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sdt-benchmark

从公开内容中筛选真正值得学习、能够迁移的对标样本,并排除名人效应、粉丝体量和独特资源造成的假象。用户要找低粉爆款、垂直对标、邻近受众样本或跨赛道灵感时使用。

English: Find public benchmark content that is genuinely learnable and transferable while filtering out celebrity effects, follower-size bias, and unique-resource noise. Use for low-follower breakout posts, niche benchmarks, adjacent audiences, or cross-category inspiration.

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See how to install

Files included (2)

  • SKILL.md2.5 KB
  • agents/openai.yaml251 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

SDT 对标筛选

同时检查“是否看懂、是否可模仿、主体差异是否构成噪音”和单条内容的相对爆发证据。账号商业对标与单条内容对标使用不同标准,不把粉丝多等同于值得模仿。

五项过滤

  1. 证据:链接、作者、时间和指标是否可核对。
  2. 相对表现:相对作者自身近期基线是否异常。
  3. 主体差异:明星、新闻、独家资源或投流是否承担了主要结果。
  4. 机制可懂:能否说清哪种情绪、信息或叙事结构在起作用。
  5. 执行可仿:目标账号能否用真实资源重建机制,而不是复制表面。

三条来源线

  • 垂直:同品类、同场景或同主题。
  • 邻近:受众处境、人物身份或消费动机相似。
  • 跨域:主题不同,但承重情绪和传播结构可迁移。

完整选题研究默认收集 6 条垂直、6 条邻近、8 条跨域候选;数据不足时如实缩减。

低粉爆款判定

优先使用:

viral_multiplier = note_likes / creator_recent_median_likes

正式对标视频先过绝对门槛:可见点赞数必须大于等于 3,000。点赞数小于 3,000、点赞不可见的视频不得进入正式对标池,最多标记为“灵感样本”。

通过绝对门槛后,主样本建议同时满足:

  • 发布时间在约定范围内
  • viral_multiplier 不低于 3
  • 作者粉丝约为目标账号的 0.3–3 倍
  • 收藏、评论或分享代理信号至少一项异常
  • 承重机制可以在目标账号的真实资源内重建

跨域样本可放宽粉丝带,但必须提高迁移性审查。明星、重大新闻、昂贵独家资源、一次性奇观、不可核实结果和纯投流样本降级或剔除。

输出

逐条标记:

  • 来源线
  • 数据完整度
  • 相对爆发证据
  • 主体差异
  • 可复制元素
  • 不可复制元素
  • 是否进入 sdt-spread

不要因为设置了 3,000 点赞硬门槛就只按点赞绝对值排序;过门槛后仍按相对爆发、主体差异和可迁移性筛选。

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