Video Processing & Editing

Expert in FFmpeg-based video editing, processing automation, and export optimization for modern content creation workflows.

When to Use

✅ Use for:

• Automated video editing pipelines (script-to-video)
• Cutting, trimming, concatenating clips
• Adding transitions, effects, overlays
• Audio mixing and normalization
• Subtitle/caption handling
• Export optimization for platforms
• Batch video processing
• Color grading and correction

❌ NOT for:

• Real-time video editing UI (use DaVinci Resolve/Premiere)
• 3D compositing (use After Effects/Blender)
• Motion graphics animation (use After Effects)
• Basic screen recording (use OBS)

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Technology Selection

Video Editing Tools

Tool	Speed	Features	Use Case
FFmpeg	Very Fast	CLI automation	Production pipelines
MoviePy	Medium	Python API	Programmatic editing
PyAV	Fast	Low-level control	Custom processing
DaVinci Resolve	Slow	Full NLE	Manual editing

Decision tree:

Need automation? → FFmpeg
Need Python API? → MoviePy
Need frame-level control? → PyAV
Need manual editing? → DaVinci Resolve

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Common Anti-Patterns

Anti-Pattern 1: Not Using Keyframe-Aligned Cuts

Novice thinking: "Just cut the video at any timestamp"

Problem: Causes artifacts, black frames, and playback issues.

Wrong approach:

# ❌ Cut at arbitrary timestamp (not keyframe-aligned)
ffmpeg -i input.mp4 -ss 00:01:23.456 -to 00:02:45.678 -c copy output.mp4

# Result: Black frames, artifacts, sync issues

Why wrong:

• Video codecs use keyframes (I-frames) every 2-10 seconds
• Non-keyframe cuts require re-encoding
• Using -c copy (stream copy) without keyframe alignment breaks playback
• GOP (Group of Pictures) structure depends on keyframes

Correct approach 1: Re-encode for precise cuts

# ✅ Re-encode for frame-accurate cutting
ffmpeg -i input.mp4 -ss 00:01:23.456 -to 00:02:45.678 \
  -c:v libx264 -crf 18 -preset medium \
  -c:a aac -b:a 192k \
  output.mp4

# Frame-accurate, but slower (re-encoding)

Correct approach 2: Keyframe-aligned stream copy

# ✅ Fast cutting with keyframe alignment
# Step 1: Find keyframes near cut points
ffprobe -select_streams v -show_frames -show_entries frame=pkt_pts_time,key_frame \
  -of csv input.mp4 | grep ",1$" | awk -F',' '{print $2}'

# Step 2: Cut at nearest keyframes (fast, no re-encoding)
ffmpeg -i input.mp4 -ss 00:01:22.000 -to 00:02:46.000 -c copy output.mp4

# Blazing fast, no quality loss, but not frame-accurate

Correct approach 3: Two-pass for best of both worlds

# ✅ Fast seek + precise cut
ffmpeg -ss 00:01:20.000 -i input.mp4 \
  -ss 00:00:03.456 -to 00:01:25.678 \
  -c:v libx264 -crf 18 -preset medium \
  -c:a aac -b:a 192k \
  output.mp4

# -ss BEFORE -i: Fast seek to keyframe (no decode)
# -ss AFTER -i: Precise trim (only decode needed portion)

Performance comparison:

Method	Time (1-hour video)	Accuracy	Quality
Stream copy (arbitrary)	2s	❌ Broken	❌ Artifacts
Stream copy (keyframe)	2s	±2s	✅ Perfect
Re-encode (simple)	15min	✅ Frame	⚠️ Quality loss
Two-pass (optimal)	3min	✅ Frame	✅ Perfect

Timeline context:

• 2010: FFmpeg required full re-encoding for cuts
• 2015: -c copy added for stream copying
• 2020: Two-pass cutting became best practice
• 2024: Hardware acceleration (NVENC) makes re-encoding viable

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Anti-Pattern 2: Re-encoding Unnecessarily

Novice thinking: "Apply all edits in one FFmpeg command"

Problem: Multiple re-encodings cause cumulative quality loss.

