annotate

Main text annotation and pacing visualization (annotate)

X-ray a chapter's main text: where the hooks are, how many payoff moments there are, which paragraph the climax is in, whether there are enough modifier words. Let the authorsee their pacing data at a glance。

When to use

When you want to see "whether this chapter has enough payoff moments, where the hooks are, and whether the pacing is right"
Mark/check modifier words (the "flavor" words of short, punchy, fast-paced satisfying fiction)
Do a pre-publication visualized health check for a chapter
and review difference:review is the macro five-dimensional diagnosis of an entire volume/entire book;annotate ismicro-level annotation of a single chapter paragraph by paragraph

Workflow

Step 1: Annotate paragraph by paragraph (output tagged main text)

Annotate the user's main text by paragraph, inserting tags inline:

【钩】 Hook (opening hook/chapter-end hook/paragraph suspense)
【爽】 Payoff moment (counterattack/face-slapping/crushing, etc., tune references/trope-library.md）
【高潮】 this chapter's emotional peak
【脸】 Face-slapping scene (mark the four beats: taunt/silence/crush/onlookers)
【伏】 Foreshadowing placement
【修】 Modifier words (mark the intensifier words/onlooker reaction words already used, tune references/xiushi-ci.md）
【缓】 pacing relaxation segment

Step 2: Output the pacing data table

【本章体检表】
字数：____
钩子：开篇钩 ✓/✗　章尾钩 ✓/✗　段落悬念 __ 个
爽点：__ 个（类型：打脸/逆袭/…）｜密度评价：达标/偏少（按平台）
高潮：在第 __ 段｜情绪强度：__/5
打脸四拍：嘲讽__ 沉默__ 碾压__ 围观__（缺哪拍标出）
修饰词：强化词__ 围观反应__ ｜密度：合适/偏少/过载(油腻)
伏笔：埋__ 个　回收__ 个
节奏曲线：紧—缓—紧 是否有呼吸感
AI 味（粗判）：比喻密度/连续同句式/句长波动 有无异常 → 要量化评分用 `aidetect`

Step 3: Give improvement suggestions

Paragraphs with a payoff gap → suggest what to add (point to the paragraph)
Face-slapping is missing the "onlookers" beat → suggest adding onlooker reaction words (give 2-3 candidates, tune xiushi-ci.md）
Payoff sentences with too few modifier words → suggest which sentence to add what word to
Paragraphs with modifier overload → suggest cutting down to 2-3 (avoid greasiness/AI flavor)
Chapter ending is bland → suggest which kind of chapter-end hook to switch to (tune hook-library.md）

Principles

Annotations shouldland on paragraphs/sentences, not be vague.
Modifier word suggestions follow "sparse use, concentrated at peaks, differs by channel" (see references/xiushi-ci.md）。
Judge density by platform: Tomato should be dense, Qidian can be sparse (tune references/platform-profiles.md）。
Male-oriented fiction emphasizes "fierce/satisfying/inspiring" intensifier words and onlooker reactions, while female-oriented fiction emphasizes expressions/emotion words.

Next step

Based on the annotations → use expand to rewrite weak paragraphs,shuangdian to add face-slapping,deslop to clean up overloaded modifier words; for AI-flavor quantified scoring + localization → aidetect。