Atlas cloud media

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 $ATLASCLOUD_API_KEY" \
  --header "Content-Type: application/json" \
  --data @"$atlas_tmp_dir/request.json" \
  --output "$atlas_tmp_dir/submit.json"

Check that .data.id is a non-empty string before polling. Treat a non-2xx response or a missing ID as submission failure; do not retry a billable request automatically because the original task may still have been accepted.

3. Poll with a Deadline

Poll every three seconds. Accept completed or succeeded as success, stop on failed or timeout, and stop after ten minutes. Preserve the prediction ID for diagnostics, but never log request headers or the API key.

prediction_id=$(jq -er '.data.id | select(type == "string" and length > 0)' \
  "${atlas_tmp_dir:?run private workspace setup first}/submit.json")

for attempt in $(seq 1 200); do
  sleep 3
  curl --fail --silent --show-error \
    "https://api.atlascloud.ai/api/v1/model/prediction/$prediction_id" \
    --header "Authorization: Bearer $ATLASCLOUD_API_KEY" \
    --output "$atlas_tmp_dir/prediction.json"

  status=$(jq -r '.data.status // "unknown"' "$atlas_tmp_dir/prediction.json")
  case "$status" in
    completed|succeeded) break ;;
    failed|timeout)
      jq -r '.data.error // "Atlas Cloud generation failed"' \
        "$atlas_tmp_dir/prediction.json" >&2
      exit 1
      ;;
  esac
done

test "$status" = "completed" || test "$status" = "succeeded"

4. Download and Verify the Output

Read the first HTTPS URL from .data.outputs. Atlas output URLs are temporary, so download promptly. Do not send Authorization or any other Atlas request headers to the output host. Reject non-HTTPS URLs and inspect the downloaded file's content type and size before treating it as a valid deliverable.

output_url=$(jq -er '.data.outputs[0] | select(startswith("https://"))' \
  "${atlas_tmp_dir:?run private workspace setup first}/prediction.json")

curl --fail --silent --show-error --location \
  "$output_url" \
  --output "$atlas_tmp_dir/output.bin"

test -s "$atlas_tmp_dir/output.bin"
file "$atlas_tmp_dir/output.bin"

# ATLAS_OUTPUT_DIR must be the user-approved destination. Resolve it to a
# physical directory, copy into an exclusive same-directory temporary file,
# then create the final name with one atomic hard-link operation. `ln` fails if
# any target already exists, including a dangling symlink.
atlas_output_dir=$(cd -- "${ATLAS_OUTPUT_DIR:?set the approved output directory}" && pwd -P) || exit 1
atlas_output_path="$atlas_output_dir/atlas-output.bin"
if ! (
  set -eu
  umask 077
  atlas_publish_tmp=$(mktemp "$atlas_output_dir/.atlas-output.XXXXXXXX")
  trap 'rm -f -- "$atlas_publish_tmp"' EXIT
  cp -- "$atlas_tmp_dir/output.bin" "$atlas_publish_tmp"
  chmod 644 -- "$atlas_publish_tmp"
  ln -- "$atlas_publish_tmp" "$atlas_output_path"
); then
  printf '%s\n' "Refusing to overwrite or redirect $atlas_output_path" >&2
  exit 1
fi

Rename the file only after its detected type is known. Report the local path, model ID, dimensions or duration, and whether the output passed basic playback or decode validation.

Failure Handling

• 401 or 403: stop and ask the user to verify access. Do not print or rotate

the key automatically.

• 400 or 422: fetch the model's current schema and correct the payload. Do

not blindly resubmit.

• 429: stop and report rate limiting; respect any Retry-After value.
• 5xx or network timeout: first poll a known prediction ID. Do not create a

second paid task unless the user approves the possible duplicate charge.

• failed or timeout: report the sanitized service error and prediction ID;

do not claim an output was generated.

• Missing or invalid media: keep the original response for diagnosis, do not

overwrite an existing destination, and do not mark the task complete.

Best Practices

• Use the public catalog and per-model schema immediately before generation.
• Keep request and response artifacts in one private per-run directory and let

the exit trap remove them, especially prediction payloads with signed URLs.

• Submit one task at a time unless the user explicitly approves a batch and its

cost.

• Keep prompts, reference-media rights, and provider content policies visible

in the approval step.

• Use short polling intervals only while a task is active; always enforce a

deadline.

• Download expiring outputs promptly and validate them locally.
• Never forward the Atlas bearer token to CDN or user-supplied URLs.

Limitations

• This source-only skill provides operational instructions, not an installed

Atlas Cloud client, bundled script, queue worker, or retry service.

• Available models, schemas, prices, and output retention can change; the live

catalog is authoritative.

• Model availability does not guarantee a prompt or reference asset is allowed.
• Generation is asynchronous and may take several minutes.
• Basic file checks do not replace human review of media quality, factual

accuracy, rights, or safety.

Security & Safety Notes

• Treat prompts and uploaded media as data sent to a third party; obtain user

consent first and avoid unnecessary personal or confidential information.

• Keep credentials in environment variables or an approved secret manager.
• Redact authorization headers and signed output URLs from logs and bug reports.
• Never execute downloaded media as code, and never use this workflow for bulk

hosting or unrelated file transfer.

• Follow applicable laws, provider policies, and intellectual-property rights.

Common Pitfalls

• Problem: A payload copied from another model returns a validation error.

Solution: Fetch the selected catalog entry's current schema and rebuild the request from that schema.

• Problem: A network timeout causes a duplicate paid request.

Solution: Preserve and poll the original prediction ID before considering a resubmission.

• Problem: The downloaded file is HTML or JSON instead of media.

Solution: Check the HTTP status, content type, file signature, and size before renaming or publishing it.

• Problem: Output download leaks the API key to another host.

Solution: Use a fresh download request with no Atlas authorization header.

Related Skills

• @video-router - Decide whether a request should use generated video before

submitting a billable task.

• @image-studio - Plan and review image-production work around generated

assets.