Agent skill

Atlas Cloud Media

by sickn33 in sickn33/agentic-awesome-skills

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

MITAuto-check passedMedia & Creative

Install Atlas Cloud Media

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill atlas-cloud-media -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills atlas-cloud-media --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/atlas-cloud-media .claude/skills/atlas-cloud-media && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
atlas-cloud-media
GitHub stars
47k
Used in
1 other repo
Token cost
~2.8k tokens
SKILL.md length
1,019 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 5 steps: Create a Private Per-Run Workspace → Discover and Validate a Model → Submit One Generation Task → …
  • Tasks that involve Async programming
  • SKILL.md covers Overview, When to Use This Skill, Preconditions and API Contract, plus 7 more sections
  • Calls jq and curl; reaches api.atlascloud.ai; needs ATLASCLOUD_API_KEY

What it does

Atlas Cloud Media is an agent skill from sickn33/agentic-awesome-skills. Generate Atlas Cloud images and videos through its asynchronous media API with schema-first model selection and credential-safe polling.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Media & Creative, covering Async programming. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Async programming

Example prompts

  • “/atlas-cloud-media”

Requirements

  • A credential in ATLASCLOUD_API_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Create a Private Per-Run Workspace
  2. Discover and Validate a Model
  3. Submit One Generation Task
  4. Poll with a Deadline
  5. Download and Verify the Output

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • jq
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.atlascloud.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • ATLASCLOUD_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Atlas Cloud Media loads about 2.8k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 1,019 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 1,019 words, ~2,828 tokens.

Download SKILL.mdSave it as .claude/skills/atlas-cloud-media/SKILL.md (or your agent's skills folder).
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

OperationMethod and endpoint
List modelsGET https://api.atlascloud.ai/api/v1/models
Generate imagePOST https://api.atlascloud.ai/api/v1/model/generateImage
Generate videoPOST https://api.atlascloud.ai/api/v1/model/generateVideo
Poll taskGET https://api.atlascloud.ai/api/v1/model/prediction/{id}

Generation and polling requests use these headers:

text
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.

bash
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:

bash
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:

bash
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:

bash
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.

bash
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.

bash
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.
Show full SKILL.md (363 more words)Show less

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.
  • @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.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/atlas-cloud-media of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit b84d35a

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Atlas Cloud Media compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Atlas Cloud Media this skillsickn33/agentic-awesome-skills47k1 repos~2.8kAutomated safety check: PassMIT
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Image Generation Gatewayscalesthio/generative-media-skills197—~20kAutomated safety check: PassMIT
Stable Audiocalesthio/generative-media-skills197—~4.8kAutomated safety check: PassMIT
YugabyteDB ASH Instrumentationyugabyte/yugabyte-db11k—~4.5kAutomated safety check: PassCustom licence
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Questions about Atlas Cloud Media

What does Atlas Cloud Media do?

Generate Atlas Cloud images and videos through its asynchronous media API with schema-first model selection and credential-safe polling. Atlas Cloud Media is an agent skill from sickn33/agentic-awesome-skills. Generate Atlas Cloud images and videos through its asynchronous media API with schema-first model selection and credential-safe polling.

When should I use Atlas Cloud Media?

Atlas Cloud Media fits situations like: tasks that involve Async programming.

How do I install Atlas Cloud Media in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill atlas-cloud-media -a claude-code`. Or copy the skill folder (skills/atlas-cloud-media in sickn33/agentic-awesome-skills) into .claude/skills/atlas-cloud-media in your project. Claude Code loads it when a task matches its description.

How do I install Atlas Cloud Media in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill atlas-cloud-media -a codex`. Or copy the skill folder (skills/atlas-cloud-media in sickn33/agentic-awesome-skills) into .agents/skills/atlas-cloud-media in your project. Codex loads it when a task matches its description.

Can I use Atlas Cloud Media in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill atlas-cloud-media -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/atlas-cloud-media, .gemini/skills/atlas-cloud-media, .github/skills/atlas-cloud-media and .opencode/skills/atlas-cloud-media in your project.

What does Atlas Cloud Media need to run?

Going by SKILL.md and its folder, Atlas Cloud Media needs the command-line tools its instructions call (jq and curl) and credentials named ATLASCLOUD_API_KEY. Our summary lists: A credential in ATLASCLOUD_API_KEY.

Does Atlas Cloud Media access the network?

SKILL.md names 1 domain. In commands or code: api.atlascloud.ai; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Atlas Cloud Media safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Atlas Cloud Media use?

Atlas Cloud Media is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Atlas Cloud Media use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Atlas Cloud Media?

Skills that share tags, products or a category with Atlas Cloud Media: Dsh CI Test Reliability (Zhou-Yujing114514/deepseek-harness-linux, 120 stars), Image Generation Gateways (calesthio/generative-media-skills, 197 stars), Stable Audio (calesthio/generative-media-skills, 197 stars) and YugabyteDB ASH Instrumentation (yugabyte/yugabyte-db, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Atlas Cloud Media?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.