data science & ml向けのClaudeスキルを発見してください。61個のスキルを閲覧し、AIワークフローに最適な機能を見つけましょう。
Optimizes numerical computing tasks in Python using high-performance array operations and vectorized mathematical functions.
Executes complex numerical calculations, linear algebra operations, and scientific computing tasks using the high-performance Julia language.
Conducts systematic, academic-grade literature reviews and research syntheses across major scientific and technical databases.
Builds type-safe, modular LLM applications using Ruby's programmatic prompt framework with signatures and automated optimization.
Enables advanced molecular analysis, chemical property calculation, and 3D coordinate generation using the RDKit toolkit.
Downloads, parses, and summarizes ArXiv research papers by analyzing their raw LaTeX source code for deep technical insights.
Converts incompatible image, video, and audio formats into supported extensions for seamless data collection uploads.
Streamlines complex file uploads and metadata mapping to Synapse data collections across local and cloud storage providers.
Guides developers in configuring Synapse plugin metadata, action definitions, and runtime environments for seamless tool integration.
Provides standardized, type-safe result schemas for Synapse SDK plugin actions to ensure consistent data output and validation.
Guides users through a comprehensive 10-step pipeline for processing multiplex imaging data on SLURM-managed HPC clusters.
Manages KINTSUGI project initialization by distinguishing between raw and processed data while automating SLURM configuration.
Formally proves mathematical theorems and verifies algorithm correctness using Lean 4 and the Aristotle API.
Searches, retrieves, and summarizes academic papers from arXiv to streamline scientific research and literature reviews.
Translates trading strategy documentation into production-ready Python backtesting code and TradingView Pine Script.
Standardizes SLURM job output naming by mapping channel numbers to biological marker names for the KINTSUGI pipeline.
Orchestrates multiple AI model providers to optimize development workflows for cost, latency, and reasoning capability.
Streamlines the development, validation, and systematic documentation of trading strategies and market edges.
Master core machine learning pillars including data preprocessing, feature engineering, and robust model evaluation pipelines.
Architects comprehensive evaluation frameworks for AI agents by defining metrics, datasets, and grading strategies.
Automates Langdock assistant management, knowledge base operations, and usage data exports through a unified CLI wrapper.
Standardizes AI agent tuning by providing a command interface for bootstrapping, running, and monitoring optimization loops in Codex.
Transforms and analyzes large datasets using DuckDB SQL directly within the Claude Code environment.
Builds production-ready AI/ML prototypes and POCs on AWS with cost optimization and Well-Architected best practices.
Builds production-grade AI applications using advanced RAG patterns, prompt engineering, and LLM orchestration frameworks.
Integrates the Google Gemini CLI into Claude to provide large-context analysis, safe sandbox execution, and structured code modifications.
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.
Generates high-performance, structured prompts using official Anthropic conventions and 2025 best practices.
Implements production-ready architectural patterns and scalable designs for enterprise LangChain applications.
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