AI Agent Skills

The Peaks Consolidation is equipped with state-of-the-art algorithms and data structures that support high-performance databending exercises. It specializes in managemen…

AI-CryptoTrader is a state-of-the-art cryptocurrency trading bot that uses ensemble methods to make trading decisions based on multiple sophisticated algorithms. Built w…

This Jesse strategy is based on financial astrology cycles detected with advanced machine learning techniques for the crypto-currencies symbols: ADA, BAT, BNB, BTC, DASH…

🪁 A fast Adaptive Machine Learning library for Time-Series, that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined wit…

jhj0517/finetuning-notebooks is a high-star GitHub project relevant to AI agent workflows.

Comprehensive guide to develop and improve Agent Skill performance. Contains best practices for skill formatting (frontmatter and metadata), naming, descriptions, fine-t…

Created a continuous, homogeneous, and structured 10 GB dataset from self obtained collections of unstructured intraday financial data. Generated features from indicator…

Extract and preprocess tiles from whole-slide images (WSI) with histolab — OpenSlide-backed slide loading, tissue detection and masks, Random/Grid/Score tile extraction,…

Support financial data science workflow, manage large structured and unstructured data sets, and apply financial econometrics and machine learning

LoRA and LoKr fine-tuning for ACE-Step 1.5. Trains custom styles from 3-10 songs, manages trained adapters, and applies them during generation. Uses ACE-Step's built-in…

A skill to create, manage, and automate machine learning pipelines.

Deploy trained machine learning models as production-ready services using REST APIs, containers, serverless functions, and orchestration platforms.

Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs,…

Best practices for dataset preparation and PEFT/LoRA fine-tuning.

Use when training, fine-tuning, or validating Ultralytics YOLO models in Platform, cloud GPUs, or local code — model.train(), yolo train/val, remote metric streaming, ep…