Aliyun Qwen Deep Research

Name: Aliyun Qwen Deep Research
Author: cinience

cinience/alicloud-skills

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

Run multi-step web research with Alibaba Cloud Model Studio Qwen Deep Research models to produce structured reports with citations.

About

Uses Model Studio Qwen Deep Research models to plan multi-step investigations, run iterative web research, and produce cited structured reports. A developer uses it for deep research workflows beyond a single chat completion.

qwen-deep-research and snapshot with MCP tool-calling support
Saves goals, request payloads, and final report snapshots

Aliyun Qwen Deep Research 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-deep-research

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

[![Listed on Skillselion](https://skillselion.com/badge/skills/cinience/alicloud-skills/aliyun-qwen-deep-research.svg)](https://skillselion.com/skills/cinience/alicloud-skills/aliyun-qwen-deep-research)

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

What it does

Run multi-step web research with Alibaba Cloud Model Studio Qwen Deep Research models to produce structured reports with citations.

Files

SKILL.mdMarkdownGitHub ↗

Category: provider

Model Studio Qwen Deep Research

Validation

mkdir -p output/aliyun-qwen-deep-research
python -m py_compile skills/ai/research/aliyun-qwen-deep-research/scripts/prepare_deep_research_request.py && echo "py_compile_ok" > output/aliyun-qwen-deep-research/validate.txt

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

Output And Evidence

Save research goals, confirmation answers, normalized request payloads, and final report snapshots under output/aliyun-qwen-deep-research/.
Keep the exact model, region, and enable_feedback setting with each saved run.

Use this skill when the user wants a deep, multi-stage research workflow rather than a single chat completion.

Critical model names

Use one of these exact model strings:

qwen-deep-research
qwen-deep-research-2025-12-15

Selection guidance:

Use qwen-deep-research for the current mainline model.
Use qwen-deep-research-2025-12-15 when you need the snapshot with MCP tool-calling support and stronger reproducibility.

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.
This model currently applies to the China mainland (Beijing) region and uses its own API shape rather than OpenAI-compatible mode.

Normalized interface (research.run)

Request

topic (string, required)
model (string, optional): default qwen-deep-research
messages (array<object>, optional)
enable_feedback (bool, optional): default true
stream (bool, optional): must be true
attachments (array<object>, optional): image URLs and related context

Response

status (string): stage status such as thinking, researching, or finished
text (string, optional): streamed content chunk
report (string, optional): final structured research report
raw (object, optional)

Quick start

python skills/ai/research/aliyun-qwen-deep-research/scripts/prepare_deep_research_request.py \
  --topic "Compare cloud video generation model trade-offs for marketing automation." \
  --disable-feedback

Operational guidance

Expect streaming output only.
Keep the initial topic concrete and bounded; broad topics can trigger long iterative search plans.
If the model asks follow-up questions and you already know the constraints, answer them explicitly to avoid wasted rounds.
Use the snapshot model when you need stable evaluation runs or MCP tool-calling support.

Output location

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

References

references/sources.md

interface:
  display_name: "Alibaba Cloud AI Research Qwen Deep Research"
  short_description: "Structured multi-step research with Qwen Deep Research"
  default_prompt: "Use $aliyun-qwen-deep-research to complete this ai/research task on Alibaba Cloud."

#!/usr/bin/env python3
"""Prepare a normalized request for Qwen Deep Research."""

from __future__ import annotations

import argparse
import json
from pathlib import Path


def main() -> None:
    parser = argparse.ArgumentParser(description="Prepare research.run request")
    parser.add_argument("--topic", required=True)
    parser.add_argument("--model", default="qwen-deep-research")
    parser.add_argument("--disable-feedback", action="store_true")
    parser.add_argument("--image", action="append", default=[])
    parser.add_argument("--output", default="output/aliyun-qwen-deep-research/requests/request.json")
    args = parser.parse_args()

    content: list[dict[str, str]] = [{"text": args.topic}]
    for image in args.image:
        content.insert(0, {"image": image})

    payload = {
        "model": args.model,
        "messages": [{"role": "user", "content": content}],
        "parameters": {
            "enable_feedback": not args.disable_feedback,
        },
        "stream": True,
    }

    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()