GLM-V-Caption

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).

GLM-V Caption Skill

Generate captions for images, videos, and documents using the ZhiPu GLM-V multimodal model.

When to Use

• Describe, caption, summarize, or interpret image/video/document content
• User mentions "describe this image", "caption", "summarize this video", "图片描述", "视频摘要", "文档解读", "看图说话"
• Extract visual or textual information from media files
• Compare multiple images
• User provides an image/video/file and asks what's in it

Supported Input Types

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 Links

Resource	Link
Get API Key	https://bigmodel.cn/usercenter/proj-mgmt/apikeys
API Docs	Chat Completions / 对话补全

Prerequisites

API Key Setup / API Key 配置（Required / 必需）

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）：

1. OpenClaw config (recommended) / OpenClaw 配置（推荐）： Set in openclaw.json under skills.entries.glmv-caption.env:

   json

   "glmv-caption": { "enabled": true, "env": { "ZHIPU_API_KEY": "你的密钥" } }
   

2. Shell environment variable / Shell 环境变量： Add to ~/.zshrc:

   bash

   export ZHIPU_API_KEY="你的密钥"
   

3. .env file / .env 文件： Create .env in this skill directory:

   text

   ZHIPU_API_KEY=你的密钥
   

⛔ MANDATORY RESTRICTIONS - DO NOT VIOLATE ⛔

1. ONLY use GLM-V API — Execute the script python scripts/glmv_caption.py
2. NEVER caption media yourself — Do NOT try to describe content using built-in vision or any other method
3. NEVER offer alternatives — Do NOT suggest "I can try to describe it" or similar
4. IF API fails — Display the error message and STOP immediately
5. NO fallback methods — Do NOT attempt captioning any other way

📋 Output Display Rules (MANDATORY)

After 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.

• Image captioning: show the full caption text
• Multiple images: show each image result
• Video/files: show the full understanding result
• If token usage is included, you may optionally display it

How to Use

Caption an Image

bash

python scripts/glmv_caption.py --images "https://example.com/photo.jpg"
python scripts/glmv_caption.py --images /path/to/photo.png

Caption Multiple Images

bash

python scripts/glmv_caption.py --images img1.jpg img2.png "https://example.com/img3.jpg"

Caption a Video

bash

python scripts/glmv_caption.py --videos "https://example.com/clip.mp4"

Caption a Document

bash

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"

Custom Prompt

bash

python scripts/glmv_caption.py --images photo.jpg --prompt "Describe the architecture style in detail"

Save Result

bash

python scripts/glmv_caption.py --images photo.jpg --output result.json

Thinking Mode

bash

python scripts/glmv_caption.py --images photo.jpg --thinking

CLI Reference

text

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.

Response Format

json

{
  "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 succeeded
• caption — the generated caption text
• usage — token usage statistics
• warning — present when content was blocked by safety review
• error — error details on failure

Error Handling

API key not configured:

text

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