data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Performs NCBI BLAST sequence similarity searches using BioPython to identify homologous DNA or protein sequences.
Performs high-speed local DNA sequence alignment against hg38 and CHM13 genomic references without external API dependencies.
Analyzes genomic alignment files to extract reads, identify insertions and deletions, and calculate coverage statistics for WGS and WES data.
Analyzes, filters, and exports genomic variant data from VCF and BCF files for bioinformatics and sequencing workflows.
Queries and annotates genomic data using the COSMIC Cancer Gene Census to identify known cancer genes and their clinical properties.
Automates IGV snapshot generation for visualizing genomic alignments and variant calls in BAM files.
Generates high-quality images from text prompts using Google Gemini 3 Pro via the fal.ai API.
Builds production-ready RAG systems and semantic search using optimized Gemini embedding-001 models and vector storage patterns.
Builds type-safe, composable LLM applications in Ruby using the DSPy framework to program AI behavior instead of manual prompting.
Implements sophisticated LLM-as-judge methodologies to evaluate and compare AI model outputs with high reliability and bias mitigation.
Implements production-grade LLM-as-a-judge patterns to evaluate model outputs with high reliability and bias mitigation.
Builds, configures, and deploys native Streamlit data applications directly within the Snowflake Data Cloud.
Implements sophisticated, multi-layered memory architectures including knowledge graphs and temporal persistence for autonomous AI agents.
Implements advanced memory architectures for AI agents to maintain session continuity and manage structured entity relationships.
Builds and packages portable AI agents that operate across multiple LLM frameworks and deployment targets without vendor lock-in.
Builds high-performance Retrieval-Augmented Generation systems using vector databases and semantic search to ground AI responses in external knowledge.
Builds robust AI applications using OpenAI's Agents SDK with multi-agent orchestration, voice capabilities, and advanced error prevention.
Implements Google Gemini File Search to build managed RAG systems with automatic document chunking and semantic search.
Generates structured, evidence-driven Product Requirements Documents (PRDs) specifically tailored for Machine Learning workflows and experiments.
Implement advanced LLM prompting techniques like few-shot learning and chain-of-thought to enhance production AI reliability and output quality.
Builds and validates sophisticated Bayesian probabilistic models using the PyMC library for advanced statistical inference.
Systematically assesses medical research proposals to quantify their impact on patient outcomes, clinical decision-making, and healthcare systems.
Automates protein testing and validation through a cloud laboratory platform for high-throughput protein design workflows.
Converts literary works into high-quality supervised fine-tuning (SFT) datasets to train AI models in specific authorial voices.
Builds and orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment.
Manages Open WebUI instances via Podman to provide a browser-based chat interface for Ollama LLM models.
Facilitates direct REST API operations for Ollama using Python to manage models and execute generation tasks.
Manages LocalAI services via Podman to provide OpenAI-compatible local model inference with full GPU acceleration.
Provides AI-ready datasets and benchmarks for drug discovery, including ADME, toxicity, and molecular generation tasks.
Streamlines computational molecular biology tasks including sequence analysis, biological file parsing, and genomic database integration.
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