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extracting-structure

三个确定性提取器,从已建立索引的视频中输出带时间戳的章节列表、含时间点与画面帧的 Bug 报告,以及按注意力触发、语速、画面变化率和屏上文字四维打分的开场钩子分析;无错误信号时如实返回未发现。

oxbshw
热度 0.00
最近更新 2026-10-19 04:17:49
分类:效率依赖:代码执行 / 读写文件应用场景:摘要总结 / 报告撰写 / 调试排错 / 社交媒体

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add oxbshw/watch-skill/extracting-structure
Metadata
name
extracting-structure
version
1.4.3
description
The user wants structure pulled out of a watched video — "make chapters for this video", "where does the bug appear in this recording", "turn this screen recording into a bug report", "how strong is my intro/hook". Use this for deterministic extraction from the index — chapters with timestamps, a fileable bug report with the exact frame, or a scored hook analysis.
license
MIT
user-invocable
true
allowed-tools
Bash, Read

Extracting structure

Three extractors turn an already-watched video into something structured. All of them answer straight from the persistent index — deterministic, no extra model calls. The video must be watched first (see watching-videos).

Chapters

watch-skill extract chapters <video_id-or-source>

Titled chapters with start/end timestamps, built from scene changes plus transcript topic shifts. Good for long tutorials, lectures, meetings — give the user the chapter list with timestamps, not a prose summary.

Bug report from a screen recording

watch-skill extract bug-report <video_id-or-source>

Pinpoints where an error appears: timestamp, the exact frame, the on-screen error text as OCR read it, and the steps that led up to it (from the transcript/actions preceding the failure). Returns found: false honestly when no error signal exists — report that as "no error found in the recording", not as an error.

Paste the output into the user's issue tracker format when they ask for a fileable report; the frame path is the attachment.

Hook analysis (creators)

watch-skill extract hook <video_id-or-source> [--seconds 15]

Scores the opening seconds on four measured axes — attention trigger in the opening line, speech pacing, visual change rate, on-screen text — each with an actionable critique, plus a 0–100 score and verdict. Give the user the per-axis critiques; the number alone helps nobody.