Durchsuchen:

affaan-m

Die AI-Agent-Skill Nutrient ist ein Dokumentenverarbeitungstool, das die Nutrient-API verwendet, um Dokumente zu konvertieren, zu extrahieren und zu redigieren.

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 ist ein Tool für die Verarbeitung von PDF, das die Textextraktion, Textmining und OCR ermöglicht

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

Das Inhalt-Hash-Cache-Muster verwendet SHA-256-Hashes, um die Ergebnisse der Dateiverarbeitung zu speichern, wodurch die Effizienz verbessert und die Arbeitslast reduziert wird

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 ist ein Datenladewerkzeug, das das Laden von Daten erleichtert

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)