Coding

Alibaba Cloud AI Chatbot

List, configure, and troubleshoot Alibaba Cloud beebot resources through OpenAPI or official SDKs.

What it does

Manage Alibaba Cloud beebot resources through Chatbot OpenAPI RPC calls, official SDKs, or OpenAPI Explorer. Discover operations and schemas from metadata for product code `Chatbot` and API version `2022-04-08`, then list, configure, or diagnose resources and verify changes with describe/list APIs. Save response summaries and reproducibility evidence under `output/alicloud-ai-chatbot/`.

When to use it

  • Inventorying beebot resources
  • Updating chatbot configuration
  • Diagnosing chatbot resource status
  • Discovering Chatbot OpenAPI operations

The skill document

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  --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

Questions people ask

How does it determine which Chatbot API and parameters to use?
It uses OpenAPI metadata to list available APIs and retrieve schemas before calling business APIs. The default product code is `Chatbot`, with API version `2022-04-08`.
Which Alibaba Cloud credentials and region settings does it use?
It first checks `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, and optional `ALICLOUD_REGION_ID`, then falls back to `~/.alibabacloud/credentials`. If the region is unclear, it asks before mutating resources.
How are operations verified and recorded?
Changes are checked with describe/list APIs. Artifacts, command output, API response summaries, and evidence containing key parameters are stored in `output/alicloud-ai-chatbot/`.

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