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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-chatbot

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Listed on Skillselion
Installs279
repo stars396
Last updatedJuly 18, 2026
Repositorycinience/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

SKILL.mdMarkdownGitHub ↗

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.py

Optional 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.txt

Pass 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, optional ALICLOUD_REGION_ID.
  • If region is unclear, ask the user before running mutating operations.

References

  • Sources: references/sources.md

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.

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