Skill UI

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Guides researchers through ten structured ideation frameworks to move from vague curiosity to actionable, defensible research proposals, supporting exploration of new problem spaces, pivoting projects, and surfacing high-impact angles.

Gastown orchestrates parallel Claude Code agents, providing CLI tools to install, set up workspaces, track beads, manage agent lifecycles, recover from crashes, and merge outputs via the Refinery while reporting health and statuses.

Sets up Lindy AI’s enterprise role-based access control so teams assign Owners, Editors, and Viewers to workspaces, manage visibility, audit agent access, and enforce scoped keys across integrations.

Fully managed, auto-scaling vector database tailored for production AI workloads such as RAG, recommendations, and semantic search, offering hybrid dense+sparse search, namespaces, metadata filters, and <100ms latency on serverless infrastructure.

Diagnoses new or existing AI workspaces in Spanish, determines whether the initiative is a Pure Engine or Evolution effort, and produces implementation roadmaps plus expert squad recommendations before development begins.

Designs and optimizes AI agent execution by defining action spaces, tools, observation formats, recovery contracts, and context-budget strategies to boost completion rates and reliability.

Fetch Hugging Face paper pages or arXiv entries with markdown and API calls to pull abstracts, metadata, authorship, upvotes, and linked models/datasets/spaces so you can summarize or analyze AI research efficiently.

Trackio is a robust library designed for comprehensive monitoring and management of Machine Learning experiments. It allows users to log key training metrics, set structured alerts (e.g., loss divergence, NaN gradients), and visualize results over time. The system supports integration via Python API for logging/alerting, and a powerful CLI for programmatic metric retrieval and dashboard synchronization to Hugging Face Spaces. Ideal for autonomous AI agent workflows and detailed research tracking.