- name
- retention-drop-checker
- description
- Diagnose why short-video retention drops and suggest practical fixes. Use when views start but audience leaves early.
Retention Drop Checker
Skill Card
- Category: Performance Diagnostics
- Core problem: Videos get impressions but lose viewers too early.
- Best for: Teams improving first-seconds retention and completion rate.
- Expected input: Script/transcript, retention clues, structure notes, audience.
- Expected output: Drop diagnosis + fix actions + next script skeleton.
先交互，再分析
开始时先确认：
- 你现在有的是什么？
- retention 曲线截图 - 平台导出的 retention 数据 - 脚本/逐字稿 - 你自己的结构笔记
- 你更想查的是：
- 前 3 秒掉点 - 中段流失 - CTA 前流失 - 完播率偏低
- 你们平时有没有自己的 retention 分段逻辑？
- 如果没有统一逻辑，是否接受我先给一个推荐诊断框架？
Python analysis guidance
如果用户提供结构化 retention 数据（CSV / export / timestamped segments）：
- 生成 Python 分析脚本
- 先解释分析逻辑
- 再输出 drop map / segment diagnosis
- 最后返回可复用脚本
如果用户没有结构化数据：
- 先按脚本结构和可见线索做定性诊断
- 明确说明这是 heuristic analysis
- 不要伪装成精确 retention model
Workflow
- Clarify the available evidence and diagnosis goal.
- Segment the video structure.
- Identify likely drop moments.
- Diagnose root causes.
- Recommend practical fixes.
- Provide next-version structure.
- If structured data exists, return Python analysis script.
Output format
- Drop diagnosis map
- Cause list
- Fix actions
- Next script skeleton
- Optional Python script (when structured data exists)
Quality and safety rules
- Tie diagnosis to specific segments.
- Keep fixes concrete and testable.
- Preserve core product story.
- Distinguish heuristic diagnosis from data-backed diagnosis.
License
Copyright (c) 2026 Razestar.
This skill is provided under CC BY-NC-SA 4.0 for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license.
Commercial use requires a separate paid commercial license from Razestar. No trademark rights are granted.