Data Science & ML Habilidades de Claude

Kalshi Prediction Markets

Accesses real-time prediction market data, betting odds, and trading analytics from the Kalshi platform.

Multi-Agent Coordination Framework

Orchestrates complex multi-agent AI systems and human-AI teams using advanced communication patterns and consensus mechanisms.

Data Visualization

Generates insightful, professional-grade charts and interactive dashboards using industry-standard libraries and design principles.

Bioinformatics Research Project Manager

Automates the initialization and lifecycle management of bioinformatics research projects with structured workflows and scientific quality standards.

R Causal Mediation Analysis

Performs advanced causal mediation analysis in R to decompose total effects into direct and indirect pathways across various statistical models.

Lab Notebook Manager

Manages structured bioinformatics lab notebooks through interactive dialogue to ensure high-quality, reproducible research documentation.

Scientific Experiment Reporter

Generates and refines structured scientific reports from bioinformatics lab notebooks with integrated figure support and professional PDF export.

Hypothesis-Driven Research

Refines and validates bioinformatics hypotheses by transforming vague observations into specific, testable experimental strategies.

SAM3 Image Segmentation API

Provides standardized API patterns and implementation guidance for Meta's Segment Anything Model 3 (SAM3) across image and video tasks.

LangGraph Architect

Architects and implements sophisticated graph-based AI agents and multi-agent workflows using LangGraph.

Python Data Reviewer

Optimizes Pandas and NumPy code for high-performance data processing, memory efficiency, and vectorization.

Statistical Trading Symbol Selection

Analyzes market data using Hurst exponent, GARCH models, and Markov regime detection to identify optimal trading symbols for quantitative strategies.

LangGraph Architecture Analysis

Analyzes LangGraph agent architectures to identify performance bottlenecks and generate data-driven improvement strategies.

Backtest Datetime Visualization

Converts trading backtest charts from generic bar indices to precise datetime-based axes for improved temporal context.

LLM Evaluation Framework

Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and comparative benchmarking.

ML Code Reviewer

Reviews machine learning and deep learning code to optimize model architecture, training loops, and data pipelines for PyTorch and TensorFlow.

Advanced Excel & Data Analysis

Manages complex spreadsheet creation, modification, and financial modeling with a focus on dynamic formulas and industry-standard formatting.

R Survival Analysis Patterns

Implements comprehensive survival analysis and time-to-event modeling patterns using the R statistical programming language.

Tidymodels Review Patterns

Automates code review for R Tidymodels workflows to prevent data leakage and ensure statistical best practices.

Mendelian Randomization in R

Performs robust causal inference using genetic variants through a comprehensive suite of Mendelian Randomization (MR) methods and sensitivity analyses.

Real-World Evidence Analysis in R

Generates real-world evidence from observational data using advanced R-based causal inference and target trial emulation techniques.

Advanced Adaptive Trial Design in R

Design, simulate, and analyze complex adaptive clinical trials using industry-standard R packages and Bayesian methods.

RDKit Cheminformatics

Performs advanced molecular modeling and cheminformatics tasks including descriptor calculation, fingerprinting, and substructure searching.

R Network Meta-Analysis Toolkit

Performs comprehensive network meta-analyses in R using frequentist and Bayesian frameworks to compare multiple treatments simultaneously.

Bayesian Modeling in R

Builds and analyzes robust Bayesian statistical models using Stan-based R packages like brms and rstanarm.

ML API Endpoint Expert

Streamlines the design and deployment of production-grade machine learning inference endpoints using FastAPI and Docker.

Recipes Feature Engineering

Streamlines machine learning data preprocessing in R using standardized Tidymodels recipes patterns.

R Diagnostic Accuracy Analysis

Performs comprehensive diagnostic test accuracy analysis in R, including ROC curves, cutpoint optimization, and meta-analysis.

Individual Participant Data (IPD) Meta-Analysis

Synthesizes patient-level data across multiple studies using advanced meta-analysis methods and R statistical frameworks.

Tidyverse Model Tuning

Optimizes machine learning hyperparameters using the R Tidymodels ecosystem for improved model performance.