architectures including knowledge graphs, temporal tracking, and cross-session persistence to maintain long-term context.
Generates publication-ready scientific figures and multi-panel layouts optimized for top-tier academic journals like Nature and Science.
Integrates pre-trained models like CLIP, Whisper, and Stable Diffusion for advanced vision, speech recognition, and image generation tasks.
Architects complex LLM workflows using advanced multi-agent patterns like supervisors, swarms, and hierarchical delegation for optimized context management.
Optimizes LLM performance through advanced prompt engineering, RAG system design, and agent workflow orchestration.
Scales Python data workflows across multiple cores or clusters to handle datasets exceeding available memory.
Optimizes AI system prompts automatically using the DSPy framework to build modular, data-driven LLM pipelines.
Automates the initialization and lifecycle management of bioinformatics research projects with structured workflows and scientific quality standards.
Streamlines the process of training and finetuning large language models using industry-standard frameworks and memory optimization techniques.
Calculates TAM, SAM, and SOM using top-down, bottom-up, and value theory methodologies to quantify business opportunities.
Guides developers through selecting, implementing, and optimizing vector search solutions for AI applications and RAG pipelines.
Backtests and evaluates quantitative trading strategies using historical stock market data to generate performance metrics and visualizations.
Optimizes PPO training performance on A100 and H100 GPUs by automatically aligning hyperparameters with hardware capabilities.
Automates quality control for stitched microscopy images by detecting saturation failures and visible tile grid patterns.
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