Killer-Skills 디렉터리에서 수천 개의 AI Agent 스킬을 탐색하고 설치하세요. Claude Code, Windsurf, Cursor 등 지원.
This AI agent skill processes documents using the Nutrient DWS API, benefiting developers by automating tasks such as format conversion, text extraction, and data redaction. It helps with AI coding by streamlining document workflows.
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.
SoulMap AI: a content-first reflective companion with a curated Markdown knowledge base, Python detectors, and tooling to validate and bundle agent-ready skills.
nano-pdf는 PDF 처리 AI 에이전트
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.
콘텐츠 해시 캐시 패턴은 SHA-256 해시를 사용하여 파일 처리 결과를 캐시하는 기술로, 파일 이름 변경과 자동 만료를 지원
Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.
Data Extraction (from https://stats.nba.com) and Processing Scripts to Produce the NBA Database on Kaggle (https://kaggle.com/wyattowalsh/basketball)
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.
Review PR
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.
Audit codebase files against the 4-pillar quality manifesto using RECON work batches, with batch processing and context budget management (Titan Paradigm Phase 2)