6 boosters for "classification" — open source, verified from GitHub, ready to install
Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Helper scripts use PEP 723 inline dependencies. Run them with :
Transformers.js enables running state-of-the-art machine learning models directly in JavaScript, both in browsers and Node.js environments, with no server required. Use this skill when you need to: The pipeline API is the easiest way to use models. It groups together preprocessing, model inference,
Tiebreakers when the request is ambiguous: "embedding model" / "vector search" / "similarity" → [SentenceTransformer]. "rerank" / "ranker" / "two-stage" → [CrossEncoder]. "SPLADE" / "sparse" / "inverted index" → [SparseEncoder]. If still unclear, ask. Override only if the user specifies otherwise: T
FLAML (Fast Library for Automated Machine Learning & Tuning) is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, etc. and optimizes their performance. The repository uses pre-co
This skill automates DEVONthink document management on macOS through JXA and Python, enabling users to programmatically organize, search, convert, and manage records while integrating with citation systems like Zotero. It's ideal for researchers, academics, and knowledge workers who need to automate complex document workflows.
Automates bulk annotation of CSV data by applying OpenAI prompts to each row and adding results as a new column. Ideal for developers who need to classify, extract, or summarize spreadsheet data at scale.
