
Alicloud Ai Image Qwen Image Edit Test
- 295 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
alicloud-ai-image-qwen-image-edit-test is a Claude Code smoke-test skill that verifies Alibaba Cloud Qwen image-edit requests, auth, and edited outputs for developers who need minimal viable validation before production
About
alicloud-ai-image-qwen-image-edit-test is a minimal viable test skill in cinience/alicloud-skills that validates the alicloud-ai-image-qwen-image-edit integration path before production use. The test opens the target skill SKILL.md, runs one minimal request through prepare_edit_request.py with DashScope SDK, and records request summary, response summary, and pass or fail reasons under output/ai-image-qwen-image-edit/. Pass criteria require the script to return ok true and generate request.json evidence. The parent skill supports Qwen Image Edit models including qwen-image-edit, qwen-image-edit-plus, qwen-image-edit-max, and qwen-image-2.0 series snapshots with prompt, image, optional mask, and size parameters. Developers reach for this smoke test after configuring DASHSCOPE_API_KEY, before wiring image-edit into agent workflows, or when debugging auth and region failures on Alibaba Cloud Model Studio.
- Qwen image-edit API smoke tests
- Request payload and auth validation
- Edited output quality checks
- Agent-safe error handling patterns
- Regression-friendly test flows
Alicloud Ai Image Qwen Image Edit Test by the numbers
- 295 all-time installs (skills.sh)
- Ranked #522 of 1,335 Generative Media skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/cinience/alicloud-skills --skill alicloud-ai-image-qwen-image-edit-testAdd your badge
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| Installs | 295 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
How do you smoke-test Qwen image-edit API auth?
Verify Alibaba Cloud Qwen image-edit requests, auth, and edited outputs before agents or apps rely on generative image manipulation in production.
Who is it for?
Developers integrating Alibaba Cloud Qwen Image Edit via DashScope who need a one-request smoke test before building agent or app image-manipulation features.
Skip if: Teams not using Alibaba Cloud Model Studio, or projects needing full image-edit feature coverage rather than a single minimal request validation.
When should I use this skill?
A developer configures DASHSCOPE_API_KEY, adds Qwen image-edit to an agent, or needs to verify one minimal edit request succeeds before production wiring.
What you get
Pass/fail test record, request.json payload, edited image output path, and DashScope API response evidence under output/ai-image-qwen-image-edit/.
- request.json evidence file
- Pass/fail test report
- Sample edited image output path
By the numbers
- Parent skill documents 8 critical Qwen Image Edit model name strings including qwen-image-edit-plus and qwen-image-2.0-p
- Pass criteria require prepare_edit_request.py to return ok true and write output/ai-image-qwen-image-edit/request.json
Files
Category: service
Cloud Backup
Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Cloud Backup.
Workflow
1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs.
AccessKey priority (must follow)
1) Environment variables: ALIBABACLOUD_ACCESS_KEY_ID / ALIBABACLOUD_ACCESS_KEY_SECRET / ALIBABACLOUD_REGION_ID Region policy: ALIBABACLOUD_REGION_ID is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: ~/.alibabacloud/credentials
API discovery
- Product code:
hbr - Default API version:
2017-09-08 - Use OpenAPI metadata endpoints to list APIs and get schemas (see references).
High-frequency operation patterns
1) Inventory/list: prefer List* / Describe* APIs to get current resources. 2) Change/configure: prefer Create* / Update* / Modify* / Set* APIs for mutations. 3) Status/troubleshoot: prefer Get* / Query* / Describe*Status APIs for diagnosis.
Minimal executable quickstart
Use metadata-first discovery before calling business APIs:
python scripts/list_openapi_meta_apis.pyOptional overrides:
python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version>The script writes API inventory artifacts under the skill output directory.
Output policy
If you need to save responses or generated artifacts, write them under: output/aliyun-hbr-backup/
Validation
mkdir -p output/aliyun-hbr-backup
for f in skills/backup/aliyun-hbr-backup/scripts/*.py; do
python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/aliyun-hbr-backup/validate.txtPass criteria: command exits 0 and output/aliyun-hbr-backup/validate.txt is generated.
