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Clean lecture notes

Skill ljim17502-hub/clean-lecture-notes-skill/skills/clean-lecture-notes

Codex skill to clean lecture transcripts and generate courseware-aligned study notes with Mermaid mindmaps and optional Feishu/Lark writeback.

Install
npx -y skills add ljim17502-hub/clean-lecture-notes-skill --skill clean-lecture-notes

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Clean lecture transcripts, meeting transcripts, class recordings, ASR output, Feishu/Lark docx transcripts, local Markdown/text transcripts, and courseware into review-ready study notes. Use when the user asks for transcript cleanup, lecture notes, course notes, classroom recording cleanup, study-note generation, Mermaid mindmaps, Feishu/Lark writeback, or 中文任务 such as 清洗录音转文字、清洗逐字稿、整理上课录音、整理课堂笔记、把录音和课件做成笔记、生成复习版笔记、写回飞书。

SKILL.md

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Clean Lecture Notes

Overview

Use this skill to turn noisy lecture transcripts, meeting transcripts, class recordings, Feishu/Lark docs, local ASR output, and optional courseware into reliable cleaned transcripts and review-ready study notes.

Default to preserving the original transcript. Unless the user explicitly asks to overwrite a source document, create new cleaned transcript and study-note artifacts.

Workflow

  1. Identify the source:
    • Local transcript file: read it directly.
    • Local audio or video: use the user's available transcription or meeting-minutes tooling first, then continue from the transcript.
    • Feishu/Lark Minutes URL: if Feishu/Lark tools are available, fetch the transcript before cleaning.
    • Feishu/Lark docx transcript URL: if lark-doc is available, fetch the source doc before cleaning.
    • Courseware URL or file: extract its title, module order, core diagrams, and assignments; use it as the study-note spine.
  2. For transcript cleanup only, read references/transcript-cleaning.md and produce a cleaned transcript.
  3. For study notes, read both references/transcript-cleaning.md and references/study-note-workflow.md.
  4. For Feishu/Lark output, read references/feishu-output.md; treat Feishu/Lark as optional integration, not a hard dependency.
  5. Before final delivery, read references/quality-checklist.md.
  6. When local note artifacts exist, run scripts/check_note_artifacts.py; for final study notes, include --final-note.

Output Contract

  • Cleaned transcript: remove filler words, repeated fragments, ASR noise, broken punctuation, and fragmented paragraphs while preserving meaning, examples, names, numbers, assignments, caveats, and event order when relevant.
  • Study notes: include a Mermaid mindmap or knowledge map, time-boxed review path, core conclusions, active recall questions, conceptual modules, practice/project migration items, interview-ready wording when useful, and sources.
  • Emphasis: bold the first occurrence of core concepts, key methods, important metrics, and high-value sentences; do not bold entire paragraphs.
  • Perspective: final study notes must use a learner review and transfer perspective. Do not write them as class minutes, a meeting recap, or "the teacher said..." narration.
  • Pending information: cleaned transcripts may keep speaker labels and pending checks; final study notes must not include a standalone 待确认 section. Put unresolved factual uncertainty in the source and quality audit section.
  • Feishu/Lark output: default to creating new docs, verify outlines after writing, and ensure the top 思维导图 section is a Mermaid flowchart or equivalent visual whiteboard rather than a plain text tree.

Optional Integrations

  • Use Feishu/Lark tools only when they are available in the current Codex environment or the user explicitly asks for Feishu/Lark writeback.
  • When Feishu/Lark tooling is unavailable, produce local Markdown artifacts and clearly state that remote writeback was skipped.
  • For visual study-note overviews, prefer Mermaid flowchart TD or flowchart LR.

Safety Rules

  • Never write secrets, API keys, access tokens, .env values, private Feishu/Lark links, or credentials into notes, logs, examples, or commits.
  • Do not overwrite or delete source transcripts unless the user explicitly asks for it.
  • Mark uncertain ASR corrections briefly instead of turning guesses into facts.
  • Do not add analytics, telemetry, or unrelated network calls.

Final Response

Report:

  • the local output paths or Feishu/Lark document URLs created,
  • whether the source transcript remained unchanged,
  • which validation commands were run,
  • any unresolved terminology or source-quality caveats.

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