Alicloud Data Analytics Dataanalysisgbi
- 284 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
alicloud-data-analytics-dataanalysisgbi is a Claude agent skill that integrates Alibaba Cloud DataAnalysis GBI into dashboards, agents, or backends for conversational generative business intelligence over cloud datasets.
About
alicloud-data-analytics-dataanalysisgbi belongs to cinience/alicloud-skills and documents how to connect applications to Alibaba Cloud DataAnalysis GBI for natural-language or generative queries against managed datasets. The skill covers authentication, query patterns, embedding GBI responses in dashboards, and exposing analytics tools to LLM agents that need governed business metrics instead of ad-hoc SQL. Data and full-stack developers reach for it when shipping China-region analytics features on Alibaba Cloud rather than wiring Looker, Metabase, or BigQuery conversational layers. It complements other alicloud data skills for end-to-end cloud analytics integration inside agent-assisted development workflows.
- DataAnalysis GBI API wiring
- Query and session configuration
- Insight response parsing
- Dataset connection patterns
- Agent analytics tool hooks
Alicloud Data Analytics Dataanalysisgbi by the numbers
- 284 all-time installs (skills.sh)
- Ranked #598 of 2,064 Data Science & ML 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-data-analytics-dataanalysisgbiAdd your badge
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| Installs | 284 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
How do you integrate Alibaba Cloud GBI analytics APIs?
Integrate Alibaba Cloud DataAnalysis GBI analytics into dashboards, agents, or backends that need conversational or generative business intelligence over cloud datasets.
Who is it for?
Full-stack and data developers embedding conversational BI from Alibaba Cloud GBI into internal dashboards or LLM agent tools.
Skip if: Teams standardized on BigQuery, Snowflake Cortex, or Metabase without Alibaba Cloud data estates.
When should I use this skill?
A developer asks to integrate DataAnalysis GBI, add conversational BI to a dashboard, or query Alibaba Cloud datasets via generative analytics APIs.
What you get
GBI API integration code, authenticated query flows, and dashboard or agent hooks returning governed analytics answers.
- GBI API client integration
- Dashboard analytics hooks
Files
Category: service
DataAnalysisGBI
Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for DataAnalysisGBI.
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:
DataAnalysisGBI - Default API version:
2024-08-23 - 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-analytics-dataanalysisgbi/
Validation
mkdir -p output/alicloud-data-analytics-dataanalysisgbi
for f in skills/data-analytics/alicloud-data-analytics-dataanalysisgbi/scripts/*.py; do
python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/alicloud-data-analytics-dataanalysisgbi/validate.txtPass criteria: command exits 0 and output/alicloud-data-analytics-dataanalysisgbi/validate.txt is generated.
Output And Evidence
- Save artifacts, command outputs, and API response summaries under
output/alicloud-data-analytics-dataanalysisgbi/. - 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 Analytics Dataanalysisgbi"
short_description: "Data analytics and BI workflows"
default_prompt: "Use $alicloud-data-analytics-dataanalysisgbi to complete this data-analytics task on Alibaba Cloud."
Sources
- OpenAPI product page:
https://api.aliyun.com/product/DataAnalysisGBI - API list (metadata):
https://api.aliyun.com/meta/v1/products/DataAnalysisGBI/versions/2024-08-23/api-docs.json - API definition (single API):
https://api.aliyun.com/meta/v1/products/DataAnalysisGBI/versions/2024-08-23/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 = "DataAnalysisGBI"
DEFAULT_VERSION = "2024-08-23"
OUTPUT_DIR = pathlib.Path("output/alicloud-data-analytics-dataanalysisgbi")
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()
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Use this skill when analytics must run on Alibaba Cloud GBI instead of Western BI or warehouse-native copilots.
FAQ
What is alicloud-data-analytics-dataanalysisgbi for?
alicloud-data-analytics-dataanalysisgbi helps developers call Alibaba Cloud DataAnalysis GBI so applications and agents can ask natural-language questions and receive governed analytics over cloud-hosted datasets.
Where can GBI results surface?
alicloud-data-analytics-dataanalysisgbi supports embedding GBI responses in web dashboards, internal tools, and LLM agent backends that need conversational business intelligence.