Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add countbot-ai/CountBot --skill image-analysisgit clone --depth 1 https://github.com/countbot-ai/CountBotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/countbot-ai/countbot/image-analysis)<a href="https://agentmods.dev/skills/countbot-ai/countbot/image-analysis"><img src="https://agentmods.dev/badge/skills/countbot-ai/countbot/image-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/countbot-ai/countbot/image-analysis"><img src="https://agentmods.dev/badge/skills/countbot-ai/countbot/image-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.00783 |
| Opus 5.5 | $0.00019 | $0.00313 |
| Sonnet 5.5 | $0.00010 | $0.00157 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade A, and why
image-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
图片分析与识别
支持智谱 GLM-4V 和千问 Qwen-VL 两种视觉模型。
当用户发送图片或要求分析图片时，必须使用此技能，不要使用 PIL、pytesseract 等其他方法。
配置
编辑 skills/image-analysis/scripts/config.json：
{
"default_model": "zhipu",
"zhipu": {
"api_key": "your-zhipu-api-key",
"model": "glm-4.6v-flash"
},
"qwen": {
"api_key": "your-qwen-api-key",
"model": "qwen3-vl-plus"
}
}
API Key 获取：
命令行调用
# 分析本地图片（最常用）
python3 skills/image-analysis/scripts/vision.py analyze --image 图片路径 --prompt "描述图片内容"
# 分析网络图片
python3 skills/image-analysis/scripts/vision.py analyze --image https://example.com/image.jpg --prompt "描述图片"
# 多图对比
python3 skills/image-analysis/scripts/vision.py analyze --image img1.jpg --image img2.jpg --prompt "对比差异"
# 指定模型
python3 skills/image-analysis/scripts/vision.py analyze --image image.jpg --prompt "描述图片" --model qwen
# 开启思考模式（仅智谱，提升准确度）
python3 skills/image-analysis/scripts/vision.py analyze --image image.jpg --prompt "详细分析" --thinking
# 视频分析
python3 skills/image-analysis/scripts/vision.py analyze --video video.mp4 --prompt "总结视频内容"
# JSON 输出
python3 skills/image-analysis/scripts/vision.py analyze --image image.jpg --prompt "描述图片" --json
AI 调用场景
用户发送图片后，系统下载到本地（如 data/temp/images/xxx.jpg）：
# 图片描述
python3 skills/image-analysis/scripts/vision.py analyze --image data/temp/images/xxx.jpg --prompt "描述这张图片的内容"
# OCR 识别
python3 skills/image-analysis/scripts/vision.py analyze --image data/temp/images/xxx.jpg --prompt "提取图片中的所有文字信息"
# 物体定位（开启思考模式）
python3 skills/image-analysis/scripts/vision.py analyze --image data/temp/images/xxx.jpg --prompt "找出物体位置，返回坐标" --thinking
模型选择
| 场景 | 推荐 |
|---|---|
| 简单描述 | 任意 |
| 复杂推理、物体定位 | 智谱 + --thinking |
| 高精度识别、文档解析 | 千问 |
| 成本敏感 | 智谱（免费） |
注意事项
- 本地图片自动转 Base64，支持 jpg/png/gif/webp/bmp
- 智谱图片限制 5MB，像素不超过 6000x6000
- 千问不支持同时处理图片、视频和文件
- 思考模式会增加响应时间但提升准确度
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 90 lines · 48 tokens per session scan A b78017024eca
image-analysis is a skill published in the GitHub repository countbot-ai/CountBot (782 stars, last pushed 6d ago), licensed MIT. It adds 48 tokens to every session and 783 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
gemini
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
rlm-reasoning-skill
Guides the RLM agent to use REPL code execution with recursive LM calls for complex reasoning, decomposition, and multi-step problem solving.
vision-skill
Understand images (describe content, answer questions about them, extract text/OCR) via a vision model. Use when the user asks what an image, screenshot, chart, scan, or photo contains.
knowledge-retrieval
Semantic search over ingested documents using RAG (LlamaIndex/ChromaDB or Foundational RAG).
openclaw
Use when setting up a multi-channel personal AI assistant gateway bridging chat platforms (Telegram, Discord, WhatsApp, WeChat) with LLM backends. OpenClaw: multi-channel AI assistant gateway for unified personal AI access.
openakita/skills@image-understanding
Analyze images using Dashscope (Qwen) Vision models for detailed description, OCR text extraction, object recognition, and visual Q&A. Use when the user needs to understand image content via Alibaba Cloud Dashscope API, especially for Chinese-language image analysis and documents.