classify-documents-by-subject-content

name	Classify Documents by Subject Content
description	Use this skill to analyze extracted text content from documents and classify them into the correct subject folder based on keyword matching and content analysis.

When to use this skill

• After extracting content from PDF, PPTX, or DOCX files
• To determine which of the 5 subject folders a document belongs to
• Before moving files to their destination folders

Classification rules

Subject-specific keyword sets

LLM (Large Language Models)

• Keywords: "language model", "transformer", "neural network", "NLP", "large language model", "BERT", "GPT", "attention mechanism", "embedding", "tokenization", "natural language processing", "deep learning"
• Context: Papers about AI models trained on text data, language processing, machine learning models for text

Trapped ion and quantum computing

• Keywords: "trapped ion", "quantum computer", "qubit", "quantum gate", "quantum algorithm", "quantum circuit", "ion trap", "quantum computing", "quantum simulation", "quantum error correction"
• Context: Papers about quantum computing systems, quantum information processing, trapped ion technologies

Black hole

• Keywords: "black hole", "event horizon", "gravitational", "spacetime", "Hawking", "relativity", "general relativity", "singularity", "gravitational wave", "astrophysics", "cosmology"
• Context: Papers about gravitational physics, cosmology, astrophysical objects and phenomena

DNA

• Keywords: "DNA", "genetics", "gene", "molecular biology", "sequence", "protein", "genetic", "genomics", "mutation", "chromosome", "RNA", "amino acid"
• Context: Papers about biological molecules, genetics, molecular biology, genomics, biological sequences

Music history

• Keywords: "music", "composer", "symphony", "song", "musical", "historical", "concert", "notation", "harmony", "melody", "rhythm", "performance", "musician"
• Context: Papers or documents about music, composers, historical musical analysis, musical theory in historical context

Classification process

1. Extract key phrases from the document's title, abstract, and first 500 words
2. Count keyword matches for each of the 5 subject categories
3. Identify dominant subject: The category with the most keyword matches
4. Check context: Read surrounding sentences to confirm the subject matches
5. Handle ambiguous cases:
   • If two subjects match equally, read more content
   • If still unclear, examine introduction/abstract more carefully
   • Only assign to music_history as fallback if truly cannot match the other 4 categories

Confidence assessment

• High confidence: 5+ keywords match from one category, and no other category has more than 1-2 matches
• Medium confidence: 3-4 keywords match, but need to verify with context reading
• Low confidence: Only 1-2 keywords match; read full abstract/introduction before deciding

Decision output

For each document, record:

• Filename
• Detected subject
• Confidence level (high/medium/low)
• Key matching keywords