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Found 537 Skills
Advanced exploratory testing techniques with Session-Based Test Management (SBTM), RST heuristics, and test tours. Use when planning exploration sessions, investigating bugs, or discovering unknown quality risks.
An automated data exploration and visualization tool that provides a complete EDA solution from data loading to professional report generation. It supports multiple chart types, intelligent data diagnosis, modeling evaluation, and HTML report generation. Suitable for data analysis projects in fields such as healthcare, finance, e-commerce, etc.
Deep contextual grep for codebases. Expert at finding patterns, architectures, implementations, and answering "Where is X?", "Which file has Y?", and "Find code that does Z" questions. Use when exploring unfamiliar codebases, finding specific implementations, understanding code organization, discovering patterns across multiple files, or locating functionality in a project. Supports three thoroughness levels quick, medium, very thorough.
Generates multiple distinct design variants of a component or page, each with a completely different visual direction, then implements the chosen one. Use when the user asks to redesign, restyle, explore design options, create multiple visual directions, or compare design approaches for any UI element -- components, pages, sections, dashboards, landing pages, or full layouts.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
Fluxo de trabalho com IA em 3 fases — Explorar (brainstorm guiado), Planejar (plano com tarefas atômicas) e Executar (passo a passo). Ative esta skill sempre que o usuário digitar `/explorar`, `/planejar` ou `/executar`, ou quando tiver qualquer objetivo que envolva planejamento, criação ou dúvida aberta. Serve tanto para tarefas de código quanto para tarefas do mundo real (escrever, pesquisar, contatar, montar). Idioma pt-BR.
For discovering and understanding database structure, tables, columns, and relationships
Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.
Test software in the style of James Bach, pioneer of exploratory testing and context-driven testing. Emphasizes skilled human investigation, heuristics-based test design, and adapting to context rather than following rigid scripts. Use when designing test strategies, performing exploratory testing, or building thinking testers.
Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.