# Ralphtrans

> Batch transcribe ALL playable lectures for a course in a persistent loop. Uses a JSON state file for checkpoint/resume. Runs until every lecture is transcribed or marked failed.

- Skill: `walkerkiller/ralphtrans` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add walkerkiller/ralphtrans`
- Raw SKILL.md: https://api.skillmd.com/api/skills/walkerkiller/ralphtrans/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: WALKERKILLER (https://skillmd.com/u/walkerkiller)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/walkerkiller/ralphtrans

---


# Ralph Batch Transcribe

Batch transcribe all playable lectures for a course with checkpoint/resume.

## When to Use

- User says `/ralphtrans` or "batch transcribe this course".
- User wants to transcribe an entire course in one go.

## Workflow

1. Start batch transcription:

```bash
python "<SKILL_DIR>/../../scripts/look_tongji.py" batch-transcribe --course-id "<ID>"
```

2. The CLI:
   - Loads all playable lectures for the course.
   - Creates/reads `batch_state.json` in the workspace to track progress.
   - Transcribes lectures one by one, updating state after each.
   - Retries failed lectures up to `--max-retries` times.
   - Prints a summary when all lectures are done or failed.

3. **Interrupt & Resume:**
   - Press Ctrl+C to safely stop. State is saved.
   - Re-run the same command to resume from pending lectures.

## State File

`batch_state.json` structure:
```json
{
  "course_id": "xxx",
  "started_at": "ISO8601",
  "updated_at": "ISO8601",
  "lectures": [
    {"sub_id": "xxx", "sub_title": "xxx", "status": "pending|done|failed", "attempts": 0, "error": null}
  ]
}
```

## After Completion

- Run `/note` for each transcribed lecture to generate study notes.
- Run `/wiki` to rebuild and serve the course site.

## 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/`).