Agent setup: If your agent doesn't auto-load skills (e.g. Claude Code),
see agent-compatibility.md once per session.
Qwen Vision (Image & Video Understanding)
Analyze images and videos using Qwen VL and QVQ models.
This skill is part of qwencloud/qwencloud-ai.
Skill directory
Use this skill's internal files to execute and learn. Load reference files on demand when the default path fails or you need details.
| Location | Purpose |
|---|
| INLINECODE0 | Image/video understanding, multi-image, thinking mode |
| INLINECODE1 |
Visual reasoning (QVQ, chain-of-thought, streaming) |
|
scripts/ocr.py | OCR text extraction |
|
scripts/vision_lib.py | Shared helpers (base64, upload, streaming) |
|
references/execution-guide.md | Fallback: curl, code generation |
|
references/curl-examples.md | Curl for base64, multi-image, video, OCR |
|
references/visual-reasoning.md | QVQ and thinking mode details |
|
references/prompt-guide.md | Query prompt templates by task, thinking mode decision |
|
references/ocr.md | OCR parameters and examples |
|
references/sources.md | Official documentation URLs |
|
references/agent-compatibility.md | Agent self-check: register skills in project config for agents that don't auto-load |
Security
NEVER output any API key or credential in plaintext. Always use variable references ($DASHSCOPE_API_KEY in shell, os.environ["DASHSCOPE_API_KEY"] in Python). Any check or detection of credentials must be non-plaintext: report only status (e.g. "set" / "not set", "valid" / "invalid"), never the value. Never display contents of .env or config files that may contain secrets.
When the API key is not configured, NEVER ask the user to provide it directly. Instead, help create a .env file with a placeholder (DASHSCOPE_API_KEY=sk-your-key-here) and instruct the user to replace it with their actual key from the QwenCloud Console. Only write the actual key value if the user explicitly requests it.
Key Compatibility
Scripts require a standard QwenCloud API key (sk-...). Coding Plan keys (sk-sp-...) cannot be used for direct API calls and do not support dedicated vision models (qwen3-vl-plus, qvq-max, etc.). The scripts detect sk-sp- keys at startup and print a warning. If qwencloud-ops-auth is installed, see its references/codingplan.md for full details.
Model Selection
| Model | Use Case |
|---|
| qwen3.5-plus | Preferred — unified multimodal (text+image+video). Thinking on by default. |
| qwen3.5-flash |
Fast multimodal — cheaper, faster. Thinking on by default. |
|
qwen3-vl-plus | High-precision — object localization (2D/3D), document/webpage parsing. |
|
qwen3-vl-flash | Fast vision — lower latency, 33 languages. |
|
qvq-max | Visual reasoning — chain-of-thought for math, charts.
Streaming only. |
|
qwen-vl-ocr | OCR — text extraction, table parsing, document scanning. |
|
qwen-vl-max | Qwen2.5-VL — best-performing in 2.5 series. |
|
qwen-vl-plus | Qwen2.5-VL — faster, good balance of performance and cost, 11 languages. |
- 1. User specified a model → use directly.
- Consult the qwencloud-model-selector skill when model choice depends on requirement, scenario, or pricing.
- No signal, clear task →
qwen3.5-plus. Use qwen3-vl-plus for precise localization or 3D detection.
⚠️ Important: The model list above is a point-in-time snapshot and may be outdated. Model availability
changes frequently. Always check the official model list
for the authoritative, up-to-date catalog before making model decisions.
Execution
Prerequisites
- - API Key: Check that
DASHSCOPE_API_KEY (or QWEN_API_KEY) is set using a non-plaintext check only (e.g. in shell:
[ -n "$DASHSCOPE_API_KEY" ]; report only "set" or "not set", never the key value). If not set: run the *
qwencloud-ops-auth* skill if available; otherwise guide the user to obtain a key from
QwenCloud Console and set it via
.env file (
echo 'DASHSCOPE_API_KEY=sk-your-key-here' >> .env in project root or current directory) or environment variable. The
script searches for
.env in the current working directory and the project root. Skills may be installed
independently — do not assume qwencloud-ops-auth is present.
