Video editing: which service, and how

Editing starts from footage that already exists, so the first rule is not to damage it. Work in this order, and pick the service for each step from the table below.

1. Probe. Run ffprobe -v error -show_entries format=duration:stream=codec_type,codec_name,width,height,r_frame_rate,sample_rate -of json in.mp4. Note the frame rate, whether it is variable, the sample rate and the duration.
2. Transcribe once if anyone speaks: word-level timestamps, saved as JSON next to the source. Every text decision reads from this file: silences, filler words, highlights, captions.
3. Write the edit decision list as data: source, in and out points, track, effect, the service for each step and its cost. Show it to the person and wait for a yes before any paid call. On varg, run estimate_cost first.
4. Render deterministically wherever you can (FFmpeg, HyperFrames, Remotion). Use a generative model only on the shortest span whose pixels must change. Cut that span out, edit it, and stitch it back.
5. Verify. ffprobe the output, grab stills at every cut point (ffmpeg -ss <t> -i out.mp4 -frames:v 1 cut-<t>.jpg) and look at them. Compare the duration with the edit list.

Which service for which edit

The first service in each row is the default pick. Switch only for the reason given. The full table, with access, pricing and links, is in references/services.md.

Job	Pick	Switch to
Cut, trim, resize, join	FFmpeg on the agent's machine	varg ffmpeg / trim / slice / resize when there is no shell or the clips already live on varg. Rendi for hosted FFmpeg on its own key
Talking-head cleanup	Word timestamps + FFmpeg (varg transcription, ElevenLabs Scribe or Groq Whisper)	auto-editor for a quick local silence pass. Descript when a person will review the edit in an app
Long video to Shorts	OpusClip for finished, scored and captioned clips	Transcript + Gemini + FFmpeg with a tracking crop when you need control. Vizard for cheap bulk clipping
Captions & subtitles	ASS from your word timestamps, burned in with FFmpeg	Remotion / HyperFrames for animated styles. Submagic / ZapCap for their templates
Assemble from a timeline	HyperFrames or Remotion when the agent writes the video	Shotstack / Creatomate for hundreds of template variants rendered in the cloud
AI edits: restyle, swap, relight	varg: minimax_h3_max_recast (swap people), minimax_h3_max_relight, id_v2v (restyle, keep identity), minimax_h3_max_extend / _insert, prompt edits on kling_v4 and omni_flash_1_1	Runway Aleph for prompt and keyframe edits. Luma Ray for motion-preserving restyle and reframe. Wan VACE for masked inpainting
Dubbing & lip sync	HeyGen Video Translate (translation, voice and lips in one call)	ElevenLabs Dubbing for the best voice, then Sync Labs (varg sync_v3, sync_v2_pro) for the lips
Upscale, slow-mo, cut-outs	Topaz (varg topaz_video) on the final export	SeedVR / ByteDance / Sima upscalers for price. Bria for background removal
Find moments & QA	Gemini video understanding (timecodes, QA questions)	varg video-analyze for a quick JSON report. Twelve Labs / VideoDB to search a library
Hand off to an editing app	A timeline file: OTIO, FCPXML or Premiere XML	DaVinci Resolve Studio MCP to drive the app live (the only official one). Premiere has only community bridges, and CapCut has no API

Rules that save a re-render

• Frame-accurate cuts need a re-encode. -c copy snaps to the nearest keyframe and can leave black frames or audio drift. Re-encode the cut (-c:v libx264 -crf 18 -c:a aac), or use varg trim with precise: true.
• One frame rate before joining. Convert mixed sources first (-vf fps=30 -r 30, -ar 48000). Then use the concat filter, not the concat demuxer, unless every input has identical codecs and settings.
• Cut audio with the video. Use the same timestamps for both. Pad each spoken cut by 50–150 ms and add a ~30 ms afade at each joint so the cuts do not click. Check that the output duration matches the edit list.
• Never overwrite the source. Write to new files, and use low-resolution proxies for analysis and previews. Apply the final cut to the full-resolution source, and upscale only the final export.
• Generative windows are short. Most video-to-video models take 5–30 s per pass. Split at a calm moment, edit each piece, and cross-fade the seams. Check faces and identity on frame grabs before you deliver.
• Hosted APIs are jobs. Store the job id, poll or take a webhook, back off on 429, and download the result straight away (some links expire within hours). Pass public URLs between steps. On varg, upload once with /v2/files.
• Reframe with tracking. For 9:16, follow the face or subject rather than cropping the center. Keep captions inside the safe zones: the top ~12% and bottom ~20% are covered by platform UI.
• Spend only after a yes. Show the edit list and the estimate first. Cache transcripts and generations. Never re-run a paid model just to move a cut.

On varg

One key covers the deterministic and the generative steps: ffmpeg, trim, slice, resize and render for the cut, transcription for word timestamps, video-analyze for QA, and the video edit, lip-sync and upscale models above. Each model's inputs are in its spec at https://varg.ai/models/<model>.md. Prices are in credits from estimate_cost; never quote them in money. For generating new footage, use the varg-ai skill.

The full table, with access, pricing, gotchas and links: references/services.md. Online: https://skills.varg.ai/browse/skills/edit-services.md

How the best editing agents cut footage (transcript first, edit decision list, word-boundary padding, 30 ms audio fades, proxies, self-checks with a retry cap, timeline hand-off): references/practices.md, 21 practices with sources. For the named edits themselves (cuts, transitions, titles, UI, cursors, devices, numbers), use the varg-video-edit-library skill.