
Alicloud Ai Chatbot
- 279 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
alicloud-ai-chatbot is a Claude Code skill that helps developers integrate Alibaba Cloud Model Studio or Bailian APIs into agent workflows, support bots, and in-app conversational assistants.
About
alicloud-ai-chatbot is an integration skill for wiring Alibaba Cloud Model Studio and Bailian APIs into software products. It guides authentication, endpoint configuration, prompt and session handling, and embedding conversational flows inside agent workflows, customer support bots, and in-app assistants. Developers use it when building on Alibaba Cloud and need a structured path from API credentials to working chat experiences rather than reading scattered console docs. The skill emphasizes production-oriented patterns: session continuity, error handling around model calls, and fitting LLM responses into existing app architecture on the Alibaba stack.
- Model Studio and Bailian chat APIs
- Session, memory, and tool-calling patterns
- Streaming responses and error handling
- Deployable support and product copilots
- AliCloud credential and region setup
Alicloud Ai Chatbot by the numbers
- 279 all-time installs (skills.sh)
- Ranked #2,420 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-chatbotAdd your badge
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| Installs | 279 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
How do you integrate Alibaba Cloud chatbot APIs?
Wire Alibaba Cloud Model Studio or Bailian APIs into agent workflows, customer support bots, and in-app conversational assistants.
Who is it for?
Developers building on Alibaba Cloud who need Bailian or Model Studio wired into agents, support bots, or in-app chat without manual API trial-and-error.
Skip if: Projects on OpenAI, Anthropic, or AWS Bedrock where Alibaba Cloud APIs are not part of the stack.
When should I use this skill?
A developer asks to connect Bailian, Model Studio, or Alibaba Cloud LLM APIs to a chatbot, agent, or in-app assistant.
What you get
Configured Bailian or Model Studio client, session flow, and embedded conversational assistant or support bot hooks.
- API client configuration
- Chat or agent session flow
- Embedded assistant integration scaffold
Files
Category: service
Chatbot (beebot)
Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for beebot.
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:
Chatbot - Default API version:
2022-04-08 - 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/alicloud-ai-chatbot/
Validation
mkdir -p output/alicloud-ai-chatbot
for f in skills/ai/service/alicloud-ai-chatbot/scripts/*.py; do
python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/alicloud-ai-chatbot/validate.txtPass criteria: command exits 0 and output/alicloud-ai-chatbot/validate.txt is generated.
Output And Evidence
- Save artifacts, command outputs, and API response summaries under
output/alicloud-ai-chatbot/. - 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 Chatbot"
short_description: "Chatbot intent and dialogue workflows"
default_prompt: "Use $alicloud-ai-chatbot to complete this ai/service task on Alibaba Cloud."
Sources
- OpenAPI product page:
https://api.aliyun.com/product/Chatbot - API list (metadata):
https://api.aliyun.com/meta/v1/products/Chatbot/versions/2022-04-08/api-docs.json - API definition (single API):
https://api.aliyun.com/meta/v1/products/Chatbot/versions/2022-04-08/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 = "Chatbot"
DEFAULT_VERSION = "2022-04-08"
OUTPUT_DIR = pathlib.Path("output/alicloud-ai-chatbot")
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
Use alicloud-ai-chatbot when the stack is Alibaba Cloud and conversational features must call Model Studio or Bailian rather than Western LLM APIs.
FAQ
Which Alibaba Cloud services does alicloud-ai-chatbot cover?
alicloud-ai-chatbot covers Alibaba Cloud Model Studio and Bailian APIs for conversational AI. The skill guides wiring those services into agent workflows, support bots, and in-app assistants with session and authentication patterns.
Can alicloud-ai-chatbot replace reading official Alibaba docs?
alicloud-ai-chatbot accelerates integration by structuring auth, endpoints, and chat flows for developers already committed to Alibaba Cloud. Teams on other cloud LLM providers should use skills aligned with their chosen platform instead.