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aliyun-qwen-deep-research

aliyun-qwen-deep-research

github.com/cinienceProductivity39661 installsUpdated 11w ago
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About aliyun-qwen-deep-research

A skill for multi-stage deep research, intended for users who need a structured report rather than a single chat completion. It supports streamed output and attachments, and offers a model snapshot with MCP tool-calling support.

Key features

  1. 01Run multi-stage research on a topic
  2. 02Stream progress and report output
  3. 03Attach image URLs and context
  4. 04Select a snapshot with MCP tool-calling support

Use cases

  1. 01Produce a structured report on a bounded topic
  2. 02Use a model snapshot for reproducible evaluation
  3. 03Add images and related context to research

MCP support

The `qwen-deep-research-2025-12-15` model snapshot supports MCP tool calling.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/cinience/alicloud-skills --skill aliyun-qwen-deep-research

After installing, type/aliyun-qwen-deep-researchin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

agents/
openai.yaml
references/
sources.md
scripts/
prepare_deep_research_request.py

Details

From the project README

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, optional)
  • enable_feedback (bool, optional): default true
  • stream (bool, optional): must be true
  • attachments (array, 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

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