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 technology through the lens of fundamental physical laws to evaluate constraints, feasibility, and energy efficiency.
Indexes PDF document collections using LightRAG to enable semantic search and context-aware research within Claude Code.
Processes genomic datasets including alignment, variant, and sequence files using a Pythonic interface to htslib.
Analyzes and processes genomic interval data using high-performance Rust-based tools and Python bindings.
Accelerates data manipulation and analysis using the high-performance Polars DataFrame library and expression-based API.
Generates high-quality visual content from text descriptions and image references using the Gemini API.
Optimizes complex systems using multi-objective evolutionary algorithms and Pareto front analysis in Python.
Automates computational pathology workflows by processing whole-slide images and multiparametric data for machine learning analysis.
Provides AI-ready drug discovery datasets, standardized benchmarks, and molecular oracles for therapeutic machine learning.
Performs advanced time series machine learning tasks including classification, forecasting, and anomaly detection using the specialized Aeon toolkit.
Provides comprehensive guidance and implementation patterns for explaining machine learning model predictions using SHAP values.
Accesses 20+ genomic databases for rapid bioinformatics queries, sequence analysis, and protein structure prediction.
Provides advanced protein language model capabilities for sequence generation, structure prediction, and functional design using ESM3 and ESM C.
Facilitates machine learning on genomic interval data, including embeddings for BED files and single-cell ATAC-seq analysis.
Architects production-grade Retrieval-Augmented Generation (RAG) systems and vector search infrastructures for knowledge-grounded AI applications.
Provides advanced capabilities for querying, visualizing, and manipulating complex ontologies through the Ontology Access Kit (OAK).
Ensures consistency in ontology term creation by applying Dead Simple Ontology Design Patterns (DOSDP) for standardized naming, definitions, and logical axioms.
Simplifies the process of editing, querying, and managing OBO format ontologies through specialized scripts and standardized curation workflows.
Automates complex PDF workflows including form filling, table extraction, OCR, and batch operations with production-grade validation.
Automates the creation, editing, and analysis of professional Excel spreadsheets with advanced formula support and financial modeling standards.
Applies Dead Simple Ontology Design Patterns to ensure consistency in term creation, naming conventions, and logical definitions.
Enables complex ontology querying, mapping, and visualization using the Ontology Access Kit (OAK) library.
Streamlines the editing, querying, and management of scientific ontologies in the Open Biomedical Ontologies (OBO) format.
Streamlines the development of AI-powered chat interfaces with streaming, tool calling, and multi-step reasoning patterns.
Implements stateful agent graphs and multi-actor workflows using the LangGraph framework.
Simplifies the registration and implementation of PydanticAI tools with automated context handling and type-safe function calling.
Identifies and fixes common implementation errors and architectural pitfalls when building PydanticAI agents.
Integrates and manages diverse LLM providers in PydanticAI with support for fallback models, streaming, and usage tracking.
Implements structured dependency injection for PydanticAI agents to manage database connections, API clients, and external resources.
Audits and evaluates AI agent codebases for compliance with the 12-Factor Agents methodology to ensure production-grade reliability.
Scroll for more results...