
Alicloud Data Lake Dlf Next Test
- 297 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
alicloud-data-lake-dlf-next-test is an Alibaba Cloud skill that tests DLF Next data lake operations to verify catalog access, ingestion paths, and pipeline behavior before production analytics or ETL workloads depend on
About
alicloud-data-lake-dlf-next-test is a Claude Code skill for data teams adopting Alibaba Cloud DLF Next who must prove catalog, ingestion, and pipeline paths work before analytics jobs go live. The skill runs verification steps across data lake operations so broken permissions, ingestion routes, or pipeline configs surface during pre-release checks. Developers reach for alicloud-data-lake-dlf-next-test when onboarding a new lakehouse, changing catalog policies, or hardening ETL dependencies on DLF Next. It suits data engineers and platform teams running Spark, SQL, or batch pipelines that read from governed catalogs. Use it before promoting analytics workloads, after infrastructure changes, or when staging environments need parity with expected production lake behavior.
- DLF Next connectivity smoke tests
- Catalog and metadata validation
- Ingestion path verification
- Permission and auth checks
- Pipeline regression scenarios
Alicloud Data Lake Dlf Next Test by the numbers
- 297 all-time installs (skills.sh)
- Ranked #179 of 911 Databases skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 297 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
How do you test Alibaba Cloud DLF Next pipelines?
Test Alibaba Cloud DLF Next data lake operations to verify catalog access, ingestion paths, and pipeline behavior before production analytics or ETL workloads depend on them.
Who is it for?
Data engineers validating Alibaba Cloud DLF Next lakehouse catalog, ingestion, and pipeline setup before analytics or ETL production workloads.
Skip if: Teams using only local Parquet files or non-Alibaba data lakes who do not need DLF Next catalog and ingestion verification.
When should I use this skill?
A developer must verify DLF Next catalog permissions, ingestion routes, or pipeline behavior before analytics or ETL depends on the data lake.
What you get
DLF Next test verification logs, catalog access checks, ingestion path results, and pipeline behavior reports.
- catalog access verification
- ingestion test results
- pipeline behavior report
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
Choose alicloud-data-lake-dlf-next-test over generic data pipeline skills when validation must target Alibaba Cloud DLF Next catalog, ingestion, and lake operations.
FAQ
What does alicloud-data-lake-dlf-next-test verify?
alicloud-data-lake-dlf-next-test verifies Alibaba Cloud DLF Next catalog access, ingestion paths, and pipeline behavior so analytics and ETL workloads do not depend on untested data lake configuration.
When should DLF Next tests run?
alicloud-data-lake-dlf-next-test should run before production analytics or ETL cutover, after catalog or ingestion changes, and when staging environments need confirmed DLF Next parity.