发现data science & ml类别的 Claude 技能。浏览 61 个技能,找到适合您 AI 工作流程的完美功能。
Optimizes LLM performance and reliability through advanced prompt engineering techniques like few-shot learning and chain-of-thought reasoning.
Masters the art of prompting by treating LLMs like senior engineers through goal-oriented instructions and clear role definition.
Manages multi-model access, fallbacks, and cost-based routing using OpenRouter and LiteLLM.
Standardizes the integration and optimization of Large Language Models through live research protocols and production-grade engineering patterns.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in proprietary data.
Integrates Ollama into projects for local AI inference, model management, and streaming API implementation.
Ensures alignment between philosophical stances, analytical language, and research practices in qualitative studies.
Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Implements a recursive, autopoietic loop for state management that synchronizes memory storage, pattern-matching recall, and generative world-building.
Implements optimized document splitting and processing workflows for Retrieval-Augmented Generation (RAG) systems.
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.
Automates the intelligent ingestion of Excel and Word documents into MXCP servers for structured querying and semantic search.
Verifies thread ancestry and reconstructs balanced world states to facilitate epistemic arbitrage and skill orchestration.
Models and analyzes dynamical systems by assigning vectors to points in phase space to define complex flows and trajectories.
Implements high-performance adaptive learning and memory distillation for AI agents using the AgentDB vector backend.
Quantifies and extracts economic value from coordination inefficiencies and world transitions using Markov blanket arbitrage and Multiverse Finance.
Implements adaptive learning and meta-cognitive capabilities for AI agents to optimize strategies based on historical experience.
Analyzes dynamical systems using the LaSalle Invariance Principle to determine asymptotic stability and long-term qualitative behavior.
Converts URDF robot descriptions into MJCF format for high-performance MuJoCo and MJX physics simulations.
Conducts rigorous statistical subgroup analyses to identify effect moderation and data heterogeneity across diverse research populations.
Implements play/coplay arena theory for autopoietic closure and learning through GF(3) conservation.
Generates publication-quality scientific figures and data visualizations following academic design standards.
Models and simulates autopoietic systems using arena theory and GF(3) conservation rules for adaptive learning.
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.
Facilitates the development, simulation, and control of 3D-printed humanoid robots for reinforcement learning research.
Implements rigorous blinding protocols to minimize bias and ensure objectivity in experimental studies and clinical trials.
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