✨ moar static analysis
This commit is contained in:
@@ -1,7 +1,5 @@
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import { detectPassiveVoice } from './passiveVoice'
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const WEASEL_PATTERN = /\b(very|quite|rather|fairly|really|just|basically|actually|literally|clearly|obviously|simply|absolutely|totally|completely|certainly|probably|mostly|nearly|almost|somewhat|arguably|undeniably|remarkably|incredibly|extremely|highly)\b/gi
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export interface PolishDimension {
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label: string
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score: number // 0–100
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@@ -13,8 +11,15 @@ export interface PolishScore {
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dimensions: {
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passiveVoice: PolishDimension
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weaselWords: PolishDimension
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adverbDensity: PolishDimension
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filterWords: PolishDimension
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showTell: PolishDimension
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sentenceRhythm: PolishDimension
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paragraphRhythm: PolishDimension
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repeatedStarters: PolishDimension
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wordVariety: PolishDimension
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cliches: PolishDimension
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overusedWords: PolishDimension
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dialogueTags: PolishDimension
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}
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}
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@@ -28,11 +33,11 @@ function rateScore(issueCount: number, wordCount: number, cap: number): number {
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function stripMarkdown(text: string): string {
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return text
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.replace(/^#{1,6}\s+/gm, '') // headings
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.replace(/\*\*?|__?/g, '') // bold/italic
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.replace(/\[([^\]]+)\]\([^)]+\)/g, '$1') // links
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.replace(/`[^`]+`/g, '') // inline code
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.replace(/^>\s+/gm, '') // blockquotes
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.replace(/^#{1,6}\s+/gm, '')
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.replace(/\*\*?|__?/g, '')
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.replace(/\[([^\]]+)\]\([^)]+\)/g, '$1')
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.replace(/`[^`]+`/g, '')
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.replace(/^>\s+/gm, '')
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}
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function countWordsIn(text: string): number {
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@@ -41,13 +46,39 @@ function countWordsIn(text: string): number {
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}
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function splitSentences(text: string): string[] {
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return text
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.split(/(?<=[.!?])\s+/)
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.map(s => s.trim())
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.filter(s => s.length > 0)
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return text.split(/(?<=[.!?])\s+/).map(s => s.trim()).filter(s => s.length > 0)
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}
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// Sentence rhythm: coefficient of variation of sentence lengths (higher variance = better rhythm)
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// ── Weasel words ──────────────────────────────────────────────────────────────
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const WEASEL_PATTERN = /\b(very|quite|rather|fairly|really|basically|actually|literally|clearly|obviously|simply|absolutely|totally|completely|certainly|probably|mostly|nearly|almost|somewhat|arguably|undeniably|remarkably|incredibly|extremely|highly)\b/gi
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// ── Adverb density ────────────────────────────────────────────────────────────
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const ADVERB_PATTERN = /\b\w+ly\b/gi
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const ADVERB_EXCEPTIONS = new Set(['only','early','daily','likely','lonely','lovely','elderly','friendly','lively','deadly','holy','ugly','silly','hilly','belly','bully','rally','ally','jelly','fully','ully'])
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function adverbDensityScore(text: string, wordCount: number): { score: number; issueCount: number } {
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const matches = (text.match(ADVERB_PATTERN) ?? []).filter(w => !ADVERB_EXCEPTIONS.has(w.toLowerCase()))
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return { score: rateScore(matches.length, wordCount, 20), issueCount: matches.length }
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}
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// ── Filter words ──────────────────────────────────────────────────────────────
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const FILTER_PATTERN = /\b(she saw|he saw|she heard|he heard|she felt|he felt|she noticed|he noticed|she realized|he realized|she thought|he thought|she wondered|he wondered|she knew|he knew|she watched|he watched|she could see|he could see|she could hear|he could hear|she remembered|he remembered|she decided|he decided)\b/gi
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function filterWordScore(text: string, wordCount: number): { score: number; issueCount: number } {
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const matches = (text.match(FILTER_PATTERN) ?? []).length
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return { score: rateScore(matches, wordCount, 8), issueCount: matches }
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}
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// ── Show / tell proxies ───────────────────────────────────────────────────────
