Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Orchestrates multiple AI model providers to optimize development workflows for cost, latency, and reasoning capability.
Fetches, searches, and manages academic papers from arXiv through a local CLI-based database.
Facilitates advanced probabilistic modeling and analysis of single-cell omics data using deep generative models.
Generates professional, publication-quality statistical graphics and complex multi-panel data visualizations using Python's Seaborn library.
Enables parallel and distributed computing for Python data science workflows to process datasets larger than available memory.
Master core machine learning pillars including data preprocessing, feature engineering, and robust model evaluation pipelines.
Analyzes genomic VCF files to provide personalized insights on health, metabolism, and genetic traits.
Generates and transforms high-quality images using Google's Gemini models through customizable Python scripts.
Builds, optimizes, and executes quantum circuits and algorithms across various hardware providers and simulators.
Performs exact symbolic computation, calculus, and equation solving in Python to handle complex mathematical formulas without numerical approximation.
Guides the selection of the optimal Claude model for custom agent tasks based on cost, speed, and reasoning requirements.
Synchronizes and updates the latest LLM model specifications, pricing, and API documentation automatically.
Empowers AI agents to perform complex scientific research tasks using a unified ecosystem of 600+ specialized tools and databases.
Analyzes textual data to extract sentiments, keywords, and core topics using advanced natural language processing techniques.
Accesses and analyzes chemical data from the world's largest open chemical database using PUG-REST and PubChemPy.
Builds complex process-based discrete-event simulations in Python to model systems with shared resources and time-based events.
Provides standardized patterns and architectural best practices for prompt engineering, AI agent orchestration, and Claude Code configuration.
Optimizes Radio Access Network performance using autonomous swarm coordination and cognitive temporal reasoning.
Transforms Claude into a specialized prompt architect for designing, optimizing, and debugging complex AI instructions and agent behaviors.
Standardizes SLURM job output naming by mapping channel numbers to biological marker names for the KINTSUGI pipeline.
Optimizes large-scale data staging on HPC environments using rsync, bash, and SLURM to ensure data integrity and script reliability.
Automates Google Vertex AI multimodal operations to process, analyze, and transform media content within your development environment.
Translates CODEX/Akoya experiment.json metadata into the KINTSUGI ExperimentConfig format with precise field and wavelength mapping.
Implements industry-standard gradient boosting algorithms for high-performance machine learning on tabular and structured datasets.
Automates laboratory liquid handling workflows by generating and optimizing Opentrons Protocol API v2 scripts for Flex and OT-2 robots.
Translates trading strategy documentation into production-ready Python backtesting code and TradingView Pine Script.
Builds comprehensive financial models including DCF analysis, sensitivity testing, and Monte Carlo simulations for investment and valuation decisions.
Orchestrates sophisticated multi-agent systems with intelligent routing, handoffs, and collaborative workflows across AI providers.
Builds sophisticated AI agents with tool-calling capabilities and multi-provider LLM integration using a Kotlin-native framework.
Recalibrates upcoming training sessions dynamically based on recent performance, user feedback, and safety constraints.
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