aaaaqwq/content-factory-pipeline
skills/content-factory-pipeline/SKILL.md
内容工厂流水线：从选题确认、实时调研、写作、审核到分发，形成数据驱动的内容生产闭环。
$ install --global
skillsauthnpx skillsauth add aaaaqwq/agi-super-team content-factory-pipelineInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
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SemgrepStatic code analysis for vulnerabilities
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Last scanned: Apr 1, 2026, 4:30 PM60.8s1 file scanned
SKILL.md
- name:
- content-factory-pipeline
- description:
- 内容工厂流水线：从选题确认、实时调研、写作、审核到分发，形成数据驱动的内容生产闭环。
内容工厂流水线 v2 — 数据驱动 + 去AI味
流程（5步闭环）
Step 1: 选题确认
CEO 确认选题 + 目标平台 + 调性要求
Step 2: 实时数据调研（小research）
必须使用搜索工具获取实时数据，禁止凭记忆编造。
调研流程：
- 用
tavily/web-search/deep-researchskill 搜索最新信息 - 搜索维度：
- 最新产品/工具动态（如 Cursor 0.50, Claude Code, OpenClaw, Gemini 2.5）
- 真实数据和统计（Stack Overflow Survey, GitHub Copilot 报告等）
- 真实事件/新闻（带来源链接）
- 社交媒体热议观点（Twitter/X, Reddit, 知乎）
- 每条素材必须标注来源URL
- 输出：
research_v2.md（带来源的结构化素材）
Step 3: 内容创作（小content）
基于 research_v2.md 写作，要求：
- 读
humanize-zhskill 的去AI味方法论 - 案例必须来自 research_v2.md 的真实素材
- 数据必须有出处
- 禁止使用：赋能、助力、值得一提、不言而喻、综上所述
- 禁止"姐妹们""宝子们"等过度套路化称呼
- 观点锋利，有争议性
Step 4: CEO 审核
- AI味检查（自然度）
- 信息新鲜度（是否用了最新数据）
- 来源可信度
- 不通过 → 反馈给小content修改
Step 5: 排版发布（小content / CEO）
- 用
xiaohongshu-publishskill 通过 browser 发布 - 或导出排版后人工发布
关键原则
- 无搜索不动笔 — 调研必须用搜索工具，不许凭记忆
- 无来源不引用 — 每个数据点和案例都要有出处
- 去AI味是硬指标 — 过不了自检不许提交
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