aliyun-qwen-deep-research
Use when a task needs Alibaba Cloud Model Studio Qwen Deep Research models to plan multi-step investigation, run iterative web research, and produce structured reports with citations or evidence summaries.
What this skill does
Category: provider # Model Studio Qwen Deep Research ## Validation ```bash 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: ```bash 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 ```bash 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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