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： 1. 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 2. 你更想查的是： - 前 3 秒掉点 - 中段流失 - CTA 前流失 - 完播率偏低 3. 你们平时有没有自己的 retention 分段逻辑？ 4. 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

1. Clarify the available evidence and diagnosis goal.
2. Segment the video structure.
3. Identify likely drop moments.
4. Diagnose root causes.
5. Recommend practical fixes.
6. Provide next-version structure.
7. If structured data exists, return Python analysis script.

Output format

1. Drop diagnosis map
2. Cause list
3. Fix actions
4. Next script skeleton
5. 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.