发现data science & ml类别的 Claude 技能。浏览 53 个技能,找到适合您 AI 工作流程的完美功能。
Streamlines the editing, querying, and management of scientific ontologies in the Open Biomedical Ontologies (OBO) format.
Enables complex ontology querying, mapping, and visualization using the Ontology Access Kit (OAK) library.
Applies Dead Simple Ontology Design Patterns to ensure consistency in term creation, naming conventions, and logical definitions.
Automates the creation of publication-quality data visualizations and business reports for machine learning workflows.
Automates the end-to-end feature engineering process for machine learning pipelines, from data quality assessment to production-ready transformations.
Manages machine learning experiment tracking and model comparison by automatically logging parameters, metrics, and artifacts to SpecWeave increments.
Manages the complete machine learning model lifecycle through centralized versioning, staging pipelines, and automated metadata tracking within the SpecWeave framework.
Builds production-ready machine learning pipelines for image classification, object detection, and semantic segmentation using PyTorch or TensorFlow.
Detects unusual patterns and outliers in data using statistical methods and machine learning algorithms integrated with the SpecWeave workflow.
Conducts comprehensive machine learning model evaluations with advanced metrics, statistical validation, and automated reporting.
Builds sophisticated time-dependent predictive models using statistical methods, machine learning, and deep learning within the SpecWeave framework.
Orchestrates end-to-end machine learning workflows within a disciplined, spec-driven development framework.
Builds production-ready natural language processing pipelines using state-of-the-art transformer models and SpecWeave automation.
Automates the transition of machine learning models into production-ready services with APIs, containerization, and monitoring.
Designs and implements production-grade DAG-based MLOps pipeline architectures using orchestrators like Airflow, Dagster, and Kubeflow.
Automates hyperparameter tuning and model selection using industry-standard frameworks like Optuna and Auto-sklearn.
Generates human-readable interpretability reports and explainability metrics for machine learning models using SHAP, LIME, and feature importance.
Automates the execution, editing, and dependency management of Jupyter notebooks using jtool and uv.
Streamlines the creation and validation of AI agent templates through automated scaffolding and profile linting.
Writes dataframe-agnostic Python code that runs seamlessly across pandas, Polars, PyArrow, and other major backends.
Integrates large language models with the emotive-mascot engine to create sentiment-driven, emotionally responsive conversational interfaces.
Simplifies LLM interactions by providing a unified Python interface for 100+ AI providers with consistent OpenAI-format syntax.
Implements robust pipes-and-filters architectures for complex ETL, media processing, and data transformation workloads.
Creates, analyzes, and manages complex Excel spreadsheets with a focus on financial modeling standards and formula integrity.
Profiles and optimizes Python code to identify bottlenecks, reduce latency, and minimize memory consumption using industry-standard tools.
Configures and manages local LLM inference using Mozilla Llamafile to provide offline, OpenAI-compatible AI capabilities.
Orchestrates task delegation to external LLM services by offloading high-token execution while maintaining central reasoning within Claude.
Conducts deep-dive information gathering and data synthesis to produce actionable strategic insights and comprehensive reports.
Executes machine learning examples on remote GPU hosts via SSH by syncing minimal workspaces and launching Docker-based training scripts.
Designs, optimizes, and deploys scalable large language model architectures and high-performance RAG systems.
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