- - Python 3.9+ (stdlib only, no pip install needed)
Environment Check
Before first execution, verify Python is available:
CODEBLOCK0
If python3 is not found, try python --version or py -3 --version. If Python is unavailable or below 3.9, skip to Path 2 (curl) in execution-guide.md.
Default: Run Script
Script path: Scripts are in the scripts/ subdirectory of this skill's directory (the directory containing this SKILL.md). You MUST first locate this skill's installation directory, then ALWAYS use the full absolute path to execute scripts. Do NOT assume scripts are in the current working directory. Do NOT use cd to switch directories before execution. Shared infrastructure lives in scripts/vision_lib.py.
Execution note: Run all scripts in the foreground — wait for stdout; do not background.
Discovery: Run python3 <this-skill-dir>/scripts/analyze.py --help (or reason.py, ocr.py) first to see all available arguments.
| Script | Purpose | Default Model |
|---|
| INLINECODE37 | Image understanding, multi-image, video, thinking mode, high-res | INLINECODE38 |
| INLINECODE39 |
Visual reasoning with chain-of-thought, video reasoning (always streaming) |
qvq-max |
|
scripts/ocr.py | OCR text extraction from documents, receipts, tables |
qwen-vl-ocr |
Input type fields (use exactly one in --request JSON):
| Field | Use for | Example |
|---|
| INLINECODE44 | Single image (URL or local path) | INLINECODE45 |
| INLINECODE46 |
Multi-image comparison (array) |
"images": ["a.jpg", "b.jpg"] |
|
"video" | Video file (URL or local path) |
"video": "clip.mp4" |
|
"video_frames" | Video as frame array |
"video_frames": ["f1.jpg", "f2.jpg"] |
⚠️ Common mistake: Do NOT use "image" for video files — use "video" instead.
CODEBLOCK1
| Argument | Description |
|---|
| INLINECODE54 | JSON request body |
| INLINECODE55 |
Load request from file |
|
--output path | Save response JSON to path |
|
--print-response | Print response to stdout |
|
--stream | Enable streaming (auto for thinking/QVQ) |
|
--upload-files | Upload local files to temp storage (for files > 7 MB) |
|
--schema path.json | JSON Schema for structured extraction |
Verify Result
- - Exit code
0 + output contains valid JSON with choices field → success - Non-zero exit, HTTP error, empty response, or JSON with
"code"/"message" error → fail - Post-execution check: When
--output is used, verify the response JSON file exists and contains expected content - MANDATORY — stderr signal check: After confirming the result, scan the command's stderr output for
[ACTION_REQUIRED] or [UPDATE_AVAILABLE]. If either signal is present, you MUST follow the instructions in Update Check below before responding to the user.
On Failure
If scripts fail, match the error output against the diagnostic table below to determine the resolution. If no match, read execution-guide.md for alternative paths: curl commands (Path 2), code generation (Path 3), and autonomous resolution (Path 5).
If Python is not available at all → skip directly to Path 2 (curl) in execution-guide.md.
| Error Pattern | Diagnosis | Resolution |
|---|
| INLINECODE68 | Python not on PATH | Try python or py -3; install Python 3.9+ if missing |
| INLINECODE71 |
Script version check failed | Upgrade Python to 3.9+ |
|
SyntaxError near type hints | Python < 3.9 | Upgrade Python to 3.9+ |
|
QWEN_API_KEY/DASHSCOPE_API_KEY not found | Missing API key | Obtain key from
QwenCloud Console; add to
.env:
echo 'DASHSCOPE_API_KEY=sk-...' >> .env; or run
qwencloud-ops-auth if available |
|
HTTP 401 | Invalid or mismatched key | Run
qwencloud-ops-auth (non-plaintext check only); verify key is valid |
|
SSL: CERTIFICATE_VERIFY_FAILED | SSL cert issue (proxy/corporate) | macOS: run
Install Certificates.command; else set
SSL_CERT_FILE env var |
|
URLError /
ConnectionError | Network unreachable | Check internet; set
HTTPS_PROXY if behind proxy |
|
HTTP 429 | Rate limited | Wait and retry with backoff |
|
HTTP 5xx | Server error | Retry with backoff |
|
PermissionError | Can't write output | Use
--output to specify writable directory |
File Input
The API accepts: HTTP/HTTPS URL, Base64 data URI, and oss:// URL. Local file paths are NOT directly supported — scripts handle conversion automatically. Pass local paths directly; no manual upload step needed.
