glm-master-skillDocumentation-only master skill for GLM ecosystem discovery and installation. This skill does not execute scripts or subprocess commands. It provides a curat...
Install via ClawdBot CLI:
clawdbot install jaredforreal/glm-master-skillGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://github.com/zai-org/GLM-5/tree/main/skills/glm-master-skillAudited Apr 16, 2026 · audit v1.0
Generated Apr 11, 2026
Researchers can use GLM-OCR skills to extract text, tables, and formulas from scanned papers, and GLM-V skills to convert PDFs into presentations or websites for sharing findings. This streamlines literature review and publication preparation.
Companies can leverage GLM-V skills for resume screening, document-based content generation, and PDF-to-web conversions to automate HR and marketing workflows. This reduces manual effort in processing resumes and creating promotional materials.
Designers and marketers can use GLM-Image for text-to-image generation and GLM-V for prompt generation from visuals to create graphics and ad copy. This accelerates content creation for social media and advertising campaigns.
Individuals or educational institutions can apply GLM-OCR handwriting extraction to convert handwritten notes, forms, or historical documents into editable digital text. This aids in archiving and accessibility of handwritten materials.
Analysts can utilize GLM-V grounding skills to identify and visualize objects in images or videos for applications like inventory management or surveillance. This supports tasks requiring target detection and bounding-box annotations.
Offer access to GLM skills through a cloud-based platform with tiered pricing based on usage volume, such as API calls or processing time. This model targets businesses needing scalable AI tools without infrastructure investment.
Provide basic OCR and image generation for free, while charging for advanced features like table extraction, high-resolution outputs, or priority support. This attracts a broad user base and converts heavy users to paid plans.
Sell customized bundles of GLM skills with dedicated support, integration services, and on-premise deployment options to large organizations. This caters to industries with strict data privacy or high-volume processing needs.
💬 Integration Tip
Start by installing skills via Clawhub for simplicity, and set the ZHIPU_API_KEY environment variable to enable most functionalities across the ecosystem.
Scored Apr 19, 2026
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Humanize AI-generated text to bypass detection. This humanizer rewrites ChatGPT, Claude, and GPT content to sound natural and pass AI detectors like GPTZero,...
Humanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 24 pattern detectors, 500+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
去除文本中的 AI 生成痕迹。适用于编辑或审阅文本,使其听起来更自然、更像人类书写。 基于维基百科的"AI 写作特征"综合指南。检测并修复以下模式:夸大的象征意义、 宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、 AI 词汇、否定式排比、过多的连接性短语。
Collaborative thinking partner for exploring complex problems through questioning
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.