Output And Evidence
- Save artifacts, command outputs, and API response summaries under
output/aliyun-hbr-backup/. - Include key parameters (region/resource id/time range) in evidence files for reproducibility.
Prerequisites
- Configure least-privilege Alibaba Cloud credentials before execution.
- Prefer environment variables:
ALIBABACLOUD_ACCESS_KEY_ID,ALIBABACLOUD_ACCESS_KEY_SECRET, optionalALIBABACLOUD_REGION_ID. - If region is unclear, ask the user before running mutating operations.
References
- Sources:
references/sources.md
interface:
display_name: "Alibaba Cloud Backup HBR"
short_description: "Cloud Backup vault and job workflows"
default_prompt: "Use $aliyun-hbr-backup to complete this backup task on Alibaba Cloud."
Sources
- OpenAPI product page:
https://api.aliyun.com/product/hbr - API list (metadata):
https://api.aliyun.com/meta/v1/products/hbr/versions/2017-09-08/api-docs.json - API definition (single API):
https://api.aliyun.com/meta/v1/products/hbr/versions/2017-09-08/apis/{ApiName}/api.json
#!/usr/bin/env python3
"""Fetch OpenAPI metadata API list for one product/version and save to output/.
Env:
- OPENAPI_META_TIMEOUT (seconds, default: 20)
"""
from __future__ import annotations
import argparse
import json
import os
import pathlib
import urllib.request
DEFAULT_PRODUCT_CODE = "hbr"
DEFAULT_VERSION = "2017-09-08"
OUTPUT_DIR = pathlib.Path("output/aliyun-hbr-backup")
def fetch_json(url: str, timeout: int) -> dict:
req = urllib.request.Request(url, headers={"User-Agent": "codex-skill"})
with urllib.request.urlopen(req, timeout=timeout) as resp:
return json.loads(resp.read().decode("utf-8"))
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--product-code", default=DEFAULT_PRODUCT_CODE)
parser.add_argument("--version", default=DEFAULT_VERSION)
parser.add_argument("--output-dir", default=str(OUTPUT_DIR))
args = parser.parse_args()
timeout = int(os.getenv("OPENAPI_META_TIMEOUT", "20"))
output_dir = pathlib.Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
url = (
f"https://api.aliyun.com/meta/v1/products/{args.product_code}"
f"/versions/{args.version}/api-docs.json"
)
payload = fetch_json(url, timeout)
raw_apis = payload.get("apis", {})
if isinstance(raw_apis, dict):
api_names = sorted(raw_apis.keys())
elif isinstance(raw_apis, list):
names = []
for item in raw_apis:
if isinstance(item, dict):
name = item.get("name") or item.get("apiName")
if name:
names.append(name)
elif isinstance(item, str):
names.append(item)
api_names = sorted(set(names))
else:
api_names = []
json_file = output_dir / f"{args.product_code}_{args.version}_api_docs.json"
md_file = output_dir / f"{args.product_code}_{args.version}_api_list.md"
json_file.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
md_lines = [
f"# {args.product_code} {args.version} API List",
"",
f"- Source: {url}",
f"- API count: {len(api_names)}",
"",
]
md_lines.extend([f"- `{name}`" for name in api_names])
md_file.write_text("\n".join(md_lines) + "\n", encoding="utf-8")
print(f"Saved: {json_file}")
print(f"Saved: {md_file}")
if __name__ == "__main__":
main()
Related skills
FAQ
What does alicloud-ai-image-qwen-image-edit-test verify?
alicloud-ai-image-qwen-image-edit-test verifies one minimal Qwen image-edit request through prepare_edit_request.py with DashScope auth. Pass requires ok true response and request.json saved under output/ai-image-qwen-image-edit/.
What credentials does the Qwen image-edit test need?
The test needs DASHSCOPE_API_KEY in the environment or dashscope_api_key in ~/.alibabacloud/credentials. Install DashScope SDK in a Python venv before running the minimal edit request script.