スキル一覧に戻る
TasseDeCafe

clean-language-reactor

by TasseDeCafe

0🍴 0📅 2026年1月22日
GitHubで見るManusで実行

SKILL.md


name: clean-language-reactor description: Clean up Russian vocabulary CSV exports from Language Reactor (YouTube subtitle learning extension). Use when the user asks to process or clean up a Language Reactor CSV file.

Clean YouTube Subtitle Vocabulary Exports

Transform vocabulary exports from YouTube subtitle extensions into flashcards for recognition practice.

Supports multiple formats:

  • Language Reactor exports (WORD| prefix format)
  • Simple CSV exports (word, sentence, timestamp, videoTitle, videoId)

Workflow

  1. Preprocess the raw export:

    python3 preprocess_language_reactor.py export.csv -o preprocessed.csv
    

    This will:

    • Auto-detect the CSV format
    • Parse the export and extract word data
    • Identify multi-word chunks (collocations) - marked with "Is Chunk=yes"
    • Automatically fetch the YouTube transcript
    • Output preprocessed.csv and preprocessed.transcript.txt
  2. Clean up the preprocessed file (with Claude):

    • Read both the preprocessed CSV and the transcript
    • Convert chunks to nominative form (e.g., "собственным успехом" → "со́бственный успе́х")
    • Add translations
    • Create simpler example sentences
    • Add stress marks
    • Output the final 4-column CSV

Examples

See the examples/ directory:

  • Preprocessed input: examples/language-reactor-preprocessed.csv
  • Transcript: examples/language-reactor-preprocessed.transcript.txt
  • Cleaned output: examples/language-reactor-cleaned.csv

Card Purpose

These cards are for recognition (passive vocabulary), not production:

  • Front: Russian word + example sentence
  • Back: English translation

Use --mode recognition when creating the deck:

python3 create_deck.py --mode recognition cleaned.csv -o passive.apkg

CSV Output Format

4 columns, comma-separated:

ColumnContent
1Russian word/phrase with stress marks
2English translation
3Example sentence in Russian
4English translation of example

Processing Rules

1. Read Both Files

When cleaning up, read:

  • The preprocessed CSV (word, translation, context, POS)
  • The transcript file (full video text for understanding context)

2. Handle Chunks and Collocations

Pre-selected chunks: Entries marked "Is Chunk=yes" were selected as multi-word phrases by the user. Keep these as collocations but convert to nominative form:

  • собственным успехомсо́бственный успе́х (one's own success)
  • качественной медицинека́чественная медици́на (quality healthcare)

Identify additional collocations: Some single words should be studied with their common collocations. Use the transcript to identify:

  • птичийна пти́чьих права́х (on precarious terms)
  • поразитьпораже́ны в права́х (disenfranchised)
  • доступдо́ступ к (+ dat.) (access to)

3. Create Simpler Example Sentences

The subtitle context is often:

  • Incomplete (cut off mid-sentence)
  • Too long or complex for flashcards
  • Contains multiple clauses

Create a simpler, clearer sentence that:

  • Uses the word with the same meaning as in the video
  • Is short enough for a flashcard (under 15 words ideally)
  • Is grammatically complete

Use the full transcript to understand the meaning and context.

4. Stress Marks (Column 1 Only)

  • Add stress mark (´) on the stressed vowel for multisyllabic words
  • Skip monosyllables
  • Skip words with ё (always stressed)

5. Gender for Soft Sign Nouns

  • For nouns ending in soft sign (ь), indicate gender with (m.) if masculine
  • Most soft-sign nouns are feminine, so only mark masculine ones
  • Examples: день (m.) — day, гость (m.) — guest, дождь (m.) — rain

6. Verb Pairs

  • When the word is a verb, include both the perfective and imperfective forms
  • When the verb is used with a preposition in the context, include that preposition and the case used with that preposition in this context Format: imperfective/perfective (e.g., ви́деть/уви́деть)
  • Only include one form if the other isn't commonly used or doesn't make sense
  • Include both when learners should know the pair

7. CSV Quoting

Wrap any field containing commas in double quotes.

Example Transformation

Preprocessed (from examples/language-reactor-preprocessed.csv):

Word,Translation,Context (RU),Context (EN),POS
птичий,"bird's, avian",Пока у тебя нет гражданства ты всегда на птичьих правах,Until you have citizenship you're always on precarious terms,Adj

Cleaned output (see examples/language-reactor-cleaned.csv):

на пти́чьих права́х,on precarious terms (no legal rights),Без гражданства ты на птичьих правах.,Without citizenship you're on precarious terms.

Note: The single word птичий became the collocation на пти́чьих права́х because that's how it was used in context.

スコア

総合スコア

40/100

リポジトリの品質指標に基づく評価

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

0/10
説明文

100文字以上の説明がある

0/10
人気

GitHub Stars 100以上

0/15
最近の活動

3ヶ月以内に更新がある

0/10
フォーク

10回以上フォークされている

0/5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

+5
タグ

1つ以上のタグが設定されている

0/5

レビュー

💬

レビュー機能は近日公開予定です