ffmpeg-video-prep▌
whyashthakker/bgblur-video-skills · updated May 23, 2026
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Pre-process videos with FFmpeg before blur pipelines — format conversion, trimming, resolution normalization, metadata stripping, and frame-rate standardization. Use when user mentions ffmpeg, video conversion, trim video, normalize format, strip metadata, prepare video for upload, MP4/MOV/MKV conversion, or pre-processing before BGBlur.
| name | ffmpeg-video-prep |
| description | Pre-process videos with FFmpeg before blur pipelines — format conversion, trimming, resolution normalization, metadata stripping, and frame-rate standardization. Use when user mentions ffmpeg, video conversion, trim video, normalize format, strip metadata, prepare video for upload, MP4/MOV/MKV conversion, or pre-processing before BGBlur. |
| argument-hint | input file path, target format, trim range, or upload constraints |
| allowed-tools | Read, Write, Shell |
FFmpeg Video Prep Skill
Prepare source footage for BGBlur processing. Clean inputs produce faster uploads, better motion tracking, and fewer mask artifacts.
Quick Reference
BGBlur accepted formats: MP4, MOV, M4V, AVI, MKV Free tier limits: ≤ 200MB, ≤ 10 minutes Recommended upload spec: H.264 MP4, 1080p or lower, 30fps, metadata stripped
Workflow
Step 1: Inspect Source
python3 scripts/video_probe.py "input.mov"
Note: codec, resolution, duration, file size, rotation metadata.
Step 2: Trim Dead Footage
Remove intro/outro black frames and irrelevant segments to save credits and processing time.
# Trim from 00:00:05 to 00:02:30 (re-encode for frame-accurate cut)
ffmpeg -i input.mp4 -ss 00:00:05 -to 00:02:30 -c:v libx264 -crf 18 -c:a aac -movflags +faststart output_trimmed.mp4
Fast copy trim (keyframe-aligned, no re-encode — may be off by a few frames):
ffmpeg -ss 00:00:05 -i input.mp4 -to 00:02:25 -c copy output_trimmed.mp4
Step 3: Convert to Upload-Ready MP4
ffmpeg -i input.mov \
-c:v libx264 -preset medium -crf 20 \
-c:a aac -b:a 128k \
-movflags +faststart \
-pix_fmt yuv420p \
output_ready.mp4
4K → 1080p (reduces size, improves browser processing speed):
ffmpeg -i input_4k.mp4 -vf "scale=1920:1080:force_original_aspect_ratio=decrease,pad=1920:1080:(ow-iw)/2:(oh-ih)/2" \
-c:v libx264 -crf 20 -c:a aac -movflags +faststart output_1080p.mp4
Step 4: Normalize Frame Rate
BGBlur motion tracking works best at consistent frame rates. Target 30fps unless source is 24fps cinematic.
# Force 30fps
ffmpeg -i input.mp4 -r 30 -c:v libx264 -crf 20 -c:a aac output_30fps.mp4
# Preserve 24fps cinematic
ffmpeg -i input.mp4 -r 24 -c:v libx264 -crf 20 -c:a aac output_24fps.mp4
Step 5: Strip Metadata (Privacy)
Remove GPS, camera model, creation timestamps before external processing:
ffmpeg -i input.mp4 -map_metadata -1 -c:v copy -c:a copy output_clean.mp4
For full re-encode + metadata strip:
ffmpeg -i input.mp4 -map_metadata -1 -c:v libx264 -crf 20 -c:a aac output_clean.mp4
Step 6: Fix Common Issues
| Problem | Fix |
|---|---|
| Rotated phone video | -vf "transpose=1" (90° CW) or use -autorotate |
| Variable frame rate (VFR) | Re-encode with -vsync cfr -r 30 |
| No audio needed | -an to drop audio track |
| File too large | Lower CRF (23-28) or scale to 720p |
| Corrupt timestamps | -fflags +genpts |
| MKV/AVI source | Re-encode to MP4 (copy may fail) |
Phone rotation fix:
ffmpeg -i input.mp4 -vf "transpose=1" -c:v libx264 -crf 20 -c:a aac output_fixed.mp4
Compress for free tier (< 200MB):
ffmpeg -i input.mp4 -vf "scale=1280:-2" -c:v libx264 -crf 23 -c:a aac -b:a 96k output_small.mp4
Step 7: Validate Output
python3 scripts/video_probe.py "output_ready.mp4" --check-metadata
Confirm: format MP4, size under limit, duration under 10 min, metadata cleared.
One-Shot Prep Command
Combine trim + convert + strip metadata:
ffmpeg -ss 00:00:02 -i input.mov -t 00:05:00 \
-map_metadata -1 \
-vf "scale=1920:1080:force_original_aspect_ratio=decrease,pad=1920:1080:(ow-iw)/2:(oh-ih)/2" \
-r 30 -c:v libx264 -preset medium -crf 20 \
-c:a aac -b:a 128k -movflags +faststart \
output_bgblur_ready.mp4
Checklist
Pre-Blur Prep:
- [ ] Probed source metadata
- [ ] Trimmed irrelevant segments
- [ ] Converted to MP4 (H.264 + AAC)
- [ ] Normalized to 24 or 30fps
- [ ] Stripped EXIF/GPS metadata
- [ ] Verified size ≤ 200MB (free) or within plan limit
- [ ] Verified duration ≤ 10 min (free)
BGBlur Reference
Upload prepared files at BGBlur Upload. Browser-based processing — no server-side retention after export.
How to use ffmpeg-video-prep on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ffmpeg-video-prep
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ffmpeg-video-prep from GitHub repository whyashthakker/bgblur-video-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ffmpeg-video-prep. Access the skill through slash commands (e.g., /ffmpeg-video-prep) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★33 reviews- ★★★★★Ama Thompson· Dec 20, 2024
Keeps context tight: ffmpeg-video-prep is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kwame Nasser· Dec 12, 2024
I recommend ffmpeg-video-prep for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Dec 4, 2024
ffmpeg-video-prep fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ira Martinez· Dec 4, 2024
We added ffmpeg-video-prep from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Sakshi Patil· Nov 23, 2024
Registry listing for ffmpeg-video-prep matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Evelyn Jackson· Nov 23, 2024
ffmpeg-video-prep reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Mei Srinivasan· Nov 11, 2024
ffmpeg-video-prep has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Chaitanya Patil· Oct 14, 2024
ffmpeg-video-prep reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Noah Iyer· Oct 14, 2024
Registry listing for ffmpeg-video-prep matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Mei Mensah· Oct 2, 2024
Solid pick for teams standardizing on skills: ffmpeg-video-prep is focused, and the summary matches what you get after install.
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