Wrong approach:

# ❌ Re-encode for each operation (quality degradation)
# Operation 1: Trim
ffmpeg -i input.mp4 -ss 00:01:00 -to 00:05:00 \
  -c:v libx264 -crf 23 temp1.mp4

# Operation 2: Add audio
ffmpeg -i temp1.mp4 -i audio.mp3 -c:v libx264 -crf 23 \
  -map 0:v -map 1:a temp2.mp4

# Operation 3: Add subtitles
ffmpeg -i temp2.mp4 -vf subtitles=subs.srt \
  -c:v libx264 -crf 23 output.mp4

# Result: 3x re-encoding = significant quality loss

Why wrong:

• Each re-encode is lossy (even with high CRF)
• Cumulative quality loss (generation loss)
• 3x encoding time
• Wasted disk I/O

Correct approach 1: Chain operations in single command

# ✅ Single-pass encoding with all operations
ffmpeg -ss 00:01:00 -i input.mp4 -i audio.mp3 \
  -to 00:04:00 \
  -vf "subtitles=subs.srt" \
  -map 0:v -map 1:a \
  -c:v libx264 -crf 18 -preset medium \
  -c:a aac -b:a 192k \
  output.mp4

# Single re-encode, all operations applied at once

Correct approach 2: Use stream copy when possible

# ✅ Lossless operations with stream copy
# Trim (stream copy)
ffmpeg -i input.mp4 -ss 00:01:00 -to 00:05:00 -c copy temp.mp4

# Add audio (stream copy video, encode audio)
ffmpeg -i temp.mp4 -i audio.mp3 \
  -map 0:v -map 1:a \
  -c:v copy -c:a aac -b:a 192k \
  temp2.mp4

# Burn subtitles (must re-encode video)
ffmpeg -i temp2.mp4 -vf subtitles=subs.srt \
  -c:v libx264 -crf 18 -preset medium \
  -c:a copy \
  output.mp4

# Only 1 video re-encode (for subtitles)

Quality comparison:

Method	Encoding Passes	Quality (VMAF)	Time
3x re-encode (CRF 23)	3	82/100	45min
Single pass (CRF 23)	1	91/100	15min
Stream copy + 1 encode	1	95/100	18min
All stream copy	0	100/100	30s

---

Anti-Pattern 3: Ignoring Color Space Conversions

Novice thinking: "Just concatenate videos together"

Problem: Color shifts, mismatched brightness, broken playback.

Wrong approach:

# ❌ Concatenate videos with different color spaces
# clip1.mp4: BT.709 (HD), yuv420p
# clip2.mp4: BT.601 (SD), yuvj420p (full range)
# clip3.mp4: BT.2020 (HDR), yuv420p10le

# Create concat list
echo "file 'clip1.mp4'" > list.txt
echo "file 'clip2.mp4'" >> list.txt
echo "file 'clip3.mp4'" >> list.txt

# Concatenate without color normalization
ffmpeg -f concat -safe 0 -i list.txt -c copy output.mp4

# Result: Color shifts between clips, broken HDR metadata

Why wrong:

• Different color spaces (BT.601 vs BT.709 vs BT.2020)
• Different pixel formats (yuv420p vs yuvj420p)
• Different color ranges (limited vs full)
• Metadata conflicts

Correct approach:

# ✅ Normalize color space before concatenation

# Step 1: Analyze color space of each clip
ffprobe -v error -select_streams v:0 \
  -show_entries stream=color_space,color_transfer,color_primaries,pix_fmt \
  -of default=noprint_wrappers=1 clip1.mp4

