Results for “axonaut”

Axum (Rust) web framework patterns for production APIs: routers/extractors, state, middleware, error handling, tracing, graceful shutdown, and testing

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Get best practices for XUnit unit testing, including data-driven tests

Write effective unit tests with XUnit, covering standard and data-driven testing approaches.

You are an expert in Axum, the web framework built on top of Tokio and Tower by the Tokio team. You help developers build high-performance, type-safe APIs and web services using Axum's extractor-based handler system, middleware via Tower layers, WebSocket support, and compile-time route validation — achieving C-level performance with Rust's memory safety guarantees.

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Provides expert guidance for fine-tuning LLMs with Axolotl, covering YAML configs, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, and multimodal support.

xUnit.net testing framework with Fact, Theory, fixtures, DI, and mocking with Moq and NSubstitute. Covers .NET testing best practices. USE WHEN: user mentions "xUnit", ".NET testing", "Fact", "Theory", "InlineData", "Moq", "NSubstitute", "C# unit test" DO NOT USE FOR: NUnit - use `nunit`, Vitest - use `vitest`, Jest - use `jest`, Playwright - use `playwright`

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Perform time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a scikit-learn compatible Python toolkit.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Rule (ahak-rule)

Runs time series machine learning with the aeon library — classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search via scikit-learn compatible APIs. Use when working with temporal data, sequential patterns, or time-indexed observations (univariate or multivariate) that need specialized algorithms beyond standard ML approaches. Part of the AlterLab Academic Skills suite.

Commit the session to durable vault memory and prepare a clean resume point

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Comprehensive guide to axum. Master the concepts, implementation, best practices, and real-world applications of axum in professional environments.

Develops and backtests quantitative trading strategies for the Chinese securities market using the QMT terminal's built-in Python framework, covering data retrieval, order placement, and position management.