Large file rule: If the local file is >= 7 MB, always add --upload-files. Base64 encoding inflates size by ~33% and will exceed the 10 MB API limit. Small files (including short video clips < 7 MB) can use the default base64 path.
| Method | When to use | How |
|---|
| Online URL | File already hosted | Pass URL directly — preferred for large files |
| Base64 (default) |
Local files < 7 MB (images or short video clips) | Script auto-converts to
data: URI |
|
Temp upload | Local files >= 7 MB | Add
--upload-files flag → uploads to DashScope temp storage (
oss:// URL, 48h TTL) |
Production: Default temp storage has 48h TTL and 100 QPS upload limit — not suitable for production, high-concurrency, or load-testing. To use your own OSS bucket, set QWEN_TMP_OSS_BUCKET and QWEN_TMP_OSS_REGION in .env, install pip install alibabacloud-oss-v2, and provide credentials via QWEN_TMP_OSS_AK_ID / QWEN_TMP_OSS_AK_SECRET or the standard OSS_ACCESS_KEY_ID / OSS_ACCESS_KEY_SECRET. Use a RAM user with least-privilege (oss:PutObject + oss:GetObject on target bucket only). The --upload-files flag is still required for vision scripts to trigger upload. If qwencloud-ops-auth is installed, see its references/custom-oss.md for the full setup guide.
Input from Other Skills
When the input file comes from another skill's output (e.g., image-gen, video-gen):
- - Pass the URL directly (e.g.,
"image": "<image_url from image-gen>") — do NOT download the URL first - Downloading and re-passing as a local path wastes bandwidth and triggers unnecessary base64 encoding or OSS upload
- All URL types are supported:
https://, oss://, INLINECODE107
Thinking Mode
| Model | Thinking Default | Notes |
|---|
| INLINECODE108 / INLINECODE109 | On | Disable with enable_thinking: false for simple tasks. |
| INLINECODE111 / INLINECODE112 |
Off | Enable with
enable_thinking: true. |
|
qvq-max | Always on |
Streaming output required. |
See visual-reasoning.md for details.
OCR (qwen-vl-ocr)
Optimized for text extraction. Supports multi-language, skewed images, tables, formulas. See ocr.md for parameters and examples.
Input Limits
Images: BMP/JPEG/PNG/TIFF/WEBP/HEIC. Min 10px sides, aspect ratio <= 200:1. Max 20 MB (URL, Qwen3.5) / 10 MB (others).
Videos: MP4/AVI/MKV/MOV/FLV/WMV. Duration 2s–2h (Qwen3.5) / 2s–10min (others). Max 2 GB (URL) / 10 MB (base64). fps range [0.1, 10], default 2.0.
Error Handling
| HTTP | Meaning | Action |
|---|
| 401 | Invalid or missing API key | Run qwencloud-ops-auth if available; else prompt user to set key (non-plaintext check only) |
| 400 |
Bad request (invalid format) | Verify messages format and image URL/format |
| 429 | Rate limited | Retry with exponential backoff |
| 5xx | Server error | Retry with exponential backoff |
Output Location
Prefer the current working directory. Default subdirectory: ./output/qwencloud-vision/.
Write prohibition: NEVER write output files into this skill's installation directory or any skills/ hierarchy. All generated content must go to output/ under the current working directory or a user-specified path.
Update Check (MANDATORY Post-Execution)
After script execution, check stderr for action signals. The script automatically detects update status and emits structured signals. You MUST act on them. This section also applies when the user explicitly asks to check for updates.
Signal: [ACTION_REQUIRED] qwencloud-update-check skill is not installed
When stderr contains this signal, you MUST present the following options to the user and wait for their response before proceeding. Do NOT silently skip this.
The update-check skill is not installed. It keeps you informed about new qwencloud/qwencloud-ai features and bug fixes.
