Skill UI

Guides developers through scikit-learn workflows for classical supervised and unsupervised tasks, data preprocessing, evaluation schemes, hyperparameter tuning, and end-to-end pipelines when building interpretable production models.

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

Transforms natural-language prompts into Excalidraw JSON diagrams, supporting flowcharts, relationships, mind maps, architecture, DFD, swimlane, class, sequence, and ER views; ideal when users ask to visualize processes or system structures and need editable Excalidraw files.

This guide provides a comprehensive walkthrough for developing and deploying Apify Actors. Actors are serverless, containerized applications designed for powerful tasks like web scraping, data processing, and complex automation. Learn the mandatory setup, template selection (Python, JS, TS), step-by-step development workflow, and crucial security best practices for building robust, production-ready data pipelines.

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.

A powerful tool for architecting production-grade multi-agent workflows. It allows users to define complex, multi-step AI pipelines using structured patterns (sequential, parallel, hierarchical). Key features include defining explicit handoff contracts, implementing robust failure handling, and managing context/cost discipline across long-running processes, ensuring reliable and scalable AI system deployment.

Generates practical and robust CI/CD pipelines by programmatically detecting the project's entire technology stack (language, runtime, tooling). It provides fast baseline setup for GitHub Actions or GitLab CI, ensuring repeatability, environment awareness, and alignment with project dependencies. Ideal for onboarding new repositories, standardizing DevOps workflows, or migrating existing deployment processes.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

This expert skill guides you through the entire process of creating, optimizing, and structuring automated email flows, including welcome, nurture, and re-engagement sequences. It ensures every email has a clear purpose, defined metrics, and a structured CTA, allowing you to move users efficiently through the entire customer lifecycle and maximize conversion rates.

This guide outlines a systematic process for transforming any recurring technical pattern, complex debugging solution, or workflow into a standalone, portable skill. It details the multi-step extraction workflow, including pattern identification, structured documentation generation (SKILL.md), and quality control checks to ensure the skill can be reused across different projects.

A comprehensive skill for fine-tuning and training large language and vision models using advanced techniques like TRL (SFT, DPO, GRPO) or Unsloth. This process runs entirely on managed Hugging Face cloud GPUs, eliminating the need for local GPU setup. It supports modern training workflows, model conversion (GGUF for local deployment), and ensures seamless artifact storage to the Hugging Face Hub.

This utility is designed to build reusable, composable command-line scripts for interacting with the Hugging Face API and the `hf` command line tool. It enables complex data workflows by supporting chaining, piping, and intermediate processing across models, datasets, and repositories, streamlining the automation of Hub tasks.