GLM Skills: Zhipu AI Official Skill Library, Providing Standardized Capability Expansion for the Agent Ecosystem

GLM Skills is an official skill collection project launched by Zhipu AI, aiming to provide standardized capability expansion interfaces for the GLM series of large models. This project integrates skills scattered across various model repositories into a unified codebase, supporting mainstream AI programming agents such as Claude Code, OpenCode, OpenClaw, AutoClaw, covering over 20 practical capabilities including multimodal visual understanding, OCR recognition, and image generation. Its core goal is to lower the integration threshold for developers and promote the construction of an open agent ecosystem.

Before the project was launched, GLM-related skills were scattered across different model repositories; after integration into a unified codebase, it marks an important step for Zhipu AI in the construction of an open agent ecosystem. Against the backdrop of large model capability homogenization, GLM Skills establishes an open platform through an open-source standardized skill library, contrasting with OpenAI's GPTs Store and Anthropic's Claude Artifacts. It emphasizes skill portability and interoperability, avoids vendor lock-in, and supports multiple agent frameworks to attract different user groups.

Skills are divided into four categories: 1. GLM-V series (multimodal visual understanding, e.g., glmv-caption for image description, glmv-prd-to-app for building web applications from PRDs, etc.); 2. GLM-OCR series (document intelligent recognition, e.g., glmocr for general text extraction, glmocr-formula for converting mathematical formulas to LaTeX, etc.); 3. GLM-Image series (image generation, e.g., glm-image-gen for text-to-image); 4. Meta series (skill management, e.g., glm-master-skill for skill discovery and installation guides). Installation methods: Clawhub is recommended (using npx commands to install single or batch skills) or cloning the source code from GitHub for manual configuration; authentication requires setting the ZHIPU_API_KEY environment variable, and it is recommended to store it in a .env file.

Typical skill examples: GLM-V's glmv-pdf-to-ppt (converting PDF to HTML presentations), glmv-stock-analyst (generating multi-source stock analysis reports); GLM-OCR's glmocr-table (converting tables to Markdown); GLM-Image's glm-image-gen (generating high-quality images based on text). Application scenarios include: Developers quickly integrating capabilities to improve efficiency; enterprises automating resume screening and document conversion to reduce labor costs; academic fields using formula recognition and paper-to-website conversion to assist research; creative industries using image generation and prompt generation to empower creation.

Technical advantages: Standardized interfaces (unified specifications support collaborative work), modular design (independent installation and updates), multi-agent compatibility (supports multiple frameworks), progressive complexity (meets needs at different levels). Project value: Provides solid application infrastructure for GLM models, helps individual developers and enterprises build AI applications and automated workflows, and serves as a high-quality reference implementation for the Chinese AI community.

1. Start with simple skills (e.g., glmocr, glmv-caption) to familiarize yourself with the process before exploring complex scenarios; 2. Pay attention to API quotas, evaluate costs before batch processing, and optimize requests; 3. Read the SKILL.md document in each skill directory to get detailed configurations and examples; 4. Participate in community contributions through GitHub Issues or Pull Requests to improve the skill ecosystem.