# Jiang Video E2e > Use this skill as the integration map for turning one already-transcribed Jiang Lens video from synced Drive artifacts into a website-visible episode or interview, delegating detailed work to the narrower ingest, transcript, read-writing, and publishing skills. - Skill: `apresmoi/jiang-video-e2e` (Agent Skill) - Install (CLI): `npx skillmds@latest add apresmoi/jiang-video-e2e` - Raw SKILL.md: https://api.skillmd.com/api/skills/apresmoi/jiang-video-e2e/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: apresmoi (https://skillmd.com/u/apresmoi) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/apresmoi/jiang-video-e2e --- # 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 agents, assume artifacts already exist locally after Drive sync: ```text content/sources/raw/youtube/// metadata.youtube.json dump.json grouped.json transcription.json content/sources/raw/youtube/Interviews/// metadata.youtube.json dump.json grouped.json transcription.json ``` If these are missing, stop and hand off to `colab-video-pipeline`. ## Stage 1: Source Ingest Use `jiang-source-ingest`. Before import, check `content/workflow/tasks/source-processing-policy.json`. Known duplicate reuploads, empty artifacts, and archive-only raw folders should stay in the archive but must not become new website-visible episodes unless a maintainer explicitly requests `--force-policy`. The E2E and import scripts enforce this; treat a policy refusal as a completed archive decision, not as a metadata or Colab blocker. Mechanical import creates: ```text content/sources/videos// content/workflow/tasks//transcript-agent-packets.jsonl ``` The integration entry point remains: ```bash node ops/scripts/process-video-e2e.mjs --video-id VIDEO_ID --channel @PredictiveHistory # or, for interview-format sources: node ops/scripts/process-video-e2e.mjs --video-id VIDEO_ID --channel Interviews/ ``` If the orchestrator stops at source import, metadata, or packet preparation, resolve that under `jiang-source-ingest`. ## Stage 2: Boundary Review If the orchestrator reports `pending-boundary-review`, use `jiang-transcript-boundary-review`. Expected review file: ```text content/workflow/reviews//transcript-boundary-decisions.json ``` Then rerun: ```bash node ops/scripts/process-video-e2e.mjs --video-id VIDEO_ID --channel @PredictiveHistory # or the same interview command used at ingest ``` ## Stage 3: Semantic Transcript Pass If the orchestrator reports `pending-agent-packets`, use `jiang-agent-transcript-pass`. Expected outputs: ```text content/workflow/proposals//packet-*.semantic.json ``` Validate packet outputs: ```bash node ops/scripts/validate-agent-pass.mjs content/workflow/proposals//*.semantic.json ``` Then rerun the orchestrator. When all packet outputs exist, it aggregates: ```text content/lens/evidence/videos/.semantic.json ``` ## Stage 4: Public Source Read Use `jiang-episode-read-writer`. Expected output: ```text content/lens/episodes//read.json ``` The public source is not complete with only transcript, claims, glossary candidates, or semantic bundles. It needs a readable Jiang-voice distillation. Interview reads should preserve interviewer pressure, questions, and conversational context where those shape Jiang's answer. ## Stage 5: Episode Publication Use `jiang-episode-publisher`. Expected generated output: ```text website/src/data/lens/episodes/.json website/src/data/lens/interviews/.json ``` Expected routes: ```text /episodes// /episodes//transcript/ /interviews// /interviews//transcript/ ``` ## Stage 6: Optional Existing Lens Links During E2E, do not create or rewrite public lens docs unless explicitly asked. If the episode directly invokes an existing lens point, use `jiang-provenance-linker` to attach the existing `lens-point:*` ID to the relevant episode mark. ## Required Validation At the end of a successful E2E test: ```bash node ops/scripts/compile-content.mjs node ops/scripts/validate-content.mjs cd website && npm run build ``` If website UI changed, inspect the rendered episode and transcript pages before handoff. ## Boundary Do not update these as part of ordinary video E2E unless the maintainer explicitly asks: - `website/src/content/docs/lens*.md` - `content/lens/canon/` - `content/lens/glossary/` - `content/lens/ledger/` - `content/workflow/proposals//corpus-impact.json` - cross-episode concept pages Those belong to corpus impact, concept writing, atlas maintenance, provenance linking, or canon promotion.