clawlite-video-content-engine

中文：将 YouTube 视频转化为 ClawLite 营销资产的内容引擎，覆盖摘要、短视频脚本、X thread 与博客输出，支持知识型内容复用与渠道化分发。日本語：YouTube動画をClawLite向け教育・マーケ配信資産へ再構成。要約、短尺動画、Xスレッド、ブログ出力まで一貫して最適化。 한국어：YouTube 영상을 ClawLite 마케팅 자산으로 재가공하는 콘텐츠 엔진. 요약, 숏폼, X 스레드, 블로그 산출물을 일괄 생성해 채널 확장을 지원. Español：Convierte videos de YouTube en activos de marketing para ClawLite: resumen, guion short-form, hilos de X y posts de blog para reutilización escalable del contenido.

X-RayLuan@x-rayluan

Install

openclaw skills install @x-rayluan/clawlite-video-content-engine

ClawLite Video Content Engine

Use this skill to convert third-party educational videos into ClawLite-compatible educational marketing content.

Core principle:

do not treat source videos as raw material for plagiarism or blind reposting
treat source videos as learning inputs that become:
- beginner summaries
- practical takeaways
- explainer shorts
- X threads
- LinkedIn/Facebook posts
- short blog summaries
- soft ClawLite bridge content

Outcome

Turn one source video into a content pack:

1 source summary
1 beginner translation
1 short-form video script
1 X thread
1 LinkedIn/Facebook post
1 short blog summary
1 CTA bridge to ClawLite

Output location rule

Write outputs to a stable folder so the workflow is reusable and auditable.

Recommended structure:

text

video-content/
  <videoId>/
    raw-transcript.md
    notebooklm-summary.md
    jk-marketing-asset.md
    source-note.md
    short-video-script.md
    x-thread.md
    linkedin-post.md
    blog-summary.md
    metadata.json

At minimum, write:

notebooklm-summary.md
jk-marketing-asset.md
source-note.md
short-video-script.md
x-thread.md
blog-summary.md
metadata.json

Workflow

Normalization rule

NotebookLM output is not the final downstream input. It must be normalized into a JK / marketing-assets layer before Elon, Tony, or Jenny consume it.

Use this chain:

YouTube / transcript source
raw extraction layer (for example yt-dlp)
NotebookLM understanding layer
JK marketing asset layer
Elon / Tony / Jenny execution outputs

1. Capture the source video context

Record:

title
creator
URL
publish date if useful
duration
main topic
likely beginner pain point

If NotebookLM is available, use it for transcript + summary extraction. If NotebookLM is unavailable, create the structure manually from transcript/notes.

When using NotebookLM UI automation:

use a screenshot-first workflow
verify the exact input field before typing
avoid generic textarea selectors
confirm source creation before moving to content generation

Read references/notebooklm-automation-guide.md before automating NotebookLM.

2. Build a source note

Create a structured source note with:

what the video is about
3 key takeaways
strongest quote or idea
why it matters for beginners
where setup friction appears
how ClawLite naturally bridges the gap

Read references/source-note-template.md when building the note.

3. Normalize into JK marketing assets

Convert the source + NotebookLM understanding into a reusable asset note for downstream lanes.

The JK asset should include:

source context
pain point
beginner misunderstanding
3 key takeaways
strongest idea / quote
angle candidates
hook candidates
ClawLite bridge
Elon social angle
Tony blog angle
Jenny lifecycle angle
source / proof lines

This asset layer should become the shared substrate for downstream content generation.

Read references/jk-marketing-asset-template.md when building this layer.

4. Translate the source into ClawLite angles

Do not simply restate the creator video. Create one or more of these angles:

beginner translation
practical summary
“what matters most” summary
“3 takeaways” summary
“too long, didn’t watch” summary
setup-friction reframing

Read references/angle-framework.md when choosing the angle.

5. Create the short-video script

Write a 30–90 second short video script with:

hook
2–3 insights
beginner framing
soft ClawLite bridge
CTA

Prefer:

educational tone
real user pain
concise and clear subtitles
no hard sell in the first half

Read references/short-video-template.md when writing the script.

6. Expand into a multi-channel content pack

Derive from the same source note and JK marketing asset:

X thread
LinkedIn/Facebook post
short blog summary
optional newsletter blurb

Read references/content-pack-template.md for the output structure.

7. Promote inbox assets into formal marketing-assets

Do not leave all value trapped in a one-off source folder. After building the JK asset, normalize reusable pieces into the shared marketing-assets layer.

Typical destinations:

pain points → 02-pain-points/
hooks → 01-hooks/
angles → 06-angles/
proof/source lines → 03-proof-points/
CTA lines → 07-cta/

Rule:

inbox/source asset = working note
marketing-assets = durable shared substrate

At minimum, extract from the JK asset:

reusable pain lines
reusable hooks
reusable angle lines
source-backed proof lines

Read references/asset-promotion-guide.md before promoting shared assets.

8. Keep the content compliant

Always:

attribute the source creator/video
add original explanation and framing
avoid copying long transcript passages
avoid heavy reuse of original video/audio
keep the result in commentary/education territory, not mirror-reposting

Read references/compliance-and-positioning.md before finalizing publishable outputs.

ClawLite bridge rules

Use soft bridges such as:

“The concept is powerful. The usual blocker is setup friction.”
“If you want to try this without the setup pain, start with ClawLite.”
“This is the idea. ClawLite makes the first step easier.”

Avoid:

overclaiming
hijacking the creator’s work into a hard product ad
turning every summary into aggressive CTA spam

Recommended output order

source note
beginner translation
short-video script
X thread
LinkedIn/Facebook post
short blog summary
ClawLite CTA bridge

Example use case

If given a source video like https://www.youtube.com/watch?v=fd4k16REDOU, produce:

a summary note
3 key beginner takeaways
a 45-second short script
a ClawLite bridge angle
a thread/post/blog content pack

NotebookLM automation layer

Use NotebookLM as the ingestion layer, not the final content layer. Its job is to help extract:

transcript understanding
summaries
section structure
notes and source context

Your real output should still be a ClawLite content pack.

When automating NotebookLM:

screenshot before every action
verify the modal/input target before typing
avoid the sidebar search textarea
re-dispatch input/change events when UI state does not update
verify that the source was actually added before continuing

Read references/notebooklm-automation-guide.md before doing any NotebookLM UI automation.