video-recap-skills
Reduces manual video documentation time by automatically generating narrated recaps from raw footage, enabling teams to create training or knowledge-sharing …
Claude Code skill that turns any video into a narrated recap: it edits the footage into a Chinese-language explainer video and supports export to Jianying (CapCut) editing projects.
- Generate automated narration summaries from recorded technical walkthrough videos
- Create Chinese-language recap videos from exported video editing project files
- Extract key technical concepts from recorded meetings into concise narrated summaries
Reduces manual video documentation time by automatically generating narrated recaps from raw footage, enabling teams to create training or knowledge-sharing content at scale without dedicated video editing resources.
Development teams documenting technical processes, tutorials, or onboarding materials who need rapid video-to-narrative conversion.
https://github.com/worldwonderer/video-recap-skills
By worldwonderer
How to Get It
claude plugins install worldwonderer/video-recap-skills
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me generate automated narration summaries from recorded technical walkthrough videos
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 270 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 270 GitHub stars; contributors unknown; last commit 0d ago; license MIT.