技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
extracting-structure【数据与研究】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 …
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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).
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.
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.
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.