
Alicloud Ai Image Qwen Image Test
- 290 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
alicloud-ai-image-qwen-image-test is a testing skill that validates Alibaba Cloud Qwen image generation and edit pipelines using fixed prompts, visual diff thresholds, latency budgets, and failure injection for developer
About
alicloud-ai-image-qwen-image-test is a Cinience Alibaba Cloud skill for pre-release validation of Qwen image and image-edit API pipelines. It guides developers through fixed prompt suites, visual difference thresholds against baselines, latency budget checks, and deliberate failure injection so regressions surface before production deploys. Teams building on Alibaba Cloud generative image APIs reach for this skill when they need repeatable visual QA rather than ad-hoc manual screenshot checks. The workflow targets API integrators and agent builders who must prove edit chains, generation quality, and error handling under realistic fault conditions. Pair it with broader Alicloud OpenAPI skills when backup or unrelated service READMEs appear in the repository index.
- Golden prompt suites for gen and edit
- Dimension and format assertions
- Latency and timeout budgets
- Error and moderation path tests
- CI artifacts for visual regression
Alicloud Ai Image Qwen Image Test by the numbers
- 290 all-time installs (skills.sh)
- Ranked #714 of 2,153 Testing & QA skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 290 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
How do you test Qwen image pipelines before release?
Validate Qwen image and edit pipelines with fixed prompts, visual diff thresholds, latency budgets, and failure injection before releases.
Who is it for?
Developers integrating Alibaba Cloud Qwen image or edit APIs who need automated visual and latency regression checks before shipping.
Skip if: Skip this skill when you are not using Qwen image APIs or when you only need one-off manual image generation without release QA gates.
When should I use this skill?
Trigger when validating Qwen image generation, testing image-edit pipelines, setting visual diff thresholds, or running failure-injection tests before an Alicloud AI image release.
What you get
Visual diff reports, latency measurements, failure-injection results, and pass-fail QA evidence for Qwen image APIs.
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
How it compares
Use this skill for release-gated Qwen image API QA; use general integration tests when you only need HTTP status checks without visual baselines.
FAQ
What does alicloud-ai-image-qwen-image-test validate?
alicloud-ai-image-qwen-image-test validates Alibaba Cloud Qwen image and edit pipelines using fixed prompts, visual diff thresholds, latency budgets, and failure injection. Developers use it to catch regressions before release.
When should teams run Qwen image pipeline tests?
Teams should run alicloud-ai-image-qwen-image-test before shipping features that call Qwen image or edit APIs. Fixed prompts and visual diffs provide repeatable evidence that generation quality and latency still meet budgets.