curate-livestream-transcripts

name	curate-livestream-transcripts
sheetId	4.09
description	Weekly transcript curation. Sweeps the 直播筆記 livestream-note Google Docs across N upstream Drive folders, extracts each doc's 逐字稿 (verbatim transcript) tab, and writes one structured zh-TW summary Doc per source into the destination SOT folder. Fully idempotent — skips any source whose target already exists. Trigger on /curate-livestream-transcripts, '整理直播逐字稿', 'curate the livestream transcripts', 'process this week's 直播筆記', or the weekly Monday routine.
category	training
project	curate-livestream-transcripts
platform	claude
status	Done
author	Peter Tu
input	None — all source/destination folder IDs are constants below. Reads 直播筆記 Docs from the upstream folders.
process	list → filter → dedup-against-destination → read 逐字稿 tab → summarize → create Doc in SOT folder
output	One 逐字稿_{Series}_{YYYY-MM-DD}_{topic} Google Doc per new source, in the destination folder, plus a run report.
synergy	[]

Curate Livestream Transcripts

npx skills add https://github.com/peter-tu-zynkr/zynkr-skill-builder --skill curate-livestream-transcripts

Autonomous, idempotent batch curator. Read every livestream-note Doc (「直播筆記…」) across the upstream folders, pull the 逐字稿 (verbatim voice-to-text) tab out of each, and produce one clean, structured zh-TW summary Doc per source in the destination (single source-of-truth) folder. Designed to run unattended on a weekly schedule — no human gates, no questions. When a source already has a summary, skip it.

This is the read-many-sources → merge-into-SOT pipeline, run on a recurring cadence.

---

Constants

Upstream source folders

Series label	Folder ID	What it holds
VibeCoding	16skZxvWi1V5ppBn6B7jHqFQnG33BRMr8	從0到1蓋產品 Vibe Coding 實戰直播 notes
ClaudeCode	1cM5pL3FVMD5CPeL02Ti9KQr3uCStRh16	Claude Code 實戰講座 notes
BizThinking	1LlYRd0RTHVp4Z_zESDg89IiZoUqJMUWc	從產品到賺錢 商業思維實戰講座 notes (series added 2026-06-05)

To add a future series: append one row here (label + folder ID) — nothing else changes.

Destination (SOT) folder

1-aXLBpLizM0PyMbYu7TIb3xw50BrFL6a — curated 逐字稿_* summary Docs land here. Write-only downstream: never read a summary back into an upstream. Data flows one direction only.

Account

All Drive operations run as [e-mail].

Tooling

• Local run (CLI, this skill invoked manually): use the mcp__google-workspace__* tools — list_docs_in_folder, get_drive_file_content, create_doc, update_drive_file.
• Cloud routine (claude.ai weekly run): the only Drive tools there are the Google-Drive connector — search_files (query parentId = 'FOLDER_ID'), read_file_content (by fileId), create_file (parentId + textContent + contentMimeType). The cloud routine prompt is self-contained and does not depend on this file being present.

---

Step 1 — List every upstream Doc

For each upstream folder, list its Docs (list_docs_in_folder locally, or search_files with parentId = '<folder>' in cloud). Record { title, fileId, series } for each, tagging series from the folder it came from.

Step 2 — Filter to livestream-note Docs

Keep a Doc only if both:

1. It is a livestream note, i.e. its title matches ANY of:
   • contains 直播筆記 anywhere, OR
   • matches <lecture name>_YYYY.M.D shorthand (e.g. Claude Code 實戰講座_2026.4.22, 商業思維實戰講座_2026.6.5), i.e. a course-name token immediately followed by _ and a dotted date.
2. A date is parseable from the title (Step 3 returns non-null).

Explicitly exclude auxiliary docs even if they sit in an upstream folder: titles containing 活動準備, 課程大綱, QA Master, Notes by Gemini, or that are recap/demo docs without 直播筆記 (e.g. 直播回顧, Demo, Event Recap, Transcript-only). These are out of scope by design.

Step 3 — Extract + normalize the date

From the title, take the first match of (20\d\d)[-./](\d{1,2})[-./](\d{1,2}). Zero-pad month and day → YYYY-MM-DD. If no date matches, drop the Doc (it failed the Step-2 gate).