1) Install now — I'll run the install command for you
2) Skip for now — do nothing this time
3) Never remind me — permanently suppress this prompt
After the user responds:
- - User chooses 1 → run:
npx skills add QwenCloud/qwencloud-ai --skill qwencloud-update-check -y, then run: INLINECODE120 - User chooses 2 → run:
python3 <this-skill-scripts-dir>/gossamer.py --dismiss, then continue normally - User chooses 3 → run:
python3 <this-skill-scripts-dir>/gossamer.py --never-install, then continue normally
Signal: [UPDATE_AVAILABLE]
When stderr contains this signal, you MUST append a brief update notice to your response, including the version info and the update command shown in the stderr output.
No signal in stderr
If stderr contains neither [ACTION_REQUIRED] nor [UPDATE_AVAILABLE], no action is needed — the skill is installed and up to date (or cached within 24h).
Explicit user request
When the user explicitly asks to check for updates (e.g. "check for updates", "check version"):
- 1. Look for
qwencloud-update-check/SKILL.md in sibling skill directories. - If found — run:
python3 <qwencloud-update-check-dir>/scripts/check_update.py --print-response and report the result. - If not found — present the install options above.
References
Agent 设置:如果你的代理不会自动加载技能(例如 Claude Code),请在每个会话中查看一次 agent-compatibility.md。
Qwen 视觉(图像与视频理解)
使用 Qwen VL 和 QVQ 模型分析图像和视频。
此技能是 qwencloud/qwencloud-ai 的一部分。
技能目录
使用此技能的内部文件来执行和学习。当默认路径失败或需要详细信息时,按需加载参考文件。
| 位置 | 用途 |
|---|
| scripts/analyze.py | 图像/视频理解、多图像、思考模式 |
| scripts/reason.py |
视觉推理(QVQ、思维链、流式输出) |
| scripts/ocr.py | OCR 文本提取 |
| scripts/vision_lib.py | 共享辅助函数(base64、上传、流式输出) |
| references/execution-guide.md | 备用方案:curl、代码生成 |
| references/curl-examples.md | 用于 base64、多图像、视频、OCR 的 curl 示例 |
| references/visual-reasoning.md | QVQ 和思考模式详情 |
| references/prompt-guide.md | 按任务、思考模式决策的查询提示模板 |
| references/ocr.md | OCR 参数和示例 |
| references/sources.md | 官方文档 URL |
| references/agent-compatibility.md | 代理自检:为不自动加载技能的代理在项目配置中注册技能 |
安全
切勿以明文形式输出任何 API 密钥或凭证。 始终使用变量引用(shell 中使用 $DASHSCOPEAPIKEY,Python 中使用 os.environ[DASHSCOPEAPIKEY])。任何对凭证的检查或检测必须是非明文的:仅报告状态(例如“已设置”/“未设置”、“有效”/“无效”),切勿报告值本身。切勿显示可能包含机密的 .env 或配置文件内容。
当 API 密钥未配置时,切勿要求用户直接提供。 相反,应帮助创建一个包含占位符(DASHSCOPEAPIKEY=sk-your-key-here)的 .env 文件,并指导用户从 QwenCloud 控制台 将其替换为实际密钥。仅当用户明确要求时,才写入实际的密钥值。
密钥兼容性
脚本需要标准的 QwenCloud API 密钥(sk-...)。编码计划密钥(sk-sp-...)不能用于直接 API 调用,也不支持专用的视觉模型(qwen3-vl-plus、qvq-max 等)。脚本在启动时会检测 sk-sp- 密钥并打印警告。如果安装了 qwencloud-ops-auth,请参阅其 references/codingplan.md 以获取完整详情。
