Aliyun Qwen Generation

Name: Aliyun Qwen Generation
Author: cinience

cinience/alicloud-skills

55 installs
396 repo stars
Updated July 18, 2026
cinience/alicloud-skills

Generate and reason over text with Alibaba Cloud Model Studio Qwen flagship models for chat, tool-calling, and long-context workflows.

About

Uses Model Studio Qwen flagship text models (qwen3-max and related) for text generation, reasoning, tool-calling, and long-context chat. A developer uses it to build chat and agent text workflows on Qwen.

qwen3-max flagship and compatible open-source variants
Saves reproducible request examples with model, region, and params

Aliyun Qwen Generation by the numbers

55 all-time installs (skills.sh)
Ranked #6,846 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 aliyun-qwen-generation

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Installs	55
repo stars	★ 396
Last updated	July 18, 2026
Repository	cinience/alicloud-skills ↗

What it does

Generate and reason over text with Alibaba Cloud Model Studio Qwen flagship models for chat, tool-calling, and long-context workflows.

Files

SKILL.mdMarkdownGitHub ↗

Category: provider

Model Studio Qwen Text Generation

Validation

mkdir -p output/aliyun-qwen-generation
python -m py_compile skills/ai/text/aliyun-qwen-generation/scripts/prepare_generation_request.py && echo "py_compile_ok" > output/aliyun-qwen-generation/validate.txt

Pass criteria: command exits 0 and output/aliyun-qwen-generation/validate.txt is generated.

Output And Evidence

Save prompt templates, normalized request payloads, and response summaries under output/aliyun-qwen-generation/.
Keep one reproducible request example with model name, region, and key parameters.

Use this skill for general text generation, reasoning, tool-calling, and long-context chat on Alibaba Cloud Model Studio.

Critical model names

Prefer the current flagship families:

qwen3-max
qwen3-max-2026-01-23
qwen3.5-plus
qwen3.5-plus-2026-02-15
qwen3.5-flash
qwen3.5-flash-2026-02-23

Common related variants listed in the official model catalog:

qwen3.5-397b-a17b
qwen3.5-122b-a10b
qwen3.5-35b-a3b
qwen3.5-27b

Prerequisites

Install SDK in a virtual environment:

python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashscope

Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.

Normalized interface (text.generate)

Request

messages (array<object>, required): standard chat turns.
model (string, optional): default qwen3.5-plus.
temperature (number, optional)
top_p (number, optional)
max_tokens (int, optional)
enable_thinking (bool, optional)
tools (array<object>, optional)
response_format (object, optional)
stream (bool, optional)

Response

text (string): assistant output.
finish_reason (string, optional)
usage (object, optional)
raw (object, optional)

Quick start (OpenAI-compatible endpoint)

curl -sS https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
  -H "Authorization: Bearer $DASHSCOPE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-plus",
    "messages": [
      {"role": "system", "content": "You are a concise assistant."},
      {"role": "user", "content": "Summarize why object storage helps media pipelines."}
    ],
    "stream": false
  }'

Local helper script

python skills/ai/text/aliyun-qwen-generation/scripts/prepare_generation_request.py \
  --prompt "Draft a concise architecture summary for a media ingestion pipeline." \
  --model qwen3.5-plus

Operational guidance

Use snapshot IDs when reproducibility matters.
Prefer qwen3.5-flash for lower-latency simple tasks and qwen3-max for harder multi-step tasks.
Keep tool schemas minimal and explicit when enabling tool calls.
For multimodal input, route to dedicated VL or Omni skills unless the task is primarily text-centric.

Output location

Default output: output/aliyun-qwen-generation/requests/
Override base dir with OUTPUT_DIR.

References

references/sources.md

interface:
  display_name: "Alibaba Cloud AI Text Qwen Generation"
  short_description: "General text generation and reasoning with Qwen flagship models"
  default_prompt: "Use $aliyun-qwen-generation to complete this ai/text generation task on Alibaba Cloud."

#!/usr/bin/env python3
"""Prepare a normalized request for Model Studio text generation."""

from __future__ import annotations

import argparse
import json
from pathlib import Path


def main() -> None:
    parser = argparse.ArgumentParser(description="Prepare text.generate request")
    parser.add_argument("--prompt", required=True)
    parser.add_argument("--model", default="qwen3.5-plus")
    parser.add_argument("--system", default="You are a concise Alibaba Cloud Model Studio assistant.")
    parser.add_argument("--temperature", type=float)
    parser.add_argument("--top-p", dest="top_p", type=float)
    parser.add_argument("--max-tokens", dest="max_tokens", type=int)
    parser.add_argument("--stream", action="store_true")
    parser.add_argument("--output", default="output/aliyun-qwen-generation/requests/request.json")
    args = parser.parse_args()

    payload = {
        "model": args.model,
        "messages": [
            {"role": "system", "content": args.system},
            {"role": "user", "content": args.prompt},
        ],
        "stream": args.stream,
    }
    if args.temperature is not None:
        payload["temperature"] = args.temperature
    if args.top_p is not None:
        payload["top_p"] = args.top_p
    if args.max_tokens is not None:
        payload["max_tokens"] = args.max_tokens

    output = Path(args.output)
    output.parent.mkdir(parents=True, exist_ok=True)
    output.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps({"ok": True, "request_path": str(output)}, ensure_ascii=False))


if __name__ == "__main__":
    main()