Trans

Skill WALKERKILLER/Look-Tongji-Notes/skills/trans

Transcribing Lessons from Tongji Look Platform to Notes with Agent Skill.

Install

npx -y skills add WALKERKILLER/Look-Tongji-Notes --skill trans

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Transcribe a single Tongji Look lecture video to SRT + TXT, optionally download slide snapshots in parallel.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

3.2 KB, as published. Nobody here has run it

Transcribe

Transcribe one lecture video from look.tongji.edu.cn and optionally download slide snapshots.

When to Use

User says /trans or "transcribe this lecture".
User provides a lecture URL or course_id + sub_id.

Workflow

Resolve lecture with URL or IDs:

python "<SKILL_DIR>/../../scripts/look_tongji.py" transcribe --lecture-url "<LECTURE_URL>"

python "<SKILL_DIR>/../../scripts/look_tongji.py" transcribe --course-id "<ID>" --sub-id "<ID>"

Transcribe + download slides in one run:

python "<SKILL_DIR>/../../scripts/look_tongji.py" transcribe --course-id "<ID>" --sub-id "<ID>" --slide

The CLI prints real-time progress. Stdout contains result paths.

Non-Interactive / Batch Usage

When running from an automated pipeline, avoid input() prompts with these flags:

Flag	Description
`--sub-id`	Specify lecture sub_id directly (skips interactive lecture picker)
`--course-id`	Specify course ID directly (skips interactive course selection)
`--slide`	Download slides after transcription
`--concurrency N`	Slide download concurrency (default 4)
`--retries N`	Slide download retries (default 3)

manifest.json — Required for Wiki Build

transcribe does NOT automatically create manifest.json. After transcription, you MUST create it manually so the wiki indexer can find this lecture.

Create manifest.json in the session's 原始数据/ directory with this structure:

{
  "course_id": "COURSE_ID",
  "sub_id": "SUB_ID",
  "course_title": "课程名称（真实中文名）",
  "session_title": "YYYY-M-D 第X节",
  "base_name": "COURSE_ID_SUB_ID",
  "artifacts": {
    "srt": "F:\\workspace\\raw\\课程名称\\YYYY-M-D 第X节\\原始数据\\COURSE_ID_SUB_ID.srt",
    "txt": "F:\\workspace\\raw\\课程名称\\...\\COURSE_ID_SUB_ID.txt",
    "notes": "",
    "timeline": "",
    "slides": "F:\\workspace\\raw\\课程名称\\...\\slides\\index.json"
  },
  "duration_seconds": 5580
}

Critical path rules:

Use absolute paths for all artifact paths (relative paths break on Windows)
duration_seconds: must be ≥ 3600 (1 hour); if < 3600, the lecture is marked不合格 and wiki shows it as 缺失
If no slides were downloaded, leave slides field empty ("")
Fields notes and timeline are filled by /note command later

Artifacts

<course_id>_<sub_id>.srt — subtitle with timestamps
<course_id>_<sub_id>.txt — plain text transcript
<course_id>_<sub_id>.json — metadata
slides/ — slide images + index.json (if --slide)
manifest.json — MUST create manually after transcription

One Session Per Call

For batch transcribing, use /ralphtrans.

Where `<SKILL_DIR>` Points

`<SKILL_DIR>` is the directory containing this `SKILL.md`. Shared scripts (`look_tongji.py`, `timeline_tools.py`, `tongji_backend/`) and references live two levels up in the repository root (`<SKILL_DIR>/../../scripts/` and `<SKILL_DIR>/../../references/`).