Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Architects production-grade LLM applications, advanced RAG systems, and intelligent multi-agent workflows with enterprise-level safety and observability.
Enables deep, multi-step chain-of-thought reasoning for complex problem-solving and logic verification within Claude.
Extracts methylation levels from Bismark-aligned bisulfite sequencing data to generate per-cytosine reports.
Streamlines machine learning experiment logging, model versioning, and reproducibility using industry-standard tools like MLflow, Weights & Biases, and DVC.
Standardizes machine learning workflows by implementing systematic experiment logging, data versioning, and reproducibility patterns.
Architects and optimizes Retrieval-Augmented Generation (RAG) systems to ground AI applications in factual, domain-specific knowledge.
Verifies, normalizes, and enriches academic paper citations and identifiers for research accuracy and metadata consistency.
Integrates fal.ai high-performance audio models for seamless text-to-speech and speech-to-text conversion within development workflows.
Manages AI model deployments, monitors usage metrics, and tracks pricing via the Fal.ai platform APIs.
Manages and monitors long-running academic research workflows and citation graph explorations within Claude Code.
Architects short-term, long-term, and graph-based memory systems to enhance AI agent persistence and reasoning.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, LLM-as-Judge patterns, and human feedback loops.
Enhances the resolution and quality of images and videos using advanced AI models via the fal.ai platform.
Converts PDFs, PPTs, and images into clean Markdown using AI-assisted GLM OCR with strict integrity verification.
Implements optimized document splitting and processing workflows for Retrieval-Augmented Generation (RAG) systems.
Performs specialized biological validation for ChIP-seq data by calculating cross-correlation metrics and fraction of reads in peaks.
Designs and implements self-referential systems, metacognitive architectures, and recursive logic patterns.
Synthesizes and compares findings across multiple research reports to identify cross-run trends and knowledge gaps.
Integrates enterprise-grade AI capabilities into SAP BTP applications using the SAP Cloud SDK for AI for JavaScript/TypeScript and Java.
Optimizes LLM performance and reliability through advanced prompt engineering techniques like few-shot learning and chain-of-thought reasoning.
Implements high-performance persistent memory and learning patterns for AI agents using AgentDB.
Streamlines machine learning workflows through systematic experiment logging, data versioning, and reproducibility tracking.
Builds focused academic literature briefings and explores citation graphs using the PapersFlow research ecosystem.
Transforms vague or inconsistent prompts into high-performance instructions that maximize LLM output quality and efficiency.
Analyzes generated prompts to provide deep insights into element usage, quality comparisons, and style-based recommendations.
Performs professional-grade stock analysis, financial statement interpretation, and intrinsic company valuations.
Optimizes LLM API expenses by implementing intelligent model routing, budget tracking, and efficient caching strategies.
Optimizes AI agent architectures by refining action spaces, tool definitions, and observation formats for higher task completion rates.
Manages end-to-end video workflows including ingestion, semantic search, programmatic editing, and real-time desktop perception.
Implements a cost-effective hybrid framework that prioritizes regex patterns for structured text parsing and reserves LLM validation for complex edge cases.
Scroll for more results...