This repository functions as a static dataset collection designed for prompt engineering and Large Language Model (LLM) evaluation. It contains no executable application logic, servers, or CLI tools. The primary data assets are structured JSON files (`prompts-zh.json`, `prompts-zh-TW.json`) and a fragmented corpus of over 600 Markdown files (`question/*.md`) containing tabular prompt data. The data lineage is direct: raw text prompts are curated into these files without ETL pipelines or transformation scripts visible in the repository. The repository serves as a benchmark and training corpus for Chinese-speaking AI users. It addresses the problem of prompt standardization and provides a wide range of task categories (classification, generation, translation, coding) for testing model capabilities. However, the data engineering structure is suboptimal; the use of 600+ individual Markdown files for a single dataset introduces unnecessary fragmentation and maintenance overhead compared to a consolidated CSV or JSON format. Maintenance activity is indicated by the provided commit timestamp (2026-04-28), suggesting the repository is actively updated. However, the content exhibits significant technical drift. The prompts heavily reference GPT-3.5 and specific time-sensitive data (stock prices, news events from 2022-2023), which reduces the long-term utility of the dataset without manual curation. The data is self-contained and requires no external dependencies to consume, making it highly reproducible for immediate use.