Atlas Cloud Media

Overview

Use Atlas Cloud's asynchronous media API to generate images or videos. This source-only skill describes model discovery, schema validation, task submission, bounded polling, and safe output retrieval; it does not bundle an SDK, executable, or hosted runtime.

When to Use This Skill

• Use when the user explicitly asks to generate an image or video with Atlas Cloud.
• Use when an existing workflow needs an Atlas Cloud image or video generation request and can make HTTPS calls.
• Use when model-specific parameters must be discovered before submission.
• Do not use this skill for OpenAI-compatible text chat; that API has a different base URL and contract.

Preconditions

1. Confirm the user is authorized to send the prompt and any reference media to a third-party service.
2. Explain that generation is paid and obtain approval before submitting a billable request.
3. Require ATLASCLOUD_API_KEY to be present in the environment. Never ask the user to paste it into chat, source files, command history, or logs.
4. Confirm the output directory and whether the user wants image generation, video generation, or both.

API Contract

Generation and polling requests use these headers:

Authorization: Bearer $ATLASCLOUD_API_KEY
Content-Type: application/json

The model catalog is public. Each catalog entry includes a schema URL; fetch that schema and validate parameters against it before sending a paid request. Do not guess parameters from another model, because names such as size, ratio, aspect_ratio, image, and image_url are model-specific.

Workflow

0. Create a Private Per-Run Workspace

Run the remaining shell snippets in the same shell session. Create a private directory before writing prompts, responses, prediction IDs, or signed URLs; the parameter expansion in later steps fails closed when this setup was skipped.

umask 077
atlas_tmp_dir=$(mktemp -d "${TMPDIR:-/tmp}/atlas-cloud-media.XXXXXXXX") || exit 1
chmod 700 -- "$atlas_tmp_dir"
trap 'rm -rf -- "$atlas_tmp_dir"' EXIT

1. Discover and Validate a Model

Fetch the catalog, filter by type (Image or Video), and match the user's requested capability. Read the selected entry's schema, verify that all required fields are present, and show the model and billable action to the user before submission.

Example discovery request:

curl --fail --silent --show-error \
  "https://api.atlascloud.ai/api/v1/models" \
  --output "${atlas_tmp_dir:?run private workspace setup first}/models.json"

jq -r '.data[] | select(.type == "Image") | [.model, .displayName, .schema] | @tsv' \
  "$atlas_tmp_dir/models.json"

2. Submit One Generation Task

Build the JSON body in a file so that quoting is deterministic and request details can be reviewed without exposing the API key.

Image example using a catalog-confirmed model:

jq -n \
  --arg model "qwen-image-3.0/text-to-image" \
  --arg prompt "A paper-cut city map in blue and white, clean editorial style" \
  '{model: $model, prompt: $prompt, size: "1024*1024", n: 1}' \
  > "${atlas_tmp_dir:?run private workspace setup first}/request.json"

curl --fail --silent --show-error \
  --request POST \
  "https://api.atlascloud.ai/api/v1/model/generateImage" \
  --header "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  --header "Content-Type: application/json" \
  --data @"$atlas_tmp_dir/request.json" \
  --output "$atlas_tmp_dir/submit.json"

Video example using a catalog-confirmed model:

jq -n \
  --arg model "bytedance/seedance-2.0-fast/text-to-video" \
  --arg prompt "A small paper boat crossing a calm pond, locked camera" \
  '{
    model: $model,
    prompt: $prompt,
    duration: 4,
    resolution: "480p",
    ratio: "16:9",
    generate_audio: false,
    watermark: false
  }' > "${atlas_tmp_dir:?run private workspace setup first}/request.json"

curl --fail --silent --show-error \
  --request POST \
  "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  --header "Authorization: Bearer $ATLASCLOUD_API_KEY" \

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