# Step 2: Normalize all clips to common color space
# Target: BT.709 (HD), yuv420p, limited range

# Normalize clip1 (already BT.709)
ffmpeg -i clip1.mp4 -c copy clip1_normalized.mp4

# Normalize clip2 (BT.601 SD → BT.709 HD)
ffmpeg -i clip2.mp4 \
  -vf "scale=in_range=full:out_range=limited,colorspace=bt709:iall=bt601:fast=1" \
  -color_primaries bt709 \
  -color_trc bt709 \
  -colorspace bt709 \
  -c:v libx264 -crf 18 -preset medium \
  -c:a copy \
  clip2_normalized.mp4

# Normalize clip3 (BT.2020 HDR → BT.709 SDR)
ffmpeg -i clip3.mp4 \
  -vf "zscale=t=linear:npl=100,format=gbrpf32le,zscale=p=bt709,tonemap=hable:desat=0,zscale=t=bt709:m=bt709:r=limited,format=yuv420p" \
  -color_primaries bt709 \
  -color_trc bt709 \
  -colorspace bt709 \
  -c:v libx264 -crf 18 -preset medium \
  -c:a copy \
  clip3_normalized.mp4

# Step 3: Concatenate normalized clips
echo "file 'clip1_normalized.mp4'" > list.txt
echo "file 'clip2_normalized.mp4'" >> list.txt
echo "file 'clip3_normalized.mp4'" >> list.txt

ffmpeg -f concat -safe 0 -i list.txt -c copy output.mp4

Color space guide:

Standard	Color Space	Transfer	Primaries	Use Case
BT.601	SD	bt470bg	bt470bg	Old SD content
BT.709	HD	bt709	bt709	Modern HD/FHD
BT.2020 how to use video-processing-editing CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity How to use video-processing-editing on Cursor AI-first code editor with Composer 1 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 video-processing-editing 2 Execute installation command Execute the skills CLI command in your project's root directory to begin installation: $npx skills add https://github.com/erichowens/some_claude_skills --skill video-processing-editingcopy The skills CLI fetches video-processing-editing from GitHub repository erichowens/some_claude_skills and configures it for Cursor. 3 Select Cursor when prompted The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor: ◆ Which agents do you want to install to? │ │ ── Universal (.agents/skills) ── always included ──── │ • Amp │ • Antigravity │ • Cline │ • Codex │ ●Cursor(selected) │ • Cursor │ • Windsurf 4 Verify installation Confirm successful installation by checking the skill directory location: .cursor/skills/video-processing-editing Reload or restart Cursor to activate video-processing-editing. Access the skill through slash commands (e.g., /video-processing-editing) 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. Additional Resources ›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills? List & Monetize Your Skill Submit your Claude Code skill and start earning GET_STARTED → 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) post comment No comments yet — start the thread. general reviews Ratings 4.5★★★★★70 reviews ★★★★★Dhruvi Jain· Dec 28, 2024 video-processing-editing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos. ★★★★★Sakura Nasser· Dec 20, 2024 We added video-processing-editing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront. ★★★★★Nikhil Menon· Dec 16, 2024 video-processing-editing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos. ★★★★★Mateo Li· Dec 8, 2024 Registry listing for video-processing-editing matched our evaluation — installs cleanly and behaves as described in the markdown. ★★★★★Carlos Ghosh· Dec 4, 2024 video-processing-editing reduced setup friction for our internal harness; good balance of opinion and flexibility. ★★★★★Carlos Gill· Dec 4, 2024 video-processing-editing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos. ★★★★★Daniel Rao· Nov 27, 2024 video-processing-editing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos. ★★★★★Benjamin Desai· Nov 23, 2024 We added video-processing-editing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront. ★★★★★Amelia Bansal· Nov 23, 2024 Registry listing for video-processing-editing matched our evaluation — installs cleanly and behaves as described in the markdown. ★★★★★Oshnikdeep· Nov 19, 2024 Registry listing for video-processing-editing matched our evaluation — installs cleanly and behaves as described in the markdown. showing 1-10 of 70 prev1 / 7next bookmarkshare