发现data science & ml类别的 Claude 技能。浏览 61 个技能,找到适合您 AI 工作流程的完美功能。
Analyzes systems and technological feasibility using fundamental laws of physics and quantitative modeling.
Analyzes global events, policy changes, and power dynamics using established political science frameworks and international relations theories.
Analyzes events and cultural systems through established anthropological frameworks and ethnographic methods.
Analyzes global events and policy changes using established political science frameworks and theoretical models.
Analyzes complex events and policies through the lens of ecological science, sustainability frameworks, and conservation biology to evaluate environmental impact.
Analyzes urban development and spatial organization using professional planning frameworks, zoning regulations, and sustainability metrics.
Builds production-ready AI agents using Anthropic's official framework for tool orchestration and subagent workflows.
Automates comprehensive AI model benchmarking and performance comparison using the Benchmark Suite V3 framework.
Accesses the UniProt knowledgebase to search, retrieve, and map protein sequence and functional information.
Evaluates technical feasibility and system dynamics using fundamental physical laws, thermodynamics, and quantitative modeling.
Integrates Google Gemini CLI to provide Claude with real-time web search, deep codebase analysis, and multi-model code generation capabilities.
Analyzes disease patterns and health events using specialized epidemiological frameworks and surveillance methods to inform public health interventions.
Provides procedural guidance for setting up HuggingFace model inference services using Flask, covering environment setup, model caching, and robust API implementation.
Provides systematic guidance for identifying, verifying, and extracting current performance data from machine learning benchmarks and embedding leaderboards.
Builds production-grade LLM applications using structured pipelines, task-model fit analysis, and deterministic architecture patterns.
Accesses and retrieves gene expression and functional genomics data from the NCBI Gene Expression Omnibus (GEO) repository.
Facilitates solving complex pattern recognition tasks by combining git workflow management with mathematical grid transformation analysis and implementation.
Builds and validates complex Bayesian models using PyMC's probabilistic programming framework.
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.
Analyzes market events and policy changes using rigorous economic frameworks and diverse schools of thought.
Reconstructs PyTorch model architectures from weight files and state dictionaries by analyzing tensor shapes and naming patterns.
Migrates legacy Python 2 scientific computing code to Python 3 using modern libraries like pandas, numpy, and pathlib.
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.
Optimizes semantic similarity retrieval tasks through expert guidance on document preprocessing, embedding model selection, and similarity ranking.
Implements SAM-based biological image segmentation pipelines, converting binary masks to polygon coordinates for microscopy data processing.
Designs specialized primers for inserting DNA sequences into circular plasmids using Q5 site-directed mutagenesis and inverse PCR techniques.
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.
Scroll for more results...