clawlite-video-content-engine（Cavallite 视频内容引擎）

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:

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

1. source note
2. beginner translation
3. short-video script
4. X thread
5. LinkedIn/Facebook post
6. short blog summary
7. 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.

Read next when needed

• references/source-note-template.md
• references/jk-marketing-asset-template.md
• references/angle-framework.md
• references/short-video-template.md
• references/content-pack-template.md
• references/asset-promotion-guide.md
• references/compliance-and-positioning.md
• references/notebooklm-automation-guide.md