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:

1. Discover models    anycap {cap} models
2. Check schema       anycap {cap} models <model> schema [--mode <mode>]
3. Generate           anycap {cap} generate --model <model> --prompt "..." -o output.ext

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:

bash

anycap image generate \
  --prompt "a cozy home office with a wooden desk, laptop, coffee cup, and plants by the window" \
  --model <model-id> \
  -o workspace-v1.png

Image-to-Image (Edit / Transform)

Use

--mode image-to-image

with a reference image to edit or transform an existing image:

bash

anycap image generate \
  --prompt "make it a watercolor painting" \
  --model <model-id> \
  --mode image-to-image \
  --param images=./photo.png \
  -o photo-watercolor.png

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:

bash

# Combine style from one image with composition from another
anycap image generate \
  --prompt "merge the architectural style of the first image with the color palette of the second" \
  --model <model-id> \
  --mode image-to-image \
  --param images='["./style-ref.png","./color-ref.png"]' \
  -o blended.png

# Mix local files and URLs
anycap image generate \
  --prompt "a portrait in the style of the reference images" \
  --model <model-id> \
  --mode image-to-image \
  --param images='["./local-ref.png","https://example.com/style-ref.jpg"]' \
  -o portrait-styled.png

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.

mermaid

graph TD
    A[Start: concept or existing image] --> B{Have an image?}
    B -->|No| C[Generate initial image]
    B -->|Yes| D[Human annotates the image]
    C --> D
    D --> E[Build prompt from annotations]
    E --> F[Generate with image-to-image]
    F --> G[Show result to human]
    G --> H{Satisfied?}
    H -->|Yes| I[Done -- deliver final asset]
    H -->|No| D

Step 1: Generate or Use an Existing Image

bash

anycap image generate \
  --prompt "a landing page hero banner with mountains and sunrise" \
  --model <model-id> \
  -o banner-v1.png

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):

bash

anycap annotate banner-v1.png --no-wait -o banner-v1-annotated.png
# Returns: {session, url, poll_command, stop_command}

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:

bash

# Fetch the result (single call, no loop)
anycap annotate poll --session <session_id>

# If recording exists, analyze it for visual understanding
anycap actions video-read --file .anycap/annotate/<session_id>/recording.webm \
  --instruction "Describe what changes the user wants"

# Clean up
anycap annotate stop --session <session_id>

For interactive sessions (human is at the terminal):

bash

anycap annotate banner-v1.png -o banner-v1-annotated.png
# Blocks until Done click, outputs annotation JSON

The annotation tool supports four tools: Rectangle (

), Arrow (

), Point (

), Freehand (

). 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:

json

{
  "annotations": [
    {"id": 1, "type": "rect", "label": "Replace with a standing desk"},
    {"id": 2, "type": "point", "label": "Add a cat sitting here"},
    {"id": 3, "type": "freehand", "label": "This area should be a bookshelf"}
  ]
}

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:

bash

anycap image generate \
  --prompt "#1: Replace the desk with a standing desk. #2: Add a cat. Keep all other elements unchanged." \
  --model <model-id> \
  --mode image-to-image \
  --param images=./banner-v1-annotated.png \
  -o banner-v2.png

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

bash

anycap video generate \
  --prompt "a cat walking on the beach at sunset, cinematic, slow motion" \
  --model <model-id> \
  -o cat-beach.mp4

Image-to-Video

Animate a still image:

bash

anycap video generate \
  --prompt "gentle camera pan across the landscape, wind blowing through trees" \
  --model <model-id> \
  --mode image-to-video \
  --param images=./landscape.png \
  -o landscape-animated.mp4

This is powerful for combining with image generation: generate a still image first, then animate it.

Video Production Workflow

mermaid

graph LR
    A[Text prompt] --> B[Generate image]
    B --> C{Animate?}
    C -->|Yes| D[image-to-video]
    C -->|No| E[Done]
    A --> F[text-to-video]
    F --> E
    D --> E

For best results with image-to-video:

Generate a high-quality still image first (iterate with annotation if needed)
Use the final image as the reference for video generation
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

bash

anycap music generate \
  --prompt "upbeat electronic track with synth leads and driving bass, 120 BPM" \
  --model <model-id> \
  -o background-track.mp3

Music generation may return multiple clips. Extract the first:

bash

anycap music generate --prompt "..." --model <model-id> -o track.mp3 \
  | jq -r '.outputs[0].local_path'

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.

bash

# Discover live modes and controls first
anycap audio models <model-id> schema --mode text-to-audio

# Generate speech with supporting ambience from text
anycap audio generate \
  --prompt 'A calm narrator says: "Welcome to the evening program." Soft room ambience underneath.' \
  --model <model-id> \
  --mode text-to-audio \
  -o evening-introduction.mp3

# Guide a new voice performance with reference audio
anycap audio generate \
  --prompt "create a new spoken welcome with the reference delivery style" \
  --model <model-id> \
  --mode audio-to-audio \
  --param audios=./reference.wav \
  -o guided-welcome.mp3

# Create a narrated audio scene from an image
anycap audio generate \
  --prompt "a guide describes this scene while matching ambience plays underneath" \
  --model <model-id> \
  --mode image-to-audio \
  --param images=./scene.png \
  -o narrated-scene.mp3

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:

bash

# Share via Drive (generates a shareable link)
anycap drive upload banner-final.png
anycap drive share banner-final.png

# Publish as a web page
anycap page deploy ./site-directory

Multi-Media Production Example

A complete workflow producing a promotional package:

bash

# 1. Generate hero image
anycap image generate \
  --prompt "modern SaaS dashboard with data visualizations, dark mode, purple accents" \
  --model <image-model-id> -o hero-v1.png

# 2. Refine via annotation (agent asks human to mark changes)
anycap annotate hero-v1.png --no-wait -o hero-v1-annotated.png
# ... human annotates, agent polls result ...
anycap image generate \
  --prompt "#1: Make the chart larger. #2: Change accent color to blue." \
  --model <image-model-id> --mode image-to-image \
  --param images=./hero-v1-annotated.png -o hero-v2.png

# 3. Create an animated version
anycap video generate \
  --prompt "slow zoom into the dashboard, data points animate in sequentially" \
  --model <video-model-id> --mode image-to-video \
  --param images=./hero-v2.png -o hero-animation.mp4

# 4. Generate background music
anycap music generate \
  --prompt "ambient tech background music, minimal, clean, 90 BPM" \
  --model <music-model-id> -o background-music.mp3

# 5. Deliver
anycap drive upload hero-v2.png hero-animation.mp4 background-music.mp3

anycap-media-production

NPX Install

Tags

SKILL.md Content

AnyCap Media Production

Prerequisites

Quick Reference

Image Production

Text-to-Image

Image-to-Image (Edit / Transform)

Multiple Reference Images

Iterative Refinement with Annotation

Step 1: Generate or Use an Existing Image

Step 2: Annotate

Step 3: Build a Prompt from Annotations

Step 4: Apply the Edit

Step 5: Iterate

Image Tips

Video Production

Text-to-Video

Image-to-Video

Video Production Workflow

Video Tips

Music Production

Text-to-Music

Music Tips

Audio Production

Delivery

Multi-Media Production Example