Aliyun Qwen Multimodal Embedding — 技能工具

Name: Aliyun Qwen Multimodal Embedding — 技能工具
Author: cinience

cinience

Aliyun Qwen Multimodal Embedding — 技能工具

v1.0.0

[自动翻译] Use when multimodal embeddings are needed from Alibaba Cloud Model Studio models such as `qwen3-vl-embedding` for image, video, and text retrieval, cr...

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by @cinience·MIT-0

AI模型访问云服务开发工具自动化系统工具

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License

MIT-0

最后更新

2026/4/2

安全扫描

VirusTotal

无害

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OpenClaw

可疑

high confidence

The skill's code and runtime behavior are simple and offline (it only writes a JSON payload), but the README asks for an API key and pairing with external vector stores that the code never uses — an incoherence worth clarifying before install.

评估建议

The code only prepares and writes a JSON request for Alibaba Cloud multimodal embeddings and does not call any network services. However, the documentation asks you to set DASHSCOPE_API_KEY or add credentials to ~/.alibabacloud/credentials and to pair with a vector store — neither is used by the included script. Before installing or providing credentials: (1) Ask the publisher why an API key is mentioned and whether the skill will ever make requests on your behalf; (2) If you don't need networke...

详细分析 ▾

⚠ 用途与能力

Name/description claim: generate multimodal embedding requests for Alibaba Cloud Model Studio. The included Python script exactly matches that purpose (it builds/writes a request JSON and does not call any network services). However SKILL.md's 'Prerequisites' asks the user to set DASHSCOPE_API_KEY or add credentials to ~/.alibabacloud/credentials and to 'pair this skill with a vector store' — none of which are used by the script. This mismatch looks like copy-paste or over-broad documentation and should be explained by the author.

⚠ 指令范围

Runtime instructions contain references to environment credentials (DASHSCOPE_API_KEY and ~/.alibabacloud/credentials) and advice to stage files in object storage, but the runtime artifact (scripts/prepare_multimodal_embedding_request.py) only composes JSON and writes to disk. There are no commands that read credentials, call network endpoints, or transmit data. The documentation thus grants broader scope than the code actually performs.

✓ 安装机制

This is an instruction-only skill with one small Python helper script and no install spec or remote downloads. No packages are fetched and nothing is written to system-wide locations during install — low install risk.

⚠ 凭证需求

No required env vars or primary credential are declared in registry metadata, but SKILL.md requests DASHSCOPE_API_KEY or an entry in ~/.alibabacloud/credentials. Because the code does not use these, the request for credentials is disproportionate and unexplained. If the skill will later be extended to call cloud APIs, requiring credentials would make sense — but as-is, asking for them is unnecessary and raises the risk of accidental credential exposure.

✓ 持久化与权限

The skill does not request always: true and has no install actions that modify other skills or system config. It has normal, limited presence (a single helper script) and no special privileges.

安全有层次，运行前请审查代码。

License

MIT-0

可自由使用、修改和再分发，无需署名。

查看条款 ↗

运行时依赖

无特殊依赖

版本

latestv1.0.02026/4/2

- Initial release of aliyun-qwen-multimodal-embedding skill. - Supports generation of multimodal embeddings (text, image, video) using Alibaba Cloud Model Studio models for retrieval, search, clustering, or offline vectorization. - Provides normalized embedding.multimodal interface with customizable model, input types, and output dimensions. - Includes validation and reproducibility steps, plus guidance for pairing with vector stores. - Documents exact supported model names and selection guidance.

● 无害

安装命令点击复制

官方npx clawhub@latest install aliyun-qwen-multimodal-embedding

镜像加速npx clawhub@latest install aliyun-qwen-multimodal-embedding --registry https://cn.clawhub-mirror.com

技能文档

Category: provider

# Model Studio Multimodal Embedding

Validation

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

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

Output And Evidence

Save normalized request payloads, selected dimensions, and sample input references under output/aliyun-qwen-multimodal-embedding/.
Record the exact model, modality mix, and output vector dimension for reproducibility.

Use this skill when the task needs text, image, or video embeddings from Model Studio for retrieval or similarity workflows.

Critical model names

Use one of these exact model strings as needed:

qwen3-vl-embedding
qwen2.5-vl-embedding
tongyi-embedding-vision-plus-2026-03-06

Selection guidance:

Prefer qwen3-vl-embedding for the newest multimodal embedding path.
Use qwen2.5-vl-embedding when you need compatibility with an older deployed pipeline.

