Loading...
Loading...
Found 85 Skills
End-to-end AI video generation - create videos from text prompts using image generation, video synthesis, voice-over, and editing. Supports OpenAI DALL-E, Replicate models, LumaAI, Runway, and FFmpeg editing.
Process video files with ffmpeg automation. Use when: compressing videos for upload; extracting audio from video; resizing for social formats; clipping segments; merging multiple videos; generating thumbnails
Complete subtitle and caption system for FFmpeg 7.1 LTS and 8.0.1 (latest stable, released 2025-11-20). PROACTIVELY activate for: (1) Burning subtitles (hardcoding SRT/ASS/VTT), (2) Adding soft subtitle tracks, (3) Extracting subtitles from video, (4) Subtitle format conversion, (5) Styled captions (font, color, outline, shadow), (6) Subtitle positioning and alignment, (7) CEA-608/708 closed captions, (8) Text overlays with drawtext, (9) Whisper AI automatic transcription (FFmpeg 8.0+ with VAD, multi-language, GPU), (10) Batch subtitle processing. Provides: Format reference tables, styling parameter guide, position alignment charts, Whisper model comparison, VAD configuration, dynamic text examples, accessibility best practices. Ensures: Professional captions with proper styling and accessibility compliance.
Full production pipeline — story to scenes, Z-Image start frames, Qwen Edit end frames, WAN FLF video clips, ffmpeg concatenation
Extract frames from video files using ffmpeg for AI/LLM analysis. Use when (1) the user asks to analyze, describe, or summarize a video file, (2) the user wants to extract frames or screenshots from a video, (3) the user provides a video file (.mp4, .mov, .avi, .mkv, .webm, etc.) and asks questions about its visual content, (4) the user wants to identify scenes, objects, or events in a video, (5) the user wants timestamps overlaid on extracted frames for temporal reference. Converts video into JPEG frames that can be attached to LLM prompts as images. Requires ffmpeg on PATH. Supports scene-change detection, model-aware optimization (Claude/OpenAI/Gemini), quality presets (efficient/balanced/detailed/ocr), grayscale and high-contrast OCR mode, and automatic FPS calculation via --max-frames.
Assemble final video from generated clips, audio, and assets using FFmpeg or Remotion. Handles concatenation, audio mixing, transitions, titles, and export. Use when combining multiple production outputs into a final deliverable.
Expert guidance for video editing with ffmpeg, encoding best practices, and quality optimization. Use when working with video files, transcoding, remuxing, encoding settings, color spaces, or troubleshooting video quality issues.
Expert in video processing, streaming protocols (HLS/DASH/WebRTC), and FFmpeg automation. Specializes in building scalable video infrastructure.
Command-line interface for Openscreen — a screen recording editor. A stateful CLI for editing screen recordings with zoom, speed ramps, trim, crop, annotations, and polished exports. Built on the Openscreen JSON project format with ffmpeg as the rendering backend. Designed for AI agents and power users who need programmatic video editing.
Use this skill when analyzing existing video files using FFmpeg and AI vision, extracting frames for design system generation, detecting scene boundaries, analyzing animation timing, extracting color palettes, or understanding audio-visual sync. Triggers on video analysis, frame extraction, scene detection, ffprobe, motion analysis, and AI vision analysis of video content.
Use when the user has a video + an SRT and wants the subtitles either burned into the pixels (libass, always-visible) or soft-muxed as a togglable track. Also handles the final composite step for the localization pipeline — burn subs, mix a dub track, and keep the original audio as a low-volume bed, all in ONE ffmpeg encode (no cascade). Verifies libass availability and auto-downloads a static evermeet ffmpeg build when Homebrew's stripped binary lacks it. Triggers — "烧字幕", "硬字幕", "burn subtitles", "burn-in subs", "embed subtitle", "soft mux SRT", "把字幕烧进视频", "做最终合成".
This skill should be used to watch a long-running background job (ffmpeg/media encode, qmd or other embedding/vector-DB run, batch agent/LLM pipeline, or a real-browser/agent-browser daemon) until it finishes or wedges, then deliver a verdict (done, needs-attention, or blocked) plus the exact next command, without burning dozens of manual poll commands. Triggers on "babysit this job", "watch this until it's done", "ping me when the encode/embed/batch finishes", "is this background process stuck", "monitor this ffmpeg/qmd run", or any request to wait on a long-running process and be told when it's complete or hung.