
Alicloud Ai Entry Modelstudio Test
- 296 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
alicloud-ai-entry-modelstudio-test is an Alibaba Cloud agent skill that regression-tests Model Studio integrations with prompt fixtures, response assertions, latency checks, and failure scenarios for developers validatin
About
alicloud-ai-entry-modelstudio-test is an agent skill in cinience/alicloud-skills that regression-tests Alibaba Cloud Model Studio integrations before release or upstream model upgrades. The skill structures prompt fixture runs, response assertion checks, latency thresholds, and failure-scenario validation so engineering teams catch breaking changes in AI entry endpoints, token limits, streaming behavior, or authentication flows early. Developers reach for alicloud-ai-entry-modelstudio-test when DashScope or Model Studio SDK calls power chat features, embedding pipelines, or agent tools and need repeatable harnesses rather than manual curl probes before each deploy. The skill follows the alicloud-skills OpenAPI workflow pattern: confirm region and credentials, discover required parameters, execute test calls, and verify outcomes with describe or list APIs. Install via cinience/alicloud-skills through the skills CLI so Claude Code or Cursor sessions generate Model Studio test cases aligned with Alibaba Cloud SDK and OpenAPI Explorer conventions during pre-release QA.
- Prompt fixture and golden-output checks
- Streaming and timeout validation
- Auth and rate-limit edge cases
- CI-friendly smoke test patterns
- Model version regression coverage
Alicloud Ai Entry Modelstudio Test by the numbers
- 296 all-time installs (skills.sh)
- Ranked #708 of 2,153 Testing & QA 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-entry-modelstudio-testAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 296 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
How do you regression-test Model Studio integrations?
Regression-test Model Studio integrations: prompt fixtures, response assertions, latency checks, and failure scenarios before release or model upgrades.
Who is it for?
Backend and AI engineers on Alibaba Cloud who need structured Model Studio regression tests before deploying chat, embedding, or agent features.
Skip if: Teams not using Alibaba Cloud Model Studio or developers seeking general LLM prompt engineering without cloud API test harnesses.
When should I use this skill?
User asks to test Model Studio integrations, write prompt fixtures for DashScope, check AI latency before release, or validate model upgrade regressions on Alibaba Cloud.
What you get
Model Studio test fixtures, assertion results, latency reports, and failure-scenario logs documenting pre-release AI integration health.
- Prompt fixture test suites
- Assertion and latency reports
- Failure scenario logs
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-entry-modelstudio-test validate?
alicloud-ai-entry-modelstudio-test validates Model Studio integrations using prompt fixtures, response assertions, latency checks, and failure scenarios. The cinience skill targets regressions before release or upstream model upgrades on Alibaba Cloud.
When should teams run Model Studio regression tests?
Teams should run Model Studio regression tests before shipping AI entry features or upgrading models when DashScope SDK behavior, streaming, or authentication changes could break production chat or embedding endpoints.