Prerequisites

Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.
Pair this skill with a vector store such as DashVector, OpenSearch, or Milvus when building retrieval systems.

Normalized interface (embedding.multimodal)

Request

model (string, optional): default qwen3-vl-embedding
texts (array, optional)
images (array, optional): public URLs or local paths uploaded by your client layer
videos (array, optional): public URLs where supported
dimension (int, optional): e.g. 2560, 2048, 1536, 1024, 768, 512, 256 for qwen3-vl-embedding

Response

embeddings (array
)
dimension (int)

usage (object, optional)
Quick start
```
python skills/ai/search/aliyun-qwen-multimodal-embedding/scripts/prepare_multimodal_embedding_request.py \
  --text "A cat sitting on a red chair" \
  --image "https://example.com/cat.jpg" \
  --dimension 1024
```
Operational guidance
- Keep input.contents as an array; malformed shapes are a common 400 cause.
- Pin the output dimension to match your index schema before writing vectors.
- Use the same model and dimension across one vector index to avoid mixed-vector incompatibility.
- For large image or video batches, stage files in object storage and reference stable URLs.
Output location
- Default output: output/aliyun-qwen-multimodal-embedding/request.json
- Override base dir with OUTPUT_DIR.
References
- references/sources.md
Category: provider
# Model Studio Multimodal Embedding
Validation
```
mkdir -p output/aliyun-qwen-multimodal-embedding
python -m py_compile skills/ai/search/aliyun-qwen-multimodal-embedding/scripts/prepare_multimodal_embedding_request.py && echo "py_compile_ok" > output/aliyun-qwen-multimodal-embedding/validate.txt
```
Pass criteria: command exits 0 and output/aliyun-qwen-multimodal-embedding/validate.txt is generated.
Output And Evidence
- Save normalized request payloads, selected dimensions, and sample input references under output/aliyun-qwen-multimodal-embedding/.
- Record the exact model, modality mix, and output vector dimension for reproducibility.
Use this skill when the task needs text, image, or video embeddings from Model Studio for retrieval or similarity workflows.
Critical model names
Use one of these exact model strings as needed:
- qwen3-vl-embedding
- qwen2.5-vl-embedding
- tongyi-embedding-vision-plus-2026-03-06
Selection guidance:
- Prefer qwen3-vl-embedding for the newest multimodal embedding path.
- Use qwen2.5-vl-embedding when you need compatibility with an older deployed pipeline.
Prerequisites
- Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.
- Pair this skill with a vector store such as DashVector, OpenSearch, or Milvus when building retrieval systems.
Normalized interface (embedding.multimodal)
Request
- model (string, optional): default qwen3-vl-embedding
- texts (array, optional)
- images (array, optional): public URLs or local paths uploaded by your client layer
- videos (array, optional): public URLs where supported
- dimension (int, optional): e.g. 2560, 2048, 1536, 1024, 768, 512, 256 for qwen3-vl-embedding
Response
- embeddings (array
  )
  dimension (int)
  
  usage (object, optional)
  Quick start
  python skills/ai/search/aliyun-qwen-multimodal-embedding/scripts/prepare_multimodal_embedding_request.py \ --text "A cat sitting on a red chair" \ --image "https://example.com/cat.jpg" \ --dimension 1024
  Operational guidance
  Keep input.contents as an array; malformed shapes are a common 400 cause.
  
  Pin the output dimension to match your index schema before writing vectors.
  
  Use the same model and dimension across one vector index to avoid mixed-vector incompatibility.
  
  For large image or video batches, stage files in object storage and reference stable URLs.
  Output location
  Default output: output/aliyun-qwen-multimodal-embedding/request.json
  
  Override base dir with OUTPUT_DIR.
  References
  references/sources.md
  
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License

运行时依赖

版本

安装命令 点击复制

技能文档

Validation

Output And Evidence

Critical model names

Prerequisites

Normalized interface (embedding.multimodal)

Request

Response

Quick start

Operational guidance

Output location

References

Validation

Output And Evidence

Critical model names

Prerequisites

Normalized interface (embedding.multimodal)

Request

Response

Quick start

Operational guidance

Output location

References

安装命令点击复制