
Alicloud Data Lake Dlf Next
- 262 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
alicloud-data-lake-dlf-next is an agent skill that adopts next-generation Alibaba Cloud DLF APIs and workflows for lakehouse metadata, storage bindings, and catalog automation.
About
alicloud-data-lake-dlf-next is a cinience/alicloud-skills entry for Alibaba Cloud DLF next-generation data lake workflows. It helps developers build or migrate lakehouse metadata layers, storage bindings, and automated catalog operations using updated DLF APIs. Reach for it when standing up a new lakehouse on Alibaba Cloud or modernizing legacy DLF integrations rather than hand-rolling catalog glue code. Confirm API version differences and IAM permissions in Alibaba Cloud DLF documentation before production cutover.
- Next-gen DLF API surface
- Migration from legacy DLF patterns
- Updated catalog and table operations
- Region and account configuration
- Lakehouse automation for agents
Alicloud Data Lake Dlf Next by the numbers
- 262 all-time installs (skills.sh)
- Ranked #424 of 1,039 Cloud & Infrastructure skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 262 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
How do you migrate to DLF next APIs on Alibaba Cloud?
Adopt the next-generation DLF APIs and workflows when building or migrating lakehouse metadata, storage bindings, and catalog automation on Alibaba Cloud.
Who is it for?
Data platform engineers building or migrating Alibaba Cloud lakehouse metadata and catalog automation.
Skip if: Small app CRUD databases or teams not using Alibaba Cloud data lake services.
When should I use this skill?
User asks about DLF next APIs, lakehouse catalog automation, or DLF migration on Alibaba Cloud.
What you get
DLF catalog configurations, storage binding definitions, and migration-ready API workflow patterns.
- Catalog automation configuration
- Storage binding definitions
Files
Category: service
Data Lake Formation (Next)
Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Data Lake Formation.
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: ALICLOUD_ACCESS_KEY_ID / ALICLOUD_ACCESS_KEY_SECRET / ALICLOUD_REGION_ID Region policy: ALICLOUD_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:
DlfNext - Default API version:
2025-03-10 - 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/alicloud-data-lake-dlf-next/
Validation
mkdir -p output/alicloud-data-lake-dlf-next
for f in skills/data-lake/alicloud-data-lake-dlf-next/scripts/*.py; do
python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/alicloud-data-lake-dlf-next/validate.txtPass criteria: command exits 0 and output/alicloud-data-lake-dlf-next/validate.txt is generated.
Output And Evidence
- Save artifacts, command outputs, and API response summaries under
output/alicloud-data-lake-dlf-next/. - 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:
ALICLOUD_ACCESS_KEY_ID,ALICLOUD_ACCESS_KEY_SECRET, optionalALICLOUD_REGION_ID. - If region is unclear, ask the user before running mutating operations.
References
- Sources:
references/sources.md
interface:
display_name: "Alibaba Cloud Data Lake DLF Next"
short_description: "DlfNext lake governance workflows"
default_prompt: "Use $alicloud-data-lake-dlf-next to complete this data-lake task on Alibaba Cloud."
Sources
- OpenAPI product page:
https://api.aliyun.com/product/DlfNext - API list (metadata):
https://api.aliyun.com/meta/v1/products/DlfNext/versions/2025-03-10/api-docs.json - API definition (single API):
https://api.aliyun.com/meta/v1/products/DlfNext/versions/2025-03-10/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 = "DlfNext"
DEFAULT_VERSION = "2025-03-10"
OUTPUT_DIR = pathlib.Path("output/alicloud-data-lake-dlf-next")
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-data-lake-dlf-next cover?
alicloud-data-lake-dlf-next guides next-generation DLF APIs for lakehouse metadata, storage bindings, and catalog automation on Alibaba Cloud during new builds or migrations.
Who should use the DLF next skill?
Data platform engineers on Alibaba Cloud use alicloud-data-lake-dlf-next when implementing or migrating lakehouse catalog automation, not for simple application databases.