// Generate captions (descriptions) for images, videos, and documents using ZhiPu GLM-V multimodal model series. Use this skill whenever the user wants to describe, caption, summarize, or interpret the content of images, videos, or files. Supports single/multiple inputs, URLs, local paths, and base64 (images only).
股票分析与涨跌预测分析。 在用户表达分析、判断或预测意图时触发，如“分析一下腾讯”、“0700最近走势如何”、“XX能不能买”、“预测一下后续走势”、“生成一份分析报告”等； 对于简单查询类需求（如“腾讯当前价格是多少”、“茅台代码是什么”）不触发本 Skill。 支持港股、A股、美股，整合多源数据（包括新闻、基本面、技术面、资金流及宏观信息）进行多维综合分析，输出图文结合、包含可视化图表的结构化分析报告。 ⚠️ 需要多模态主模型支持（如 glm-5v-turbo），主模型需能读取图片。
Write a textual content based on given document(s) and requirements, using ZhiPu GLM-V multimodal model. Read and comprehend one or multiple documents (PDF/DOCX), write a content in Markdown format according to the specified requirements. Use when the user wants to draft a paper/article/essay/report/review/post/brief/proposal/plan, etc.
A skill that uses GLM-V native grounding capabilities for coordinate conversion, bounding-box visualization, and more. GLM-V native grounding can locate any target specified by the prompt in an image and output relative coordinates normalized to 0-1000 based on image size. Coordinate formats include 2D bounding box (default), 2D points, and 3D bounding box. GLM-V also supports spatiotemporal localization and tracking of multiple prompt-specified targets in videos, outputting 2D bounding boxes per second.
Convert a PDF (research paper, report, or any document) into a polished multi-slide HTML presentation with a structured outline JSON and summary markdown. Trigger this skill when the user mentions making slides or a PPT from a PDF — in Chinese or English.
Convert a PDF (research paper, technical report, or project document) into a beautiful single-page academic/project website with a structured outline JSON. Trigger this skill when the user wants to make a paper page, project homepage, or academic website from a PDF — in Chinese or English.
Build a complete, production-ready full-stack web application from PRD documents, prototype images, and resource files. Handles the entire pipeline: system design, database schema, seed data, backend API, frontend UI, visual verification against prototypes, and deployment script generation. Use this skill whenever the user: - Provides a PRD (product requirement document) and wants a working app built - Says things like "根据PRD开发", "build from PRD", "implement this product", "把需求文档做成应用", "develop this app from requirements" - Has prototype images + requirements and wants full-stack implementation - Wants to turn product specifications into a running web application - Mentions building an app from wireframes/mockups combined with a requirements doc Trigger this skill even if the user just says "帮我开发" or "build this" with PRD materials present in the working directory.
| name | glmv-caption |
| description | Generate captions (descriptions) for images, videos, and documents using ZhiPu GLM-V multimodal model series. Use this skill whenever the user wants to describe, caption, summarize, or interpret the content of images, videos, or files. Supports single/multiple inputs, URLs, local paths, and base64 (images only). |
| metadata | {"openclaw":{"requires":{"env":["ZHIPU_API_KEY"],"bins":["python"]},"primaryEnv":"ZHIPU_API_KEY","emoji":"🖼️","homepage":"https://github.com/zai-org/GLM-V/tree/main/skills/glmv-caption"}} |
Generate captions for images, videos, and documents using the ZhiPu GLM-V multimodal model.
| Type | Formats | Max Size | Max Count | Base64 |
|---|---|---|---|---|
| Image | jpg, png, jpeg | 5MB / 6000×6000px | 50 | ✅ |
| Video | mp4, mkv, mov | 200MB | — | ❌ |
| File | pdf, docx, txt, xlsx, pptx, jsonl | — | 50 | ❌ |
⚠️ file_url cannot mix with image_url or video_url in the same request. ⚠️ Videos and files only support URLs — local paths and base64 are NOT supported (images only).
| Resource | Link |
|---|---|
| Get API Key | https://bigmodel.cn/usercenter/proj-mgmt/apikeys |
| API Docs | Chat Completions / 对话补全 |
This script reads the key from the ZHIPU_API_KEY environment variable and shares it with other Zhipu skills.
