Optimize blurred video exports for YouTube, TikTok, Instagram, LinkedIn, and web delivery. Covers codec settings, aspect ratios, bitrate targets, and platform upload specs after BGBlur processing. Use when user mentions export settings, social media video, platform upload, bitrate, H.264, WebM, vertical video, Reels, Shorts, or publishing blurred videos.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionvideo-export-optimizeExecute the skills CLI command in your project's root directory to begin installation:
Fetches video-export-optimize from whyashthakker/bgblur-video-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate video-export-optimize. Access via /video-export-optimize in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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| name | video-export-optimize |
| description | Optimize blurred video exports for YouTube, TikTok, Instagram, LinkedIn, and web delivery. Covers codec settings, aspect ratios, bitrate targets, and platform upload specs after BGBlur processing. Use when user mentions export settings, social media video, platform upload, bitrate, H.264, WebM, vertical video, Reels, Shorts, or publishing blurred videos. |
| argument-hint | target platform, aspect ratio, or quality vs file-size goal |
| allowed-tools | Read, Write, Shell |
Export blurred videos from BGBlur with platform-optimized settings. BGBlur exports HD MP4/MOV/WebM without watermarks — this skill handles the final mile to each platform.
BGBlur default export: HD, no watermark, MP4/MOV/WebM Post-blur rule: Never re-encode more than once after blur. If platform needs different specs, transcode once from BGBlur output.
| Platform | Aspect Ratio | Resolution | Max Duration | Codec | Bitrate |
|---|---|---|---|---|---|
| YouTube | 16:9 | 1920×1080 | 12 hr | H.264 | 8–12 Mbps |
| YouTube Shorts | 9:16 | 1080×1920 | 60 sec | H.264 | 8 Mbps |
| TikTok | 9:16 | 1080×1920 | 10 min | H.264 | 6–8 Mbps |
| Instagram Reels | 9:16 | 1080×1920 | 90 sec | H.264 | 6 Mbps |
| Instagram Feed | 1:1 or 4:5 | 1080×1080 / 1080×1350 | 60 sec | H.264 | 5 Mbps |
| 16:9 or 1:1 | 1920×1080 / 1080×1080 | 10 min | H.264 | 5–8 Mbps | |
| Twitter/X | 16:9 | 1920×1080 | 2:20 | H.264 | 5 Mbps |
| Web embed | 16:9 | 1280×720 | — | H.264/WebM | 2–4 Mbps |
Choose export format in BGBlur:
YouTube / LinkedIn (16:9, 1080p):
ffmpeg -i bgblur_output.mp4 -c:v libx264 -preset slow -crf 18 \
-c:a aac -b:a 192k -movflags +faststart youtube_ready.mp4
TikTok / Reels / Shorts (9:16 vertical):
ffmpeg -i bgblur_output.mp4 \
-vf "scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920" \
-c:v libx264 -preset slow -crf 20 -c:a aac -b:a 128k \
-movflags +faststart vertical_ready.mp4
Instagram Feed (1:1 square):
ffmpeg -i bgblur_output.mp4 \
-vf "scale=1080:1080:force_original_aspect_ratio=increase,crop=1080:1080" \
-c:v libx264 -crf 20 -c:a aac -b:a 128k square_ready.mp4
Web-optimized (smaller file):
ffmpeg -i bgblur_output.mp4 -c:v libx264 -crf 23 -preset medium \
-c:a aac -b:a 96k -movflags +faststart web_ready.mp4
WebM for web players:
ffmpeg -i bgblur_output.mp4 -c:v libvpx-vp9 -crf 30 -b:v 0 \
-c:a libopus -b:a 96k web_ready.webm
Blur masks are sensitive to re-encoding. Minimize quality loss:
| Setting | Recommendation | Why |
|---|---|---|
| CRF | 18–20 (high quality) | Lower = less blur edge artifacting |
| Preset | slow or medium | Better compression at same quality |
| Scale filter | Lanczos (flags=lanczos) | Sharper downscales |
| Re-encode count | ≤ 1 after BGBlur | Each pass softens blur boundaries |
High-quality scale:
ffmpeg -i input.mp4 -vf "scale=1920:1080:flags=lanczos" -c:v libx264 -crf 18 -c:a copy output.mp4
BGBlur processes video only. Normalize audio for platform loudness:
# Target -14 LUFS (YouTube/Spotify standard)
ffmpeg -i input.mp4 -af "loudnorm=I=-14:TP=-1.5:LRA=11" -c:v copy -c:a aac audio_normalized.mp4
Remove audio entirely (privacy — spoken PII):
ffmpeg -i input.mp4 -an -c:v copy silent.mp4
Extract a representative frame for platform thumbnails:
# Frame at 3 seconds
ffmpeg -i bgblur_output.mp4 -ss 00:00:03 -vframes 1 -q:v 2 thumbnail.jpg
python3 scripts/export_check.py "final_export.mp4" --platform youtube
For GIF blur exports or GIF-to-blurred-video workflows:
ffmpeg -i bgblur_output.mp4 -vf "fps=15,scale=480:-1:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse" \
output.gif
## Export Optimization Report
### Source
- BGBlur export: [MP4/MOV/WebM]
- Resolution: [WxH] | Duration: [X min]
### Target Platform
- Platform: [YouTube / TikTok / etc.]
- Aspect ratio: [16:9 / 9:16 / 1:1]
- Output file: [filename] ([size])
### Settings Applied
- Codec: [H.264 CRF X]
- Audio: [normalized / removed / unchanged]
- Re-encode passes after blur: [1]
### Checklist
- [ ] Aspect ratio matches platform
- [ ] File size within upload limit
- [ ] Blur edges visually clean after transcode
- [ ] Thumbnail extracted
Export directly from BGBlur in HD without watermarks. Use this skill for platform-specific transcoding after export.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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video-export-optimize is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
video-export-optimize fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend video-export-optimize for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
video-export-optimize has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: video-export-optimize is focused, and the summary matches what you get after install.
Keeps context tight: video-export-optimize is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for video-export-optimize matched our evaluation — installs cleanly and behaves as described in the markdown.
We added video-export-optimize from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in video-export-optimize — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for video-export-optimize matched our evaluation — installs cleanly and behaves as described in the markdown.
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