Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Anchors predictions and decision-making in statistical frequencies to avoid cognitive bias and improve estimation accuracy.
Formalizes natural language mathematical questions into Lean 4 and verifies them using the Harmonic Aristotle prover API.
Implements structured competitive prediction frameworks using Brier scores and systematic debiasing to enhance organizational forecasting accuracy.
Simplifies the design and analysis of complex control systems using the C11-based ctrlsys library.
Analyzes complex optimization problems using evolutionary landscape metaphors to identify local traps and global optima.
Manages complex Excel workbooks with automated formula creation, financial modeling standards, and data analysis.
Implements and optimizes reinforcement learning workflows using the Stable Baselines3 PyTorch library.
Refactors trading system logic to transform rigid trade rejections into intelligent, constraint-based position sizing.
Quantifies uncertainty in estimates by generating plausible ranges to enable more reliable data-driven decision making.
Analyzes and designs self-sustaining systems through the lens of collective catalysis and network closure.
Refines probability estimates and decision-making by systematically updating beliefs as new data or evidence emerges.
Integrates high-performance inference and LoRA fine-tuning for 100+ open-source LLMs via OpenAI-compatible APIs and the firectl CLI.
Implements high-throughput machine learning inference patterns for processing large-scale datasets on a scheduled basis.
Identifies and manages risks in fat-tailed distributions where extreme events occur more frequently than standard models predict.
Adjusts and optimizes aggregated probability forecasts to compensate for crowd conservatism and improve predictive accuracy.
Architects autonomous AI agent systems using structured reasoning patterns, memory management, and resilient tool integration.
Quantifies the accuracy of probabilistic predictions to improve decision-making and calibration through systematic feedback.
Prevents false positive pattern recognition in data and visual analysis by distinguishing genuine signals from cognitive illusions.
Implements a decoupled architecture for pre-computing machine learning predictions at scheduled intervals to optimize costs and serving latency.
Identifies long-term societal and structural shifts through bottom-up pattern detection and massive data aggregation.
Optimizes Retrieval-Augmented Generation architectures through advanced semantic chunking, hybrid search strategies, and vector embedding pipelines.
Identifies the minimal computational structure and causal states needed to predict complex system behavior from observed data streams.
Prevents flawed decision-making by identifying and debunking illusory patterns or spurious correlations in data and observations.
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