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atlas-cloud-media

Generate Atlas Cloud images and videos through its asynchronous media API with schema-first model selection and credential-safe polling.

Install / Use

npx skills add sickn33/agentic-awesome-skills --skill atlas-cloud-media

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

91/100

Supported Platforms

Claude Code
Gemini CLI
Cursor
OpenAI Codex

Our assessment of atlas-cloud-media

atlas-cloud-media scores 91/100 on our quality scale, 434th of 2,860 Development & Engineering skills we index (top 16%).

Its SKILL.md is 11 KB long, well organised into 21 sections with 7 code examples: a thorough specification that gives an agent plenty to work with.

With 46,875 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 3 days ago, so atlas-cloud-media is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

atlas-cloud-media compared with similar skills

All 4 of these similar skills score higher than atlas-cloud-media; compare them before choosing.

SkillScoreStarsUpdatedFormat
atlas-cloud-media (this skill)by sickn339146.9k3d agoSKILL.md
Agent-Reachby Panniantong10085.7k12d agoCLAUDE.md
headroomby headroomlabs-ai10073.9ktodayCLAUDE.md
ai-job-searchby MadsLorentzen10044.1ktodayCLAUDE.md
claude-howtoby luongnv8910041.7k1d agoCLAUDE.md

Frequently asked questions

How do I install atlas-cloud-media?
Run npx skills add sickn33/agentic-awesome-skills --skill atlas-cloud-media. The install tabs above show the steps for each supported agent.
Which AI agents does atlas-cloud-media work with?
It is written for Claude Code, Gemini CLI, Cursor and OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
Is atlas-cloud-media safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is atlas-cloud-media still maintained?
The repository was last updated 3 days ago, so atlas-cloud-media is actively maintained.

name: atlas-cloud-media description: "Generate Atlas Cloud images and videos through its asynchronous media API with schema-first model selection and credential-safe polling." category: media risk: critical source: self source_type: self date_added: "2026-08-12" author: binyangzhu000-sudo tags: [atlas-cloud, image-generation, video-generation, media-api] tools: [claude, codex, cursor, gemini]

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

| Operation | Method and endpoint | | --- | --- | | List models | GET https://api.atlascloud.ai/api/v1/models | | Generate image | POST https://api.atlascloud.ai/api/v1/model/generateImage | | Generate video | POST https://api.atlascloud.ai/api/v1/model/generateVideo | | Poll task | GET https://api.atlascloud.ai/api/v1/model/prediction/{id} |

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.

Related Skills

View on GitHub
GitHub Stars46.9k
CategoryDevelopment
Updated3d ago
Forks6.8k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions