AI Expert Analysis and Writing

What problem it addresses

Many technical articles open with only a hook, leaving readers unable to tell who is involved, what happened, and why it matters after the first paragraph. The body can also lack context, data, comparison, and explanation, making judgments feel thin and scenes underdeveloped. This skill provides an executable writing constraint set. Its goal is not to insert framework labels, but to force evidence checks before drafting and produce a clear structure in the final draft.

How it works

• Pre-writing analysis: Validate judgments with SMART to check for data and time points, use PEST to cover at least three dimensions among economy, society, technology, and policy, and prepare comparison and scene lists.
• Writing structure: The opening must state who, what was done, and why it matters. Middle paragraphs should combine problem, data, comparison, and explanation. The ending should provide two judgments and one open question.
• Paragraph density: Each paragraph should aim for context -> data -> comparison -> explanation, include at least one time, competitor, numeric, or logical comparison, and keep one information-dense line every one or two paragraphs.
• Draft archiving: Saving is triggered when the article has a title, meets the word threshold, or is content-complete, reducing loss from relying on conversation history.

Boundaries

It suits technical commentary, product analysis, industry observation, and long-form public account writing where argument and scene detail matter. It still requires solid source material and does not replace fact checking, and it is less suitable for short messages, clickbait titles, or pure marketing copy.