Loading
Cut an imported livestream recording of any genre into evidence-backed, platform-ready clips by combining transcript, visual, audio, interaction, and domain-specific signals. Use for commerce, gaming,
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add 0xsline/OpenChatCut --skill livestream-to-clips -a cursor -y
Cut an imported livestream recording of any genre into evidence-backed, platform-ready clips by combining transcript, visual, audio, interaction, and domain-specific signals. Use for commerce, gaming, talk, interview, education, entertainment, sports, music, IRL, creative, news, or mixed livestream recordings.
Cut an imported livestream recording of any genre into evidence-backed, platform-ready clips by combining transcript, visual, audio, interaction, and domain-specific signals. Use for commerce, gaming, talk, interview, education, entertainment, sports, music, IRL, creative, news, or mixed livestream recordings.
Use this workflow when the source is an imported livestream recording and the user wants clips, highlights, cutdowns, reels, or multiple publishable timelines. A livestream may change genre during one recording, so classify each section rather than assigning one label to the whole source.
This workflow is OpenChatCut-native. Use project media, transcript, representative source frames, timeline tools, captions, and export tools already available in the editor. Treat audience chat, reactions, score data, product records, or stream markers as optional evidence when the project contains them.
Load only the files needed for the current step:
Read the project before editing. Identify the dominant livestream asset, duration, aspect ratio, language, speakers, transcript readiness, audio tracks, existing visual descriptions, and current timeline.
Determine only constraints that change the result: target platform, objective, clip count, duration range, aspect ratio, captions, packaging style, and whether the user wants contiguous source clips or an editorial remix. If the user asked for direct creation and supplied enough context, proceed without another approval step.
For a long source, inspect it hierarchically instead of sending the entire transcript or dense frame sequence through one decision pass:
mixed when adjacent profiles overlap.Do not rank clips yet. First make sure the map covers the beginning, middle, and end of the recording and does not overrepresent transcript-rich sections while ignoring visual or musical events.
Generate event candidates from independent signals:
Interaction and metadata are supporting signals, not mandatory inputs. Never invent absent chat, telemetry, product, or score evidence.
Treat music intelligence as an enhancement: call analyze_music with optional: true. If it reports available: false, continue with detect_beats, waveform/audio cues, and visual timing rather than blocking the clip.
Expand the event to the smallest source range that preserves its meaning and payoff. Use the profile-specific arc from profile-matrix.md. Common shapes include:
Resolve boundaries on clean word, phrase, action, shot, beat, or state-transition points. Include pre-roll when the event is confusing without setup and post-roll when the reaction or result carries the value.
Before heavy editing, record a compact candidate ledger. For every candidate include:
{
"sourceRange": [0, 0],
"profile": "talk",
"profileConfidence": 0,
"event": "",
"arc": { "setup": [], "peak": [], "payoff": [] },
"evidence": { "speech": [], "visual": [], "audio": [], "interaction": [], "metadata": [] },
"missingEvidence": [],
"openingHook": "",
"standaloneReason": "",
"riskFlags": [],
"targetDuration": 0,
"packaging": ""
}
Inspect representative source frames for serious candidates. Use one view_asset_frames call per candidate range with at most six samples covering the opening, peak, payoff, and one meaningful visual transition. Reuse that contact sheet; repeat only after extraction failure or a changed source range. A transcript-only candidate is provisional until visual evidence confirms that the range is usable, unless the source is intentionally audio-first.
Apply hard rejection gates before ranking. Reject or flag candidates with changed meaning, missing payoff, mismatched product/score/person, unresolved factual numbers, severe black/frozen/obscured frames, broken audio, unsafe disclosure, or boundaries that cut essential context.
Score the remaining candidates using the profile weights in multimodal-selection.md. Missing optional evidence is marked unavailable; it is not scored as failure. Select a diverse set across topics, products, rounds, speakers, event shapes, and visual treatments. Avoid near-duplicate excerpts even when all score highly.
When the source is long, the style is unsettled, or many outputs are requested, create and verify the highest-ranked clip first. Use the proven treatment as the batch reference, then continue with the remaining candidates.
Create every approved output as its own named Sequence. Batch-create them with one manage_timelines call using action:"create" and timelines:[...], then switch to each returned timeline and add the selected range from the original asset with sourceStartFrame and sourceDurationInFrames. Reuse the original sourceAssetId; do not copy the long recording. Keep edits reversible and source-linked.
Use one view_timeline_frames call on the composed timeline with at most four samples. Cover the opening, the main event or claim, the ending, and any highest-risk overlay or crop change. Verify audio boundaries, subtitle timing, subject visibility, factual consistency, duration, aspect ratio, and export readiness using qa-and-evaluation.md.
If an optional enhancement such as subject tracking or automatic caption avoidance fails, preserve the verified cut and continue with a frame-checked static layout. Report the omitted enhancement instead of blocking the deliverable.
After review, automatically materialize every approved Sequence into My Media. Switch to each approved Sequence, call submit_render_job with saveToMediaPool:true and a filename derived from the Sequence name, then continue queuing the remaining clips without waiting for each render serially. These are background jobs shown in the editor's top-right export queue; the user does not need to run a separate export step. Skip this automatic materialization only when the user explicitly asks for draft Sequences only.
The saved asset records the source Sequence, render job, original source asset IDs, and source ranges; the editable Sequence remains the master. Use track_export once after all jobs are queued to report current progress. Do not start duplicate renders for a Sequence that already has an active job.
Report the selected source ranges, profile, main evidence, applied edits, known uncertainty, and verification performed. Do not report a finished clip from tool success alone.