Produce media assets using AnyCap: generate images, videos, music, speech, dialogue, and complete audio scenes from text or reference inputs, refine images through interactive visual annotation, and deliver finished assets. Covers the full production workflow from concept to delivery across all medi

AnyCap Media Production Read this entire file before starting. It covers the full production workflow across image, video, music, and audio including iterative refinement with human feedback. Workflow guide for producing media assets with AnyCap. Covers image, video, music, and audio from initial generation through iterative refinement to delivery. This skill is about how to produce media . For CLI command reference and parameters, read the anycap cli skill. Prerequisites AnyCap CLI must be installed and authenticated. Read the anycap cli skill if setup is needed. Quick Reference Media Generate Refine Typical duration Image anycap image generate Annotate + image to image 5 30s Video anycap video generate Re generate with adjusted params 30 120s Music anycap music generate Re generate with adjusted prompt 30 90s Audio anycap audio generate Re generate with adjusted prompt or references Model dependent All generation commands follow the same pattern: Always choose model IDs from the live model catalog and inspect the schema for the selected mode before relying on model specific parameters. Always use o with a descriptive filename. Image Production Text to Image Generate an image from a text prompt: Image to Image (Edit / Transform) Use mode image to image with a reference image to edit or transform an existing image: Reference images can be local paths or URLs. The CLI handles upload automatically. Multiple Reference Images Some models accept multiple reference images for style transfer, composition blending, or subject driven generation. Use JSON array syntax to pass multiple files: Tips: Use JSON array syntax '["path1","path2"]' repeating param images= overwrites rather than appends. Local file paths inside the array are auto uploaded, same as single file mode. Not all models support multiple references. Check the model schema first. When unsupported, the model typically uses only the first image. Iterative Refinement with Annotation When text prompts alone cannot describe the desired edit precisely ("move this", "remove that specific thing", "change the color of this area"), use the annotation workflow. For the full annotation guide including URL/video review, headless access, recording analysis, and multi user collaboration read the anycap human interaction skill. Step 1: Generate or Use an Existing Image Step 2: Annotate Open the annotation tool so the human can visually mark regions, describe desired changes, and optionally record a narrated walkthrough. Multiple users can collaborate on the same session in real time. For agent workflows (non blocking, recommended): Show the URL to the human and ask them to annotate. Multiple people can open the same URL to collaborate. Wait for the human to confirm they are done, then: For interactive sessions (human is at the terminal): The annotation tool supports four tools: Rectangle ( R ), Arrow ( A ), Point ( P ), Freehand ( F ). Each annotation gets a numbered marker and a text label. Step 3: Build a Prompt from Annotations The annotation output contains structured data. Translate each label into a coherent prompt: Prompt: " 1: Replace the desk with a standing desk. 2: Add a cat sitting at the marked position. 3: Transform the outlined area into a bookshelf. Keep all other elements unchanged." Rules: Reference each annotation by its number ( 1, 2, etc.) Include the human's exact label text Add "Keep all other elements unchanged" to preserve unmodified areas Step 4: Apply the Edit Use the annotated image (with visual markers) as the reference: Step 5: Iterate If the human wants more changes, use the latest version as input and repeat from Step 2. Version filenames ( v1 , v2 , v3 ) so the human can compare and revert. Image Tips Start broad, refine narrow. First generation nails the composition. Annotation iterations handle targeted adjustments. One thing at a time. If multi region edits produce poor results, try one annotation per pass. Annotated image only. Pass only the annotated image as the reference. Most models understand numbered markers and remove them from the output. Video Production Text to Video Image to Video Animate a still image: This is powerful for combining with image generation: generate a still image first, then animate it. Video Production Workflow For best results with image to video: 1. Generate a high quality still image first (iterate with annotation if needed) 2. Use the final image as the reference for video generation 3. Keep the video prompt focused on motion and camera movement, not scene description Video Tips Video generation takes 30 120s. Use async execution when your runtime supports it. Check model schema for supported parameters ( aspect ratio , duration , etc.). Different models excel at different styles. Check available models with anycap video models . Music Production Text to Music Music generation may return multiple clips. Extract the first: Music Tips Be specific about genre, tempo, instruments, and mood in prompts. Music generation takes 30 90s. Use async execution when possible. Check model parameters via schema some models support duration , genre , tags . Audio Production Audio generation covers speech synthesis, dialogue, and complete audio scenes generated from text or reference media. A scene can combine voices, background music, ambience, and sound effects; use the separate music capability when the deliverable is primarily a song or instrumental track. Use the live schema for reference limits and audio controls. Local reference files are uploaded automatically. The JSON output preserves duration, size, subtitle, and usage data when the provider returns them. Delivery When the asset is ready, deliver using the appropriate method: Multi Media Production Example A complete workflow producing a promotional package:

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