data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Executes and monitors neural network training runs using best-practice configurations and mandatory logging backends.
Streamlines academic literature reviews by discovering, ranking, and processing research papers from top scientific venues.
Discovers, prioritizes, and manages academic research papers for systematic literature reviews and methodology research.
Streamlines academic literature review and research paper discovery with automated prioritization and screening workflows.
Processes and prepares data files for AI agent testing and deployment workflows.
Aligns Python trading strategies with TradingView Pine Script exports and research logic to ensure 100% backtest parity.
Automates the creation of high-quality ML/AI review papers for arXiv using the IEEEtran LaTeX template and verified citations.
Builds production-grade Python document-processing systems featuring Docling integration, OCR fallbacks, and editable DOCX generation.
Streamlines the development of Retrieval-Augmented Generation (RAG) features for Redmine with strict grounding and citation requirements.
Facilitates the development and management of multi-agent Retrieval-Augmented Generation (RAG) pipelines with seamless Notion integration.
Transcribes audio and video files into text via a specialized Speech2Text API with support for JWT authentication and task polling.
Verifies mathematical claims and generates Lean 4 formal proofs or counterexamples using the Harmonic Aristotle API.
Enhances decision-making through a multi-model adversarial reasoning protocol and reliability-weighted aggregation.
Provides expert guidance and routine lookup for the ctrlsys control systems library, covering LQR design, Kalman filtering, and system identification.
Provides expert guidance on control system design, analysis, and identification using the ctrlsys library.
Extracts structured training pairs from academic peer reviews and source documents to build high-quality datasets for LLM fine-tuning.
Develops production-grade Python code with a focus on type safety, fail-fast logic, and rigorous testing for research environments.
Automates multi-stage research idea generation and evaluation using graph-guided search and tournament-style ranking.
Optimizes multi-agent AI systems through intelligent coordination, performance profiling, and cost-aware orchestration.
Automates the creation of production-grade Pegasus scientific workflows from high-level pipeline descriptions.
Visualizes solar observation data, EUV imagery, and machine learning model outputs using SunPy and Matplotlib.
Transforms raw SDO/AIA solar observation data into standardized, ML-ready formats through automated calibration and registration pipelines.
Enables Claude to interactively explore, analyze, and modify open Microsoft Excel workbooks using natural language commands.
Develops and deploys deep learning models for solar physics using preprocessed Sun and Space Weather data.
Converts Snakemake and Nextflow pipelines into robust Pegasus workflows for high-performance computing environments.
Downloads solar observation data from SDO, STEREO, and Solar Orbiter missions for scientific analysis and machine learning.
Establishes rigorous and defensible ground truth labels for evaluation datasets based on authoritative guidelines.
Streamlines academic research data analysis by enforcing reproducible dbt pipelines and interactive Streamlit dashboards.
Facilitates reproducible academic research and data pipelines using dbt and Streamlit while enforcing rigorous data integrity standards.
Extracts structured training examples from document sets to create high-quality datasets for teaching LLMs specific tasks or styles.
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