by dongbeixiaohuo · Claude Skill · ★ 189
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写稿Agent v0.6.4 🚀 一个基于 Claude Code Skills + Subagents 的全栈写作系统。 不仅是写作,更是打磨: 🤖 反AI味写作:从选题到初稿,源头遏制 AI 腔调。 🧬 深度 Humanizer:注入人类观点、细节与灵魂,彻底去除机器味。 🎨 文章配图师:自动设计视觉风格,生成并植入高质量配图。 📺 真实读者模拟:模拟真实用户的"心理弹幕"与"朋友圈转发",只为了检验传播力。 支持 DeepSeek / 智谱GLM / MiniMax 等多种国产大模型,兼容 OpenAI/Gemini 接口,成本极低(使用包月套餐几可忽略不计)。 从选题生成、风格建模、写作执行到发布评审与配图,提供完整的 AI 写作工作流。 🎯 项目简介 写稿Agent 是一个协作式写作工作流系统,通过强制性的模式选择、需求澄清、风格建模、素材调研和主编审稿,帮助你写出不像AI生成的高质量文章。 v0.6.4 技能结构优化 ⭐ New 🔧 Progressive Disclosure 架构升级:应用 Skill Creator 最佳实践,大幅提升技能加载效率。 📉 Token 使用优化:公众号文章获取技能从 1238 行精简至 200 行,Token 消耗减少 85%。 📚 文档结构化:核心流程保留在 SKILL.md,详细说明拆分到 references 目录,按需加载。 📖 调用示例增强:工作流导演新增完整的 Agent 工具调用示例,提升可执行性。 v0.6.3 去AI味专家进阶升级 ⚖️ 50分制质量自评:加入严苛输出把控,强迫 AI 根据五大维度自评,低于 40 分内部打回重写。 🚫 致命黑名单词库:精准打击“此外”、“至关重要”、“织锦”、“格局”等典型机器生成的“塑料词汇”。 ⚡ 快速排雷自检 (Quick Check):强制打断 AI 常见的“三段式强迫症”、“等长句式”和“无聊排比”。 ❤️ 全新注入灵魂指令:通过引入具体生活细节、强加第一人称时局感、甚至刻意的逻辑混乱,赋予文本真正的强人设观感。 v0.6.0 去AI味与真实模拟 🤖 Humanizer 去
| Stars | 189 |
| Forks | 47 |
| Language | JavaScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 47.71/100 |
| Open Issues | 3 |
| Last Updated | 2026-04-25 |
| Created | 2025-12-21 |
| Platforms | claude-code, node |
| Est. Tokens | ~394k |
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writing-agent is 🚀 一个基于 Claude Code (Skills + Subagents) 的“去AI味”全栈写作系统。不仅防套路,更通过专属规则强制注入人类观点与细节,搭配读者测试评估与自动图文排版。全面支持 DeepSeek / 智谱GLM / MiniMax 等国产低成本大模型,提供从选题、风格建模到审稿发布的高维全自动写作工作流。. It is categorized as a Claude Skill with 189 GitHub stars.
writing-agent is primarily written in JavaScript. It covers topics such as ai-writing, claude-code, content-generation.
You can find installation instructions and usage details in the writing-agent GitHub repository at github.com/dongbeixiaohuo/writing-agent. The project has 189 stars and 47 forks, indicating an active community.
writing-agent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to writing-agent on Agent Skills Hub include oh-story-claudecode, shuorenhua, ai-collab-playbook. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.