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ChaiWithJaiChaiWithJai/davinci-resolve-claude-skills/skills/social-video-folder-autocutter/SKILL.md

social-video-folder-autocutter

Skill19 starsChanged 6 months agoMITView on GitHub

Plan repeatable hard-drive folder-to-video pipelines for speaker-led multicam content, including 9:16 Reels/Shorts up to 3 minutes at 1.1x talking speed and 16:9 long-form 3-7 minute cuts, with Resolve handoff, Editframe asset generation, audio/caption gates, and narrative beat planning.

1 other project uses a version of this.

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We split this skill into the rules it gives an agent

14 rules. About 0 are common, 12 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈12

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this before editing when the user wants a repeatable pipeline from a folder of camera/audio files into social or long-form cuts.

    line 8
  2. Very specific:

    run the DaVinci skill probe_media.py.

    line 18
  3. Very specific:

    prefer wide/medium/close-up sequences over isolated filler shots.

    line 32
  4. Distinctive:

    If the speaker is physically demonstrating a concept, prioritize that performance over generic B-roll.

    line 36
  5. Distinctive:

    Keep B-roll source URLs/license notes in the plan, and keep raw B-roll sources disabled on a separate Resolve track for HITL replacement.

    line 38
  6. Distinctive:

    Use Coverr as the default free B-roll source when the project lacks enough native visual evidence.

    line 39
  7. Distinctive:

    Prefer Coverr clips that can function as visual evidence for a spoken claim.

    line 42
  8. Distinctive:

    Use this loop when a prior cut is close and the user says it is nearly postable:

    line 57
  9. Distinctive:

    Preserve the liked picture cut: Do not re-edit the spine unless the user asks.

    line 59
  10. Distinctive:

    Keep caption text spoken-first: Do not replace spoken captions with topic cards.

    line 61
  11. Distinctive:

    Only then hillclimb B-roll: Add B-roll where it solves a specific edit problem: jump cut coverage, proof of a claim, visual explanation, …

    line 64
  12. Distinctive:

    Use Editframe in parallel for motion assets, not as the primary NLE when the user expects Resolve source layers.

    line 81
  13. Distinctive:

    Avoid using Editframe as the only deliverable for source-camera editing unless the user explicitly wants a rendered web-video output.

    line 92
  14. Distinctive:

    Never call the job done unless these artifacts exist:

    line 96

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Social Video Folder Autocutter

Use this before editing when the user wants a repeatable pipeline from a folder of camera/audio files into social or long-form cuts.

Output Profiles

  • Vertical social: 1080x1920, 9:16, up to 3 minutes, default 1.1x speed for speaker-led sections, Netflix-style captions, source layers preserved in Resolve.
  • Horizontal long-form: 1920x1080, 16:9, 3-7 minutes, default normal speed, lower thirds/captions optional, source layers preserved in Resolve.

