zhipu-cogview-image_skill

Overview

This skill generates images using Zhipu AI's CogView model via the BigModel API. It provides a lightweight command-line approach and a curl-based example to send prompts and receive generated images. The workflow is designed for quick integration and local scripting with API key-based authentication.

How this skill works

The skill calls the BigModel image generation endpoint (https://open.bigmodel.cn/api/paas/v4/images/generations) using an Authorization header with a ZHIPU_API_KEY stored in the environment. Requests include the target model (cogview-4) and a text prompt; the API returns generated image data or links. The scripts include input validation, JSON escaping with jq, a 60s timeout, and generic error handling to reduce information leakage.

When to use it

• Create concept art, character designs, or visual prototypes from text prompts.
• Automate batch image generation in scripts or CI jobs where command-line tools are preferred.
• Quickly iterate on visual ideas without integrating a full SDK.
• Generate images for mockups, storyboards, or creative exploration.
• Archive or reproduce previously generated images from prompt histories.

Best practices

• Set ZHIPU_API_KEY as an environment variable at runtime; avoid storing secrets in dotfiles.
• Run generation commands in trusted environments to prevent API key exposure via process lists.
• Keep prompts concise and under the documented length limit (e.g., ≤1000 chars) to avoid rejections.
• Use HTTPS and enforce TLS 1.2+; respect the provided request timeout to avoid hanging processes.
• Handle API errors generically in UI logs to avoid leaking sensitive details.

Example use cases

• Single-image generation: call the curl example with a descriptive prompt to produce one image.
• Scripted batch runs: loop over a CSV of prompts and save results to organized folders.
• Prototype UI assets: quickly generate placeholders for apps and landing pages.
• Creative experimentation: test multiple prompt styles and parameters to refine output.
• Content backups: archive generated images and their prompts for reproducibility.