Explorando:

affaan-m

La habilidad de agente de IA Nutrient es una herramienta de procesamiento de documentos que utiliza la API de Nutrient para convertir, extraer y redactar documentos.

TharushaKula

FFmpeg automation for cutting, trimming, concatenating videos. Audio mixing, timeline editing, transitions, effects. Export optimization for YouTube, social media. Subtitle handling, color grading, batch processing. Use for videogen projects, content creation, automated video production. Activate on video editing, FFmpeg, trim video, concatenate, transitions, export optimization. NOT for real-time video editing UI, 3D compositing, or motion graphics.

tuanductran

SoulMap AI: a content-first reflective companion with a curated Markdown knowledge base, Python detectors, and tooling to validate and bundle agent-ready skills.

alphaonedev

nano-pdf es una herramienta de procesamiento de PDF que permite la extracción de texto, minería de texto y OCR

kreuzberg-dev

git-standards is a high-performance HTML to Markdown converter that maintains Conventional Commits 1.0.0 standards, benefiting developers with structured data extraction from 56+ document formats.

affaan-m

El patrón de cache de hash de contenido utiliza hashes SHA-256 para almacenar resultados de procesamiento de archivos, mejorando la eficiencia y reduciendo la carga de trabajo

affaan-m

Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.

wyattowalsh

Data Extraction (from https://stats.nba.com) and Processing Scripts to Produce the NBA Database on Kaggle (https://kaggle.com/wyattowalsh/basketball)

huggingface

This skill enables running Python workloads on Hugging Face cloud infrastructure, ideal for developers. It handles data processing, batch inference, experiments, and model training without requiring local GPU/TPU setup.

dlt-hub

Review PR es una herramienta de carga de datos que facilita la carga de datos

huggingface

This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.

optave

Audit codebase files against the 4-pillar quality manifesto using RECON work batches, with batch processing and context budget management (Titan Paradigm Phase 2)