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/aliyun-qwen-vl

Use when understanding images with Alibaba Cloud Model Studio Qwen VL models (qwen3-vl-plus/qwen3-vl-flash and latest aliases). Use when building image Q&A, visual analysis, OCR-like extraction, chart/table reading, or screenshot understanding workflows.

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$ npx -y skills add cinience/alicloud-skills --skill aliyun-qwen-vl --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/aliyun-qwen-vl

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when understanding images with Alibaba Cloud Model Studio Qwen VL models (qwen3-vl-plus/qwen3-vl-flash and latest aliases). Use when building image Q&A, visual analysis, OCR-like extraction, chart/table reading, or screenshot understanding workflows.

SKILL.md

aliyun-qwen-vl.SKILL.md
name: aliyun-qwen-vl
description: Use when understanding images with Alibaba Cloud Model Studio Qwen VL models (qwen3-vl-plus/qwen3-vl-flash and latest aliases). Use when building image Q&A, visual analysis, OCR-like extraction, chart/table reading, or screenshot understanding workflows.
version: 1.0.0

Category: provider

Model Studio Qwen VL (Image Understanding)

Validation

mkdir -p output/aliyun-qwen-vl
python -m py_compile skills/ai/multimodal/aliyun-qwen-vl/scripts/analyze_image.py && echo "py_compile_ok" > output/aliyun-qwen-vl/validate.txt

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

Output And Evidence

  • Save raw model responses and normalized extraction results to `output/aliyun-qwen-vl/`.
  • Include input image reference and prompt for traceability.

Use Qwen VL models for image input + text output understanding tasks via DashScope compatible-mode API.

Prerequisites

  • Install dependencies (recommended in a venv):
python3 -m venv .venv
. .venv/bin/activate
python -m pip install requests
  • Set `DASHSCOPE_API_KEY` in environment, or add `dashscope_api_key` to `~/.alibabacloud/credentials`.

Critical model names

Prefer the Qwen3 VL family:

  • `qwen3-vl-plus`
  • `qwen3-vl-flash`

When you need explicit "latest" routing or reproducible snapshots, use supported aliases/snapshots from the official model list, such as:

  • `qwen3-vl-plus-latest`
  • `qwen3-vl-plus-2025-12-19`
  • `qwen3-vl-flash-2026-01-22`
  • `qwen3-vl-flash-latest`

Legacy names still seen in some workloads:

  • `qwen-vl-max-latest`
  • `qwen-vl-plus-latest`

For OCR-specialized extraction, prefer `skills/ai/multimodal/aliyun-qwen-ocr/` instead of using the general VL skill.

Normalized interface (multimodal.chat)

Request

  • `prompt` (string, required): user question/instruction about image.
  • `image` (string, required): HTTPS URL, local path, or `data:` URL.
  • `model` (string, optional): default `qwen3-vl-plus`.
  • `max_tokens` (int, optional): default `512`.
  • `temperature` (float, optional): default `0.2`.
  • `detail` (string, optional): `auto`/`low`/`high`, default `auto`.
  • `json_mode` (bool, optional): return JSON-only response when possible.
  • `schema` (object, optional): JSON Schema for structured extraction.
  • `max_retries` (int, optional): retry count for `429/5xx`, default `2`.
  • `retry_backoff_s` (float, optional): exponential backoff base seconds, default `1.5`.

Response

  • `text` (string): primary model answer.
  • `model` (string): model actually used.
  • `usage` (object): token usage if returned by backend.

Quickstart

python skills/ai/multimodal/aliyun-qwen-vl/scripts/analyze_image.py \
  --request '{"prompt":"Summarize the main content in this image","image":"https://example.com/demo.jpg"}' \
  --print-response

Using local image:

python skills/ai/multimodal/aliyun-qwen-vl/scripts/analyze_image.py \
  --request '{"prompt":"Extract key information from the image","image":"./samples/invoice.png","model":"qwen3-vl-plus"}' \
  --print-response

Structured extraction (JSON mode):

python skills/ai/multimodal/aliyun-qwen-vl/scripts/analyze_image.py \
  --request '{"prompt":"Extract fields: title, amount, date","image":"./samples/invoice.png"}' \
  --json-mode \
  --print-response

Structured extraction (JSON Schema):

python skills/ai/multimodal/aliyun-qwen-vl/scripts/analyze_image.py \
  --request '{"prompt":"Extract invoice fields","image":"./samples/invoice.png"}' \
  --schema skills/ai/multimodal/aliyun-qwen-vl/references/examples/invoice.schema.json \
  --print-response

cURL (compatible mode)

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-vl-plus",
    "messages":[
      {
        "role":"user",
        "content":[
          {"type":"image_url","image_url":{"url":"https://example.com/demo.jpg"}},
          {"type":"text","text":"Describe this image and list executable actions"}
        ]
      }
    ],
    "max_tokens":512,
    "temperature":0.2
  }'

Output location

  • If `--output` is set, JSON response is saved to that file.
  • Default output dir convention: `output/aliyun-qwen-vl/`.

Smoke test

python tests/ai/multimodal/aliyun-qwen-vl-test/scripts/smoke_test_qwen_vl.py \
  --image ./tmp/vl_test_cat.png

Error handling

| Error | Likely cause | Action | | --- | --- | --- | | 401/403 | Missing or invalid key | Check `DASHSCOPE_API_KEY` and account permissions. | | 400 | Invalid request schema or unsupported image source | Validate `messages` content and image URL/path format. | | 429 | Rate limit | Retry with exponential backoff and lower concurrency. | | 5xx | Temporary backend issue | Retry with backoff and idempotent request design. |

Operational guidance

  • For stable production behavior, pin snapshot model IDs instead of pure `-latest`.
  • Compress very large images before upload to reduce latency and cost.
  • Add explicit extraction constraints in prompt (fields, JSON shape, language).
  • For OCR-like output, ask for confidence notes and unresolved text markers.

Workflow

1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. 2) Run one minimal read-only query first to verify connectivity and permissions. 3) Execute the target operation with explicit parameters and bounded scope. 4) Verify results and save output/evidence files.

References

  • Source list: `references/sources.md`
  • API notes: `references/api_reference.md`
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