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Found 131 Skills
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
Guidance for training FastText text classification models with constraints on model size and accuracy. This skill should be used when training FastText models, optimizing hyperparameters, or balancing trade-offs between model size and classification accuracy.
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
Use when designing error handling, retry policies, timeout behavior, or failure classification in Python. Also use when code swallows exceptions, loses error context across boundaries, has unbounded retries, silent failures, or lacks idempotency guarantees on retried writes.
Analyze emotion — mood classification, energy, valence, genre detection
Guide incident response from detection to post-mortem using SRE principles, severity classification, on-call management, blameless culture, and communication protocols. Use when setting up incident processes, designing escalation policies, or conducting post-mortems.
Write Domain-Driven Design architecture models using DomainLang (.dlang files). Covers domains, bounded contexts, context maps, teams, classifications, terminology, relationships, namespaces, and imports. Use when creating DDD models, mapping bounded context relationships, documenting ubiquitous language, or generating .dlang files for strategic design.
TODO.md file output template examples for todo-task-planning command. Provides structured checklist format with task classification, status indicators, and research rationale.
Use when "CLIP", "Whisper", "Stable Diffusion", "SDXL", "speech-to-text", "text-to-image", "image generation", "transcription", "zero-shot classification", "image-text similarity", "inpainting", "ControlNet"
Create an interactive classification quiz MicroSim using p5.js where students read scenarios and classify them into the correct category from multiple choice options. Uses a data.json file for easy question editing. Ideal for teaching students to recognize patterns, identify types, or categorize examples across any subject domain.
Codified expertise for customs documentation, tariff classification, duty optimization, restricted party screening, and regulatory compliance across multiple jurisdictions. Informed by trade compliance specialists with 15+ years experience. Includes HS classification logic, Incoterms application, FTA utilization, and penalty mitigation. Use when handling customs clearance, tariff classification, trade compliance, import/export documentation, or duty optimization.
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in Node.js and browsers (with WebGPU/WASM) using pre-trained models from Hugging Face Hub.