English Vocabulary Deep Explanation Assistant

The problem it addresses

When learning English vocabulary, the usual friction is not finding a definition, but getting an explanation that is too shallow: a learner may know that a word can mean "to look at," yet miss how the root spect connects inspect, respect, and spectacular; or know that a word has multiple meanings, but not which sense appears frequently in CET, postgraduate entrance exams, IELTS, or TOEFL. vocabulary-explanation is built for structured word explanation, turning a single English word or a batch of vocabulary into a reusable study document that covers pronunciation, roots, meanings, derivatives, near-synonyms, confusables, phrases, and memory hooks.

How it works

The skill supports two working modes:

• Single-word deep explanation: for one English word, it produces a fixed structure with basic information, pronunciation guidance, root and etymology analysis, detailed multiple meanings, derivatives, related root words, near-synonym distinctions, confusable words, synonyms and antonyms, common phrases, and memory techniques.
• Batch vocabulary explanation: for a word list or a set of unknown words from an article, it generates one combined document. It starts with the vocabulary list, then creates a card for each word, including a meaning summary, exam-frequency markings, derivatives, and a short memory tip.

The key output constraints are practical: core meanings should include both a simple sentence and a CET or postgraduate-exam-level advanced sentence; high-frequency exam meanings are marked with 🌟 and the corresponding exam; near-synonym notes should state the semantic, register, and collocation differences; when a section has no suitable content, the output should say "no obvious [category]" rather than forcing content.

Boundary and notes

This skill is useful for exam-oriented vocabulary study, flashcard creation, and review notes. It is not a full curriculum planner or speaking coach. For proper nouns, slang, archaic usage, or highly context-dependent expressions, quality depends on the context supplied by the prompt. Batch mode is better suited to a limited word list; very large lists should be split into smaller batches to keep each entry sufficiently concrete.