
Alicloud Ai Recommend Airec
- 265 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
alicloud-ai-recommend-airec is an Alibaba Cloud skills package skill that integrates AiRec recommendation APIs for personalized feeds, product ranking, and behavioral recall in apps, storefronts, and content surfaces.
About
alicloud-ai-recommend-airec is a backend integration skill from cinience/alicloud-skills for Alibaba Cloud AiRec personalization APIs. The skill guides wiring recommendation endpoints for personalized content feeds, product ranking, and behavioral recall across mobile apps, e-commerce storefronts, and media surfaces. Developers reach for alicloud-ai-recommend-airec when adding Alibaba-native recommendation to existing Alibaba Cloud deployments or migrating from manual ranking rules. It focuses on API integration patterns, request schemas, and recall configuration rather than training custom ML models from scratch.
- AiRec event and behavior ingestion
- Personalized ranking API integration
- Recall and filter policy configuration
- Ecommerce and feed recommendation hooks
- Real-time inference client patterns
Alicloud Ai Recommend Airec by the numbers
- 265 all-time installs (skills.sh)
- Ranked #2,452 of 16,546 AI & Agent Building 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-recommend-airecAdd your badge
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| Installs | 265 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
How do you integrate Alibaba AiRec recommendation APIs?
Integrate Alibaba AiRec recommendation APIs for personalized feeds, product ranking, and behavioral recall in apps, storefronts, and content surfaces.
Who is it for?
Backend engineers on Alibaba Cloud adding AiRec-powered personalization to apps, storefronts, or content feeds.
Skip if: Teams on AWS or GCP only, or projects building fully custom recommendation models without Alibaba AiRec.
When should I use this skill?
A developer integrates Alibaba AiRec APIs for personalized feeds, product ranking, or behavioral recall in production apps.
What you get
AiRec API integration with personalized feed endpoints, product ranking config, and behavioral recall wiring.
- api integration code
- ranking config
- feed endpoint wiring
Files
Category: service
AIRec
Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for AIRec.
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:
Airec - Default API version:
2020-11-26 - 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-ai-recommend-airec/
Validation
mkdir -p output/alicloud-ai-recommend-airec
for f in skills/ai/recommendation/alicloud-ai-recommend-airec/scripts/*.py; do
python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/alicloud-ai-recommend-airec/validate.txtPass criteria: command exits 0 and output/alicloud-ai-recommend-airec/validate.txt is generated.
Output And Evidence
- Save artifacts, command outputs, and API response summaries under
output/alicloud-ai-recommend-airec/. - 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 AI Recommend AIRec"
short_description: "AIRec recommendation configuration workflows"
default_prompt: "Use $alicloud-ai-recommend-airec to complete this ai/recommendation task on Alibaba Cloud."
Sources
- OpenAPI product page:
https://api.aliyun.com/product/Airec - API list (metadata):
https://api.aliyun.com/meta/v1/products/Airec/versions/2020-11-26/api-docs.json - API definition (single API):
https://api.aliyun.com/meta/v1/products/Airec/versions/2020-11-26/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 = "Airec"
DEFAULT_VERSION = "2020-11-26"
OUTPUT_DIR = pathlib.Path("output/alicloud-ai-recommend-airec")
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-recommend-airec integrate?
The alicloud-ai-recommend-airec skill integrates Alibaba Cloud AiRec recommendation APIs for personalized feeds, product ranking, and behavioral recall in apps, storefronts, and content surfaces on Alibaba Cloud.
When should developers use AiRec versus custom ML models?
Use alicloud-ai-recommend-airec when Alibaba Cloud AiRec managed recommendation fits the stack and speed-to-ship matters. Build custom models when recommendation logic must be fully proprietary outside Alibaba services.