sn-da-image-caption
图片理解与数据提取 skill。当图片文件(.png/.jpg/.jpeg/.gif/.webp/.bmp)是主要输入且用户需要理解、提取数据或分析图片内容时使用。提供预配置的 caption 脚本(scripts/caption.py),通过 vision 模型将图片转为文本描述,无需额外配置 API Key。覆盖:(1) 通过 scripts/caption.py 对图表/表格/截图/流程图进行 caption,(2) 将 caption 文本解析为结构化 DataFrame,(3) 基于提取数据重新生成可视化图表,(4) 导出为 Excel/CSV。**遇到以下任一情况就主动使用本 skill,不要自行猜测图片内容**:①用户出现触发词:图片分析 / 图表提取 / 表格识别 / OCR / 图片描述 / 截图分析 / 图表数据 / 提取图片中的数据 / 图片转表格 / 识别图片 / image caption / extract data from image / chart analysis / table OCR;②用户上传或指定了图片文件(.png / .jpg / .jpeg / .gif / .webp / .bmp)并要求理解、提取数据或分析内容;③任务需要从图表截图、表格截图、UI 截图、流程图中提取结构化信息;④用户要求将图片中的数据转为 Excel/CSV
npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-image-caption --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Image Caption Analysis — 图片描述与数据提取 ## Overview Analyze, extract data from, or understand image files (.png, .jpg, .jpeg, .gif, .webp, .bmp). The core workflow: 1. Run `scripts/caption.py` to get a text description of the image 2. Parse the description into structured data (DataFrame, etc.) 3. Analyze, visualize, or export ## scripts/caption.py — Image Caption The script converts images to text descriptions via a vision model. Configure via `SN_API_KEY` (minimum required), or use `SN_VISION_API_KEY` / `SN_VISION_BASE_URL` / `SN_VISION_MODEL` for fine-grained control. See the project environment variable spec for the full fallback chain. ### Usage ```bash # Basic — get text description python3 scripts/caption.py /mnt/data/image.png # Custom prompt — guide what to extract python3 scripts/caption.py /mnt/data/chart.png --prompt "提取所有数值,Markdown 表格格式" # JSON output — includes detected type, usage stats, cache info python3 scripts/caption.py /mnt/data/image.png --json # Batch — process all images in a directory python3 scripts/caption.py /mnt/data/images/ --batch --output /mnt/data/captions.json # Override model (optional) python3 scripts/caption.py /mnt/data/image.png --model gemini-3
- Overview
- scripts/caption.py — Image Caption
- Usage
- Options
- What it does automatically
- JSON output format
- Calling from Python
- Prompt Strategy
- Parsing Caption Results
- Visualization
- Chinese Font Setup (MANDATORY)
- Color Palette
- Save & Display
- Export to Excel
Basic — get text description python3 scripts/caption.py /mnt/data/image.png Custom prompt — guide what to extract python3 scripts/caption.py /mnt/data/chart.png --prompt "提取所有数值,Markdown 表格格式" JSON output — includes detected type, usage stats, cache info python3 scripts/caption.py /mnt/data/image.png --json Batch — process all images in a directory python3 scripts/caption.py /mnt/data/images/ --batch --output /mnt/data/captions.json Override model (optional) python3 scripts/caption.py /mnt/data/image.png --model gemini-3.1-flash-lite-preview
What does the sn-da-image-caption skill do?
图片理解与数据提取 skill。当图片文件(.png/.jpg/.jpeg/.gif/.webp/.bmp)是主要输入且用户需要理解、提取数据或分析图片内容时使用。提供预配置的 caption 脚本(scripts/caption.py),通过 vision 模型将图片转为文本描述,无需额外配置 API Key。覆盖:(1) 通过 scripts/caption.py 对图表/表格/截图/流程图进行 caption,(2) 将 caption 文本解析为结构化 DataFrame,(3) 基于提取数据重新生成可视化图表,(4) 导出为 Excel/CSV。**遇到以下任一情况就主动使用本 skill,不要自行猜测图片内容**:①用户出现触发词:图片分析 / 图表提取 / 表格识别 / OCR / 图片描述 / 截图分析 / 图表数据 / 提取图片中的数据 / 图片转表格 / 识别图片 / image caption / extract data from image / chart analysis / table OCR;②用户上传或指定了图片文件(.png / .jpg / .jpeg / .gif / .webp / .bmp)并要求理解、提取数据或分析内容;③任务需要从图表截图、表格截图、UI 截图、流程图中提取结构化信息;④用户要求将图片中的数据转为 Excel/CSV
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-image-caption --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,855 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
