jiang-video-e2elisted

# Jiang Video E2E Use this when testing or explaining the full path for one video: ```text Google Drive Colab artifacts -> committed raw source artifacts -> canonical source transcript -> semantic packet outputs -> internal semantic bundle -> public source read -> generated website episode or interview ``` This is a pipeline map, not a future autonomous-agent persona. Autonomous agents should normally run the narrower skill for their job. This skill is useful when a maintainer asks for one video end-to-end or when we need to test whether the narrower skills compose correctly. ## Model Policy Default to `gpt-5.4` for first-pass video parsing, semantic packet completion, and public episode/interview read drafting. Scheduled production wakes should use low reasoning when supported; request escalation only when the source is dense, noisy, or conceptually consequential. Escalate to `gpt-5.5` for detailed QA, source ambiguity, contradiction, strong new Jiang formulations, or possible lens/atlas mutation. Do not use mini-class models for normal source parsing; they are for coordination and cheap comparison only. The first pass is allowed to be a strong draft. It must preserve exact source refs, signature moments, questions, chronology, and enough evidence for a later strong-model QA or lens pass to improve it without rereading the whole pipeline from scratch. ## Stage 0: Colab Has Produced Artifacts Colab automation belongs to `colab-video-pipeline`. For normal content agen