본문으로 건너뛰기
cccskills
무료GitHub에 공개

atlas-cloud-media

Generate images and videos through Atlas Cloud by checking each model’s input requirements before submission. Guides the agent through sending a request, checking progress within a time limit, and downloading and validating the resulting media.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Generate an image from a text prompt with Atlas Cloud
  • Generate a short video from a scene description
  • Check model-specific inputs before submitting a request
  • Download and validate completed media files

포함된 파일(1)

  • SKILL.md11.0 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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:

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.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Assess a new or existing AI project by examining its code, goals, and constraints. Recommend skills confirmed to exist in the available registry, then outline tasks and implementation phases with dependencies, completion criteria, and checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Planning the first steps of a new AI project
  • Assessing technical issues in an existing project
  • Selecting relevant skills from an available registry
sickn33/agentic-awesome-skills4.8만2026년 10월 23일 업데이트

007

무료

Review code and infrastructure for security risks, map potential threats, and plan stronger defenses. The skill covers OWASP checks, attack and defense exercises, and incident response, with validation required for each environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Reviewing code and infrastructure security
  • Mapping threats with STRIDE or PASTA
  • Planning stronger security settings
sickn33/agentic-awesome-skills4.8만2026년 10월 23일 업데이트

Turn a repeatable task into an Andru.ia skill with clear inputs, outputs, examples, and limits. Draft SKILL.md, check the folder structure and required validations, and prepare a pull request describing the changes for review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating a skill for a repeatable task
  • Improving an existing skill’s examples and limits
  • Validating skill files and preparing a pull request
sickn33/agentic-awesome-skills4.8만2026년 10월 23일 업데이트

Prepare an industry briefing once a project's sector is defined. It covers sector context, potentially applicable regulations, familiar interface patterns, technology choices, and risks, while separating verified evidence from hypotheses and questions requiring further review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Researching industry requirements before product design
  • Checking regulations for a project's jurisdiction
  • Choosing interface patterns familiar to target users
sickn33/agentic-awesome-skills4.8만2026년 10월 23일 업데이트

Create presentation drafts from text, outlines, or documents through the 2slides API. Match a reference image's style, choose themes, add AI voice narration, and export slide images or audio for review and use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Create a presentation from text or an outline
  • Summarize a document into slides
  • Match slide styling to a reference image
sickn33/agentic-awesome-skills4.8만2026년 10월 23일 업데이트

Plan a 360-degree or peer feedback process and create empty response templates from confirmed requirements. Produces consistent CSV, SQL, JSON Schema, or Notion property mappings while keeping visibility choices and scoring configuration separate.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Plan an anonymous peer feedback process
  • Create a CSV template for feedback responses
  • Align feedback fields across SQL and JSON Schema
sickn33/agentic-awesome-skills4.8만2026년 10월 23일 업데이트

sickn33의 스킬 모두 보기

이 스킬의 문제 신고