Loading...
Loading...
Found 13 Skills
Use as the fallback for custom HyperFrames HTML video composition authoring when no specialized workflow fits. Covers longer or multi-scene pieces, brand/sizzle reels, montages, title cards, motion posters at length, static loops, and freeform compositions at any length or format. Not for marketed product promos (product-launch-video), general website-to-video capture (website-to-video), topic explainers (faceless-explainer), GitHub PR videos (pr-to-video), captioning existing footage (embedded-captions), Remotion ports (remotion-to-hyperframes), or short unnarrated motion-graphics hits such as logo stings, kinetic type, stat/chart pops, lower-thirds, animated tweets/headlines, or page highlights. If a specialized workflow clearly fits the input, prefer it (see /hyperframes-read-first); use this only as the input/length-agnostic fallback.
Use to summarize a recorded video via the LVS summarization microservice (HITL-gated) with a VLM fallback. Not for live RTSP captioning or incident-range reports.
Write structured VGL (Visual Generation Language) JSON prompts for Bria's FIBO image generation models. Use this skill when creating detailed image descriptions in JSON format for text-to-image generation, image editing, inpainting, outpainting, background generation, or captioning. Triggers include requests to write structured prompts, create VGL JSON, describe images for AI generation, or work with Bria/FIBO's structured_prompt format. Also use when converting natural language image requests into the deterministic JSON schema required by FIBO models.
Multimodal AI processing via Google Gemini API (2M tokens context). Capabilities: audio (transcription, 9.5hr max, summarization, music analysis), images (captioning, OCR, object detection, segmentation, visual Q&A), video (scene detection, 6hr max, YouTube URLs, temporal analysis), documents (PDF extraction, tables, forms, charts), image generation (text-to-image, editing). Actions: transcribe, analyze, extract, caption, detect, segment, generate from media. Keywords: Gemini API, audio transcription, image captioning, OCR, object detection, video analysis, PDF extraction, text-to-image, multimodal, speech recognition, visual Q&A, scene detection, YouTube transcription, table extraction, form processing, image generation, Imagen. Use when: transcribing audio/video, analyzing images/screenshots, extracting data from PDFs, processing YouTube videos, generating images from text, implementing multimodal AI features.
Use to summarize a recorded video via the LVS summarization microservice (HITL-gated) with a VLM fallback. Not for report generation or live RTSP captioning.
Use this skill when deploying standalone RT-VLM dense captioning or calling its REST API (uploads, captions, streams, chat-completions, Kafka). Not for VSS profile deploy or video-search ingestion.
Use to run top-level VSS fusion search on archived video, or to ingest video files / RTSP streams for search. Not for ad-hoc Q&A or live captioning.
Process and generate multimedia content using Google Gemini API for better vision capabilities. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (better image analysis than Claude models, captioning, reasoning, object detection, design extraction, OCR, visual Q&A, segmentation, handle multiple images), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image with Imagen 4, editing, composition, refinement), generate videos (text-to-video with Veo 3, 8-second clips with native audio). Use when working with audio/video files, analyzing images or screenshots (instead of default vision capabilities of Claude, only fallback to Claude's vision capabilities if needed), processing PDF documents, extracting structured data from media, creating images/videos from text prompts, or implementing multimodal AI features. Supports Gemini 3/2.5, Imagen 4, and Veo 3 models with context windows up to 2M tokens.
Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.
Use to run top-level VSS fusion search on archived video, or to ingest video files / RTSP streams for search. Do NOT use for ad-hoc visual Q&A (use vss-ask-video), live captioning (use vss-deploy-dense-captioning), or video summarization and reports (use vss-summarize-video).
AI-powered video captioning — transcribe speech, optimize/translate subtitles, burn into video with beautiful customizable styles (ASS outline or rounded background). Free ASR and translation included.
Multi-step video annotation pipeline that turns raw videos into Chain-of-Thought training data — multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with reasoning traces, via VLM/LLM distillation. Use when the user wants to "create video training data", "generate video QA datasets", "build CoT reasoning traces from videos", "auto-label videos", or run the video_reasoning_annotation pipeline. Triggers include "video annotation", "video CoT", "video QA", "chain-of-thought", "video captioning pipeline", "video distillation".