Plan draft video-ad variations from supplied performance evidence, preserving the distinction between observed data and creative hypotheses.
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Find useful moments in long-form video, create ranked short clips, reframe them, and generate captions without losing source provenance.
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Turn an authorized long-form source into a small, ranked set of coherent short-form candidates. Preserve the source meaning, identify useful start/end points, adapt framing, and caption only after the candidate segment is known. This is editorial packaging, not automatic publication.
If no platform is named, return a neutral 9:16 candidate plus the assumption; do not claim that a crop is platform-compliant without a supplied rule set.
media.clip_video/AI clipping when available to propose highlights.
Capture each candidate's timestamps, transcript or rationale, score if
supplied, and source parent.media.reframe_video or the live
autocrop schema for the requested ratio. Check faces, products, text, and
speaker direction after the crop.media.caption_video only after selecting the
segment. Preserve supplied wording; flag uncertain transcription, names,
numbers, and claims for human review.The current MuAPI utility schemas differ: AI clipping accepts a source video and highlight count, autocrop accepts explicit start/end times and a ratio, and captions accept provider/language/theme fields. Resolve them through MODELS.md and muapi-video-tools.md; do not assume a single tool performs all three stages.
Return a ranked candidate table with clip ID, source ID, exact timestamps,
reason/hook, transcript status, crop ratio, caption status, model/tool,
prediction IDs, output URLs, QA notes, and rejected/failed candidates. Include
the source link and parent/child relationships. Save an asset manifest and
receipt under .video/ only when permitted, following
reports-and-receipts.md.
Never change the source's meaning, remove qualifying context, or imply that captions or crops are final without review. Publishing remains out of scope.
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Plan draft video-ad variations from supplied performance evidence, preserving the distinction between observed data and creative hypotheses.
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Create authorized presenter, avatar, and UGC-style video drafts with explicit voice, likeness, consent, and synthetic-media handling.
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Analyze a script or existing edit, identify visual gaps, and generate supplemental B-roll coverage with clear parent and factual-status labels.
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Build a consented continuity system for an authorized person, fictional character, product, or world across video shots.
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Translate creative intent into coherent cinematography, framing, camera, lens, lighting, motion, and sound direction for a video shot.
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Create a non-explicit romantic scene from authorized adult references with clear consent, identity boundaries, and synthetic-media labeling.
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