Required Pipeline

  1. Scan the folder:
    • run the DaVinci skill probe_media.py;
    • run audio_quality_audit.py;
    • identify camera ISOs, HQ audio, intro/bumper, brand files, old renders, and existing Resolve exports.
  2. Make a story plan:
    • hook/agenda,
    • body beats,
    • recap/CTA,
    • rejected or risky segments.
  3. Build edit decisions:
    • source file, in/out, angle, reason, transition, crop, audio source, caption text.
    • reject word-boundary-only cuts.
  4. Build a B-roll coverage plan:
    • assign B-roll only where it proves a claim, establishes the event world, covers a jump cut, compresses time, or gives the audience an emotional/visual release.
    • tag every B-roll candidate as wide, medium, close-up, insert/detail, environment, screen/product, or human moment.
    • prefer wide/medium/close-up sequences over isolated filler shots.
    • require source page/licensing metadata for stock footage and store it with the manifest.
    • reject low-opacity overlays over faces unless the user explicitly asks for texture; default to full-opacity cutaways or designed picture-in-picture.
    • On warm-traffic educational reels, use B-roll sparingly as proof/coverage. Keep the speaker on screen for trust-building beats, and reserve stock inserts for claims about work, learning, planning, community, or tool use.
    • If the speaker is physically demonstrating a concept, prioritize that performance over generic B-roll. Use the board/prop as a quick visual aid, not a long replacement for the speaker.
    • Reject static whiteboard/slide replacements longer than 3s-5s unless the user explicitly asks for a screen-recording style lesson. For Instagram, the speaker should remain visible across spoken educational beats.
    • Keep B-roll source URLs/license notes in the plan, and keep raw B-roll sources disabled on a separate Resolve track for HITL replacement.
    • Use Coverr as the default free B-roll source when the project lacks enough native visual evidence. Store search query, source URL, creator/source attribution, license URL, download path, and rejection reason for any candidate not used.
    • Coverr licensing/API notes must be recorded with the manifest: free downloads require attribution credit unless the account/license says otherwise; API usage requires clickable Coverr attribution; do not use Coverr assets for AI training/datasets or to build a competing stock/video service.
    • For Chai With Jai workshop promos, default search intents should include: small business owner, entrepreneur workshop, business planning, whiteboard, team collaboration, laptop work, coffee meeting, Jersey City, AI technology, coding, marketing campaign, and community event.
    • Prefer Coverr clips that can function as visual evidence for a spoken claim. Reject generic city lights, abstract technology, slow lifestyle filler, or anything with identifiable brands/trademarks unless the usage is clearly safe.
  5. Generate review artifacts:
    • low-res proxy or stills for critic review,
    • audio ranking JSON,
    • caption preview,
    • Resolve/FCPXML/EDL plan.
  6. Build the real handoff:
    • Resolve layered timeline for final editing,
    • original repair audio disabled,
    • clean dialogue and music separated,
    • graphics/captions/B-roll on separate tracks,
    • timeline audit passes.

Winning Instagram Reels Loop

Use this loop when a prior cut is close and the user says it is nearly postable:

  1. Preserve the liked picture cut. Do not re-edit the spine unless the user asks. Export or locate a picture spine without burned captions when possible.
  2. Caption from actual speech timing. Extract dialogue from the liked cut, run Whisper word-level transcription, and build short burned-in phrase captions. Correct only high-confidence domain terms such as names, event titles, dates, and obvious ASR artifacts.
  3. Keep caption text spoken-first. Do not replace spoken captions with topic cards. Topic cards may remain as a separate top lane, but the lower caption lane must match what the speaker says.
  4. Verify phone-safe stills. Inspect opening, mid-body, B-roll/whiteboard, CTA, and closing frames. Captions must avoid the speaker's mouth/chin and Instagram's bottom UI region.
  5. Verify audio. Target -14 to -16 LUFS integrated, true peak below -1 dBTP, with music ducked under speech.
  6. Only then hillclimb B-roll. Add B-roll where it solves a specific edit problem: jump cut coverage, proof of a claim, visual explanation, location/event context, or emotional reset.

Required Output Artifacts For Repeat Use

For each folder cut, create or update:

  • story_plan.md
  • edit_decisions.csv or .json
  • broll_plan.json
  • broll_asset_manifest.json
  • caption_chunks.txt
  • captions.ass or Resolve caption track
  • verification_report.json
  • Resolve timeline or handoff report

Editframe Role

Use Editframe in parallel for motion assets, not as the primary NLE when the user expects Resolve source layers.

Good uses:

  • animated branded bumpers,
  • Netflix-style/word-highlight caption assets,
  • lower thirds,
  • waveform/title cards,
  • social-safe graphic overlays,
  • fast HTML/React review proxies.

Avoid using Editframe as the only deliverable for source-camera editing unless the user explicitly wants a rendered web-video output. Editframe renders can be imported back into Resolve as V3/V4 graphics or bumper layers.

Handoff Contract

Never call the job done unless these artifacts exist:

  • media manifest,
  • story plan,
  • edit decisions,
  • audio quality audit,
  • caption artifact or burned-in review,
  • layered Resolve timeline or interchange file,
  • timeline audit report.

Useful Command

python3 skills/social-video-folder-autocutter/scripts/create_folder_cut_brief.py \
  /path/to/project-folder --out /path/to/project-folder/output/folder_cut_brief.json

Then use davinci-resolve-social-editor for the Resolve build.

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