Skill UI

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.

Diagnose and fix failing or flaky Playwright tests via a systematic reproduce-trace-categorize-fix workflow, validate stability by repeated runs, and recommend CI safeguards to prevent regressions.

This guide demonstrates how to implement clean and robust asynchronous pipelines in TypeScript using `TaskEither` from `fp-ts`. It solves the common problem of deeply nested, unmanageable `try/catch` blocks, allowing developers to compose sequential API calls and operations while correctly handling success and failure paths in a functional, immutable manner. Ideal for complex data fetching and workflow orchestration.

TaskEither is a functional programming utility from fp-ts designed for handling asynchronous operations that might fail. It provides a robust, type-safe mechanism for managing complex async workflows, such as chained API calls or promise wrappers. Instead of relying on traditional try/catch blocks where errors can be lost, TaskEither forces the developer to explicitly handle both success and failure paths, making error management predictable and composable.

A rigorous framework designed to audit GitHub Actions workflows for exploitable security vulnerabilities. This skill focuses strictly on real-world attack patterns, requiring every identified finding to include a concrete exploitation scenario (Entry Point, Payload, Impact). Ideal for securing CI/CD pipelines against external attackers.

Use this skill to perform read-only API calls for exploring, previewing, searching, and filtering structured datasets hosted on Hugging Face. It allows users to validate dataset availability, list splits, paginate large datasets, and retrieve comprehensive metadata and statistics, making it ideal for data validation and content extraction workflows.

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 skill provides comprehensive tools for AI engineers and researchers to publish, manage, and link research papers directly on the Hugging Face Hub. It supports creating dedicated paper pages, linking papers to corresponding models and datasets, claiming authorship, and generating professional, markdown-based research articles with LaTeX support. Use it to streamline the entire academic publishing workflow from initial draft to final publication.

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.

Use this command to set up a structured multi-agent collaboration environment within AgentHub. It allows you to define the core task, specify the required number of agents, and configure detailed evaluation criteria (metrics, directions, and commands) for both quantitative benchmarking and qualitative LLM judging. This is the starting point for any agent competition or complex AI workflow.

Provides setup logic for integrating WhatsApp services, likely linking messaging workflows for automation or notifications.

This assistant streamlines the workflow for managing GitHub issues using the GitHub CLI. It guides users through structured processes to create, list, and view issues. When creating, it prompts for detailed information (including issue type, reproduction steps, and expected vs. actual behavior for bugs), ensuring high-quality issue reports. It also allows filtering and listing of existing issues.