Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Implements sophisticated Retrieval-Augmented Generation architectures to ground AI responses in real-time external knowledge and eliminate hallucinations.
Analyzes and extracts structured insights from video files using AI-powered understanding and timestamped key moment identification.
Converts audio and video files into text transcripts with word-level timestamps using WhisperX.
Verifies mathematical claims and generates Lean 4 formal proofs or counterexamples using the Harmonic Aristotle API.
Simplifies the design and analysis of complex control systems using the C11-based ctrlsys library.
Implements comprehensive audio processing and text-to-speech generation using the Google Gemini API.
Analyzes and extracts insights from images, videos, and audio files using advanced AI models.
Transforms raw datasets into professional-grade charts, graphs, and visual plots using intelligent data analysis.
Transcribes audio files locally using whisper.cpp with CUDA acceleration for high-performance speech-to-text conversion.
Predicts market price and quantity changes by analyzing the interaction between producer supply and consumer demand at equilibrium.
Analyzes complex optimization problems using evolutionary landscape metaphors to identify local traps and global optima.
Facilitates the development of AI-powered applications using the OpenAI SDK, covering GPT-5 models, Responses API, and advanced tool calling.
Enhances decision-making through a multi-model adversarial reasoning protocol and reliability-weighted aggregation.
Provides expert guidance and routine lookup for the ctrlsys control systems library, covering LQR design, Kalman filtering, and system identification.
Provides expert guidance on control system design, analysis, and identification using the ctrlsys library.
Extracts structured training pairs from academic peer reviews and source documents to build high-quality datasets for LLM fine-tuning.
Implements real-time machine learning architectures for processing unbounded data streams with sub-100ms prediction latency.
Automates the creation of production-grade Pegasus scientific workflows from high-level pipeline descriptions.
Implements continuous model updates and incremental learning patterns to handle evolving data streams without full retraining.
Leverages the data flywheel mental model to build compounding competitive advantages through automated machine learning and user-generated signals.
Implements high-throughput machine learning inference patterns for processing large-scale datasets on a scheduled basis.
Corrects probability judgments by integrating statistical base rates with case-specific information to avoid common cognitive biases.
Implements Schmidhuber's compression progress theory to provide intrinsic curiosity rewards for autonomous AI learning and exploration.
Validates hypotheses and scientific theories by ensuring they are testable and capable of being proven false through rigorous experimentation.
Visualizes solar observation data, EUV imagery, and machine learning model outputs using SunPy and Matplotlib.
Implements real-time machine learning prediction patterns for high-throughput data streams with sub-second latency.
Models complex system dynamics using stocks, flows, and feedback loops to quantitatively predict behavior and test policy interventions.
Performs systematic testing of input variables to identify key drivers and assess model risk across finance, engineering, and strategy.
Extracts structured training examples from document sets to create high-quality datasets for teaching LLMs specific tasks or styles.
Integrates high-performance inference and LoRA fine-tuning for 100+ open-source LLMs via OpenAI-compatible APIs and the firectl CLI.
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