脚本通过 ZHIPU_API_KEY 环境变量获取密钥，与其他智谱技能共用同一个 key。
Get Key / 获取 Key： Visit Zhipu Open Platform API Keys / 智谱开放平台 API Keys to create or copy your key.
Setup options / 配置方式（任选一种）：
OpenClaw config (recommended) / OpenClaw 配置（推荐）： Set in openclaw.json under skills.entries.glmv-caption.env:
"glmv-caption": { "enabled": true, "env": { "ZHIPU_API_KEY": "你的密钥" } }
Shell environment variable / Shell 环境变量： Add to ~/.zshrc:
export ZHIPU_API_KEY="你的密钥"
.env file / .env 文件： Create .env in this skill directory:
ZHIPU_API_KEY=你的密钥
⛔ MANDATORY RESTRICTIONS - DO NOT VIOLATE ⛔
python scripts/glmv_caption.pyAfter running the script, you must show the full raw output to the user exactly as returned. Do not summarize, truncate, or only say "generated". Users need the original model output to evaluate quality.
python scripts/glmv_caption.py --images "https://example.com/photo.jpg"
python scripts/glmv_caption.py --images /path/to/photo.png
python scripts/glmv_caption.py --images img1.jpg img2.png "https://example.com/img3.jpg"
python scripts/glmv_caption.py --videos "https://example.com/clip.mp4"
python scripts/glmv_caption.py --files "https://example.com/report.pdf"
python scripts/glmv_caption.py --files "https://example.com/doc1.docx" "https://example.com/doc2.txt"
python scripts/glmv_caption.py --images photo.jpg --prompt "Describe the architecture style in detail"
python scripts/glmv_caption.py --images photo.jpg --output result.json
python scripts/glmv_caption.py --images photo.jpg --thinking
python {baseDir}/scripts/glmv_caption.py (--images IMG [IMG...] | --videos VID [VID...] | --files FILE [FILE...]) [OPTIONS]
| Parameter | Required | Description |
|---|---|---|
--images, -i | One of | Image paths or URLs (supports multiple, base64 OK) |
--videos, -v | One of | Video paths or URLs (supports multiple, mp4/mkv/mov) |
--files, -f | One of | Document paths or URLs (supports multiple, pdf/docx/txt/xlsx/pptx/jsonl) |
--prompt, -p | No | Custom prompt (default: "请详细描述这张图片的内容" / "Please describe this image in detail") |
--model, -m | No | Model name (default: glm-4.6v) |
--temperature, -t | No | Sampling temperature 0-1 (default: 0.8) |
--top-p | No | Nucleus sampling 0.01-1.0 (default: 0.6) |
--max-tokens | No | Max output tokens (default: 1024, max 32768) |
--thinking | No | Enable thinking/reasoning mode |
--output, -o | No | Save result JSON to file |
--pretty | No | Pretty-print JSON output |
--stream | No | Enable streaming output |
Note: --images, --videos, and --files are mutually exclusive per API limits.
{
"success": true,
"caption": "A landscape photo showing a mountain range at sunset...",
"usage": {
"prompt_tokens": 128,
"completion_tokens": 256,
"total_tokens": 384
}
}
Key fields:
success — whether the request succeededcaption — the generated caption textusage — token usage statisticswarning — present when content was blocked by safety reviewerror — error details on failureAPI key not configured:
ZHIPU_API_KEY not configured. Get your API key at: https://bigmodel.cn/usercenter/proj-mgmt/apikeys
→ Show exact error to user, guide them to configure
Authentication failed (401/403): API key invalid/expired → reconfigure
Rate limit (429): Quota exhausted → inform user to wait
File not found: Local file missing → check path
Content filtered: warning field present → content blocked by safety review