Skill: cinience/aliyun-qwen-generation (version 1.0.0)

--- name: aliyun-qwen-generation description: Use when generating or reasoning over text with Alibaba Cloud Model Studio Qwen flagship text models (`qwen3-max`, `qwen3.5-plus`, `qwen3.5-flash`, snapshots, and compatible open-source variants). Use when building chat, agent, tool-calling, or long-context text generation workflows on Model Studio. version: 1.0.0 --- Category: provider # Model Studio Qwen Text Generation ## Validation ```bash 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: ```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`. ## 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) ```bash 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 ```bash 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`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)