Step 4 — Build the "already done" set from the destination

List the destination folder. For every existing Doc named 逐字稿_{Series}_{YYYY-MM-DD}_..., record the prefix key {Series}|{YYYY-MM-DD}. This is the idempotency set.

Step 5 — Decide what to create

For each filtered source, its key is {series}|{date}. Skip if that key is already in the destination set (record the matched existing filename for the report). Otherwise it is a create candidate.

Step 6 — Read the 逐字稿 tab for each create candidate

Read the source Doc (get_drive_file_content / read_file_content). The connector flattens all tabs into one text stream: structured human notes first, then a line that is just 逐字稿, then the raw verbatim transcript (often starting Speaker 1: 00:00 …).

• Locate the 逐字稿 section marker; the transcript is everything after it.
• If there is no 逐字稿 marker / the transcript section is empty → skip this source, and note （無逐字稿 tab） in the report. Do not create a Doc.

Step 7 — Detect cross-source duplication

Mis-filed duplicate transcripts have been observed. Compare the first ~500 non-whitespace chars of each candidate's transcript against the others in this run. If two are near-identical, still create one Doc per source, but add a ⚠️ 重複註記 line in each affected Doc body (naming the other source) and flag it in the final report.

Step 8 — Generate the structured summary (per source)

The 逐字稿 is long, unstructured, filler-heavy. Never dump it raw. Produce a clean Markdown summary, all in zh-TW (keep English technical terms in English — Skills, MCP, Vercel, Supabase, Plan Mode, agent, etc.). Structure:

# {original doc title}

> 來源：{original doc title}
> 原始文件：{original Google Doc URL}
> 系列：{Series}　·　日期：{YYYY-MM-DD}
> （若偵測到重複）⚠️ 逐字稿與「{other source title}」內容高度重疊，疑似誤置

## 主題
{one-paragraph main theme}

## 重點段落
### {topic heading 1}
- {key takeaway}
- {key takeaway}
### {topic heading 2}
- …

## 技術步驟 / 指令
- {concrete steps, commands, tool names, configs demonstrated — only if present}

## Q&A
- Q: {question} — A: {answer}
（若逐字稿中無問答互動則整段省略）

## 一句話總結
{single-sentence wrap-up}

Omit any section that has no content (except 主題, which always appears).

Step 9 — Compute the target filename

逐字稿_{Series}_{YYYY-MM-DD}_{topic} where {topic} is a 3–6 word zh-TW/English summary derived from the transcript, using × as the separator — matching the existing house style, e.g.:

• 逐字稿_ClaudeCode_2026-05-20_Skills Pipeline × Lift-and-Shift × Schema 版本驗證
• 逐字稿_VibeCoding_2026-05-22_Vercel 部署 × Kanban 拖拉 × 平行 Agent

Step 10 — Create the Doc in the destination folder

• Local: create_doc(title, content) → then update_drive_file(fileId, add_parents=<dest>, remove_parents='root') to move it into the SOT folder. Retrieve its URL.
• Cloud: create_file(parentId=<dest>, title=<filename>, textContent=<summary>, contentMimeType='text/plain') — text/plain auto-converts to a Google Doc inside the folder.

Step 11 — Final report

Output (zh-TW):

• 來源總數 N (after filtering)
• 略過 M（已存在） — each with the matched existing destination filename
• 新建 K — each with its new filename + Doc URL
• 重複註記 — any cross-source duplications flagged
• 錯誤 — per-source errors (e.g. 無逐字稿 tab, read failure). Never swallow errors silently.

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Notes

• The routine converges: once every source has a summary, a run creates 0 Docs and only reports skips. That is the healthy steady state.
• Adding a new series = one row in the Constants table (and the same row in the cloud routine prompt). The filter and dedup logic are series-agnostic.
• Keep this skill and the cloud routine prompt in sync when editing logic. The cloud routine is the claude.ai remote trigger Weekly livestream transcript curation (id trig_01HD3SAYjYyfQCNedEsZTaTW), cron 19 2 * * 1 (Mon 02:19 UTC), bound to the claude.ai Google-Drive connector. Edit it via the RemoteTrigger tool (action: update, that trigger_id) or /schedule.
• The cloud run depends on the claude.ai Google-Drive connector being authorized for [e-mail]'s Drive (same propagation gotcha as the project-status routine). If a Monday run reports 0 sources / permission errors, re-auth the connector on claude.ai.