模型选择
| 模型 | 使用场景 |
|---|
| qwen3.5-plus | 首选 — 统一多模态(文本+图像+视频)。默认开启思考模式。 |
| qwen3.5-flash |
快速多模态 — 更便宜、更快。默认开启思考模式。 |
|
qwen3-vl-plus | 高精度 — 物体定位(2D/3D)、文档/网页解析。 |
|
qwen3-vl-flash | 快速视觉 — 更低延迟,支持 33 种语言。 |
|
qvq-max | 视觉推理 — 用于数学、图表的思维链。
仅支持流式输出。 |
|
qwen-vl-ocr | OCR — 文本提取、表格解析、文档扫描。 |
|
qwen-vl-max | Qwen2.5-VL — 2.5 系列中性能最佳。 |
|
qwen-vl-plus | Qwen2.5-VL — 速度更快,性能和成本平衡良好,支持 11 种语言。 |
- 1. 用户指定了模型 → 直接使用。
- 当模型选择取决于需求、场景或定价时,请咨询 qwencloud-model-selector 技能。
- 无信号,任务明确 → qwen3.5-plus。对于精确定位或 3D 检测,使用 qwen3-vl-plus。
⚠️ 重要提示:上述模型列表是时间点快照,可能已过时。模型可用性
经常变化。在做出模型决策之前,务必查看官方模型列表
以获取权威的最新目录。
执行
前提条件
- - API 密钥:使用非明文检查(例如在 shell 中:[ -n $DASHSCOPEAPIKEY ];仅报告“已设置”或“未设置”,切勿报告密钥值)来确认 DASHSCOPEAPIKEY(或 QWENAPIKEY)是否已设置。如果未设置:运行 qwencloud-ops-auth 技能(如果可用);否则指导用户从 QwenCloud 控制台 获取密钥,并通过 .env 文件(在项目根目录或当前目录执行 echo DASHSCOPEAPI_KEY=sk-your-key-here >> .env)或环境变量进行设置。脚本会在当前工作目录和项目根目录中搜索 .env。技能可能独立安装 — 不要假定 qwencloud-ops-auth 存在。
- Python 3.9+(仅标准库,无需 pip 安装)
环境检查
在首次执行之前,确认 Python 可用:
bash
python3 --version # 必须是 3.9+
如果找不到 python3,请尝试 python --version 或 py -3 --version。如果 Python 不可用或低于 3.9,请跳转到 execution-guide.md 中的路径 2 (curl)。
默认:运行脚本
脚本路径:脚本位于此技能目录(包含此 SKILL.md 的目录)的 scripts/ 子目录中。你必须首先找到此技能的安装目录,然后始终使用完整的绝对路径来执行脚本。 不要假定脚本位于当前工作目录。不要在执行前使用 cd 切换目录。共享基础设施位于 scripts/vision_lib.py。
执行说明: 在前台运行所有脚本 — 等待标准输出;不要后台运行。
发现: 首先运行 python3 <此技能目录>/scripts/analyze.py --help(或 reason.py、ocr.py)以查看所有可用参数。
| 脚本 | 用途 | 默认模型 |
|---|
| scripts/analyze.py | 图像理解、多图像、视频、思考模式、高分辨率 | qwen3.5-plus |
| scripts/reason.py |
带思维链的视觉推理、视频推理(始终流式输出) | qvq-max |
| scripts/ocr.py | 从文档、收据、表格中提取 OCR 文本 | qwen-vl-ocr |
输入类型字段(在 --request JSON 中仅使用一个):
| 字段 | 用途 | 示例 |
|---|
| image | 单张图像(URL 或本地路径) | image: photo.jpg |
| images |
多图像比较(数组) | images: [a.jpg, b.jpg] |
| video | 视频文件(URL 或本地路径) | video: clip.mp4 |
| video
frames | 视频作为帧数组 | videoframes: [f1.jpg, f2.jpg] |
⚠️ 常见错误:不要对视频文件使用 image — 应使用 video。
bash
图像分析
python3 <此技能目录>/scripts/analyze.py \
--request {prompt:这张图片里有什么?,image:https://example.com/photo.jpg} \
--output output/qwencloud-vision/result.json --print-response
视频分析(本地文件 — 对于 >= 7 MB 的文件添加 --upload-files)
python3 <此技能目录>/scripts/analyze.py \
--request {prompt:描述这个视频中发生了什么,video:clip.mp4} \
--upload-files --print-response
python3 <此技能目录>/scripts/reason.py \
--request {prompt:逐步解决这个数学问题,image:problem.png} \
--print-response
python3 <此技能目录>/scripts/ocr.py \
--request {image