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。
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic interface for RDKit.
Automates laboratory liquid handling and equipment control through a hardware-agnostic Python interface.
Structures clinical research and evaluates health evidence using established evidence-based medicine frameworks like PICOT and GRADE.
Accesses the RCSB Protein Data Bank to search, retrieve, and analyze 3D structural data for biological macromolecules.
Verifies that machine learning models correctly respect intended symmetries through systematic numerical tests and debugging guidance.
Processes medical imaging data in the DICOM standard format for healthcare applications and radiology workflows.
Stress-tests predictions by assuming failure and working backward to identify hidden risks and blind spots.
Applies unsupervised machine learning models to genomic interval data for embedding, clustering, and bioinformatics analysis.
Provides structured frameworks and systematic workflows for Test-Driven Development (TDD), data exploration, and statistical modeling.
Quantifies uncertain business choices and crafts persuasive, data-backed narratives to secure stakeholder buy-in.
Matches data questions to optimal chart types and generates narrated reports that turn raw metrics into actionable business insights.
Designs neural network architectures that preserve symmetry groups to improve model efficiency and robustness.
Parses and creates Flow Cytometry Standard (FCS) files for seamless integration with Python data science workflows.
Maps identified symmetries to mathematical groups to formalize requirements for equivariant and invariant neural network architectures.
Detects and mitigates cognitive biases to improve decision-making, forecasting accuracy, and intellectual honesty.
Builds custom, interactive data-driven visualizations using D3.js for complex layouts, maps, and bespoke charting requirements.
Establishes objective statistical baselines for predictions and projects by anchoring them in historical reality and the 'Outside View.'
Systematically investigates causal relationships to identify fundamental root causes and distinguish them from symptoms or correlations.
Explains machine learning model predictions and feature importance using SHapley Additive exPlanations (SHAP) across various model architectures.
Builds, fits, and validates Bayesian statistical models using PyMC and ArviZ for probabilistic programming.
Implements systematic Bayesian probability updates to improve forecasting and decision-making under uncertainty.
Evaluates financial models using state-of-the-art metrics specifically designed for range bar (price-based sampling) data.
Enhances Glide apps with AI-powered columns for text generation, OCR, transcription, and automated data processing.
Optimizes training epochs for machine learning models using Walk-Forward Efficiency and efficient frontier analysis.
Interacts with the Replicate AI platform to run models, manage deployments, and perform fine-tuning directly from the command line.
Automates professional spreadsheet creation, complex financial modeling, and data analysis with formula-driven accuracy.
Leverages a multi-model ensemble to provide diverse, parallel insights from GPT, Gemini, and Claude for complex problem-solving.
Analyzes and evaluates LLM prompts using a structured 10-Layer Architecture to improve clarity, precision, and output performance.
Calculates probability-weighted outcomes to facilitate rational decision-making under uncertainty for investments, product development, and strategic initiatives.
Generates evidence-based, testable scientific hypotheses and structured experimental designs to accelerate research and discovery.
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