IAPro-Community Smart Clip Detection
Use this skill when a product needs to find short, valuable moments inside long audio or video content.
Detection Contract
- Start from transcript segments with timestamps when available. Add visual and audio signals only when they improve ranking.
- Validate source consent, processing rights, language, duration, platform targets, and user quota before analysis.
- Batch candidate generation by transcript windows. Send only segment text, timestamps, and compact metadata required for scoring.
- Cache transcript segments, embeddings, signal extraction, and prompt outputs by source checksum plus prompt/schema version.
- Score candidates by hook strength, self-contained meaning, novelty, emotion, clarity, platform fit, and editability.
- Keep source timestamps, confidence, reason, title ideas, captions, aspect ratio, and source segment IDs.
- Deduplicate overlapping candidates using timestamp overlap, semantic similarity, and source segment IDs.
- Store enough metadata to regenerate or re-score after model, prompt, schema, or platform changes.
- Prefer review queues over auto-publishing for customer-facing, paid, or brand-sensitive products.
- Use idempotent render jobs for approved clips; never overwrite approved assets without a new version.
Candidate Schema
type ClipCandidate = {
sourceAssetId: string;
startMs: number;
endMs: number;
durationMs: number;
score: number;
confidence: number;
reason: string;
hookText: string;
summary: string;
titleOptions: string[];
platformFit: Array<"TikTok" | "Reels" | "Shorts" | "LinkedIn" | "YouTube">;
reviewStatus: "pending" | "approved" | "rejected" | "needs_edit";
promptVersion: string;
};
Token Reduction
- Chunk transcripts by coherent segments, not arbitrary character counts.
- Keep a rolling summary for context, but score only the current window and adjacent overlap.
- Use hashes for transcript excerpts in logs and persist full excerpts only when review requires them.
- Run cheap deterministic filters before model scoring: duration, silence, speaker count, and duplicate overlap.
Verification
- Test empty transcript, missing timestamps, overlapping candidates, duplicate reprocessing, malformed model output, revoked consent, queue retry, and review approval/rejection.
- Compare outputs against a small labeled fixture to verify scoring changes before rollout.
Related Skills
skill-live-processingskill-manual-video-processingskill-ai-orchestrationskill-unified-analytics