data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in proprietary data.
Automates the intelligent ingestion of Excel and Word documents into MXCP servers for structured querying and semantic search.
Automates the translation of MetaTrader 5 (MQL5) indicators into validated Python implementations for algorithmic trading.
Implements Renormalization Group (RG) flows using categorical ACSet semantics and topological XY model defect analysis.
Conducts rigorous statistical subgroup analyses to identify effect moderation and data heterogeneity across diverse research populations.
Generates publication-quality scientific figures and data visualizations following academic design standards.
Automates the creation of comprehensive pre-registration documents to ensure research transparency and prevent questionable scientific practices.
Generates standardized PRISMA 2020 flow diagrams to document the study selection process in systematic literature reviews.
Identifies and prioritizes research gaps from systematic literature reviews to justify new studies and grant proposals.
Implements rigorous blinding protocols to minimize bias and ensure objectivity in experimental studies and clinical trials.
Implements standardized patterns for summing tax and benefit variables across entities using the adds attribute and add() function.
Calculates and interprets standardized effect sizes to quantify the magnitude of research findings beyond simple p-values.
Generates structured evidence synthesis matrices to organize and compare research data for systematic reviews.
Integrates sophisticated OpenAI API features including streaming chatbots, content generation, and rate-limited API routes for production-ready AI applications.
Validates World Extractable Value (WEV) and topological system states using GF(3) logic and cybernetic reafference loops.
Models developmental biology cell fate transitions through gradient flow, potential surfaces, and fractional diffusion dynamics.
Verifies GF(3) symmetry conservation and Markov blanket integrity across renormalization group flows for cybernetic system stability.
Verifies thread ancestry and reconstructs balanced world states to facilitate epistemic arbitrage and skill orchestration.
Implements adaptive learning and meta-cognitive capabilities for AI agents to optimize strategies based on historical experience.
Implements high-performance adaptive learning and memory distillation for AI agents using the AgentDB vector backend.
Models and analyzes dynamical systems by assigning vectors to points in phase space to define complex flows and trajectories.
Implements L0 regularization and intelligent sampling techniques to optimize neural network sparsification and survey data calibration.
Quantifies and extracts economic value from coordination inefficiencies and world transitions using Markov blanket arbitrage and Multiverse Finance.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, LLM-as-Judge patterns, and human feedback loops.
Converts URDF robot descriptions into MJCF format for high-performance MuJoCo and MJX physics simulations.
Models and simulates autopoietic systems using arena theory and GF(3) conservation rules for adaptive learning.
Implements play/coplay arena theory for autopoietic closure and learning through GF(3) conservation.
Computes non-Archimedean distance metrics for hierarchical clustering, p-adic analysis, and version control ancestry.
Facilitates the development, simulation, and control of 3D-printed humanoid robots for reinforcement learning research.
Transforms raw datasets into impactful visual narratives using advanced data visualization techniques and narrative design principles.
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