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const EMOTION_ADJECTIVES = 'angry|sad|happy|afraid|scared|nervous|anxious|excited|lonely|confused|surprised|disappointed|embarrassed|ashamed|jealous|tired|bored|furious|terrified|delighted|miserable|guilty|proud|content|relieved|frustrated|hopeful|desperate|bitter|disgusted|horrified|depressed|irritated|overwhelmed|resentful|heartbroken|ecstatic|joyful|melancholy|sorrowful|gloomy|cheerful|peaceful|calm|worried|tense|uneasy|weary'
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const SHOW_TELL_PATTERN = new RegExp(`\\b(was|were|is|are)\\s+(very\\s+)?(${EMOTION_ADJECTIVES})\\b`, 'gi')
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function showTellScore(text: string, wordCount: number): { score: number; issueCount: number } {
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const matches = (text.match(SHOW_TELL_PATTERN) ?? []).length
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return { score: rateScore(matches, wordCount, 10), issueCount: matches }
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}
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// ── Sentence rhythm ───────────────────────────────────────────────────────────
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function sentenceRhythmScore(text: string): { score: number; issueCount: number } {
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const sentences = splitSentences(text)
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if (sentences.length < 3) return { score: 100, issueCount: 0 }
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@@ -55,10 +86,8 @@ function sentenceRhythmScore(text: string): { score: number; issueCount: number
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const lengths = sentences.map(s => s.split(/\s+/).length)
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const mean = lengths.reduce((a, b) => a + b, 0) / lengths.length
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const variance = lengths.reduce((a, b) => a + (b - mean) ** 2, 0) / lengths.length
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const stdDev = Math.sqrt(variance)
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const cv = mean > 0 ? stdDev / mean : 0
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const cv = mean > 0 ? Math.sqrt(variance) / mean : 0
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// Count "monotonous runs": 3+ consecutive sentences within 2 words of each other in length
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let runsCount = 0
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let runLen = 1
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for (let i = 1; i < lengths.length; i++) {
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@@ -70,113 +99,176 @@ function sentenceRhythmScore(text: string): { score: number; issueCount: number
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}
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}
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// cv >= 0.6 = great rhythm; 0.3 = mediocre; < 0.3 = monotonous
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const score = Math.min(100, Math.round((cv / 0.6) * 100))
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return { score, issueCount: runsCount }
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}
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// Word variety: type-token ratio in sliding windows of 100 words
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// ── Paragraph rhythm ──────────────────────────────────────────────────────────
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function paragraphRhythmScore(text: string): { score: number; issueCount: number } {
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const paragraphs = text.split(/\n{2,}/).map(p => p.trim()).filter(p => p.length > 0)
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if (paragraphs.length < 3) return { score: 100, issueCount: 0 }
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const lengths = paragraphs.map(p => countWordsIn(p))
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const mean = lengths.reduce((a, b) => a + b, 0) / lengths.length
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const variance = lengths.reduce((a, b) => a + (b - mean) ** 2, 0) / lengths.length
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const cv = mean > 0 ? Math.sqrt(variance) / mean : 0
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// Count wall-of-text paragraphs (> 150 words) and tiny runs (≤ 5 words each)
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const walls = lengths.filter(l => l > 150).length
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const score = Math.min(100, Math.round((cv / 0.8) * 100))
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return { score, issueCount: walls }
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}
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// ── Repeated sentence starters ────────────────────────────────────────────────
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function repeatedStarterScore(text: string): { score: number; issueCount: number } {
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const sentences = splitSentences(text)
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if (sentences.length < 4) return { score: 100, issueCount: 0 }
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const starters = sentences.map(s => s.split(/\s+/)[0]?.toLowerCase().replace(/[^a-z]/g, '') ?? '')
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let runs = 0
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let runLen = 1
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for (let i = 1; i < starters.length; i++) {
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if (starters[i] === starters[i - 1] && starters[i] !== '') {
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runLen++
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if (runLen === 3) runs++
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} else {
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runLen = 1
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}
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}
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// Also penalise pronoun-heavy openings overall
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const PRONOUNS = new Set(['he','she','i','they','it','we','you'])
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const pronounStarts = starters.filter(w => PRONOUNS.has(w)).length
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const pronounRatio = pronounStarts / starters.length
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const runPenalty = Math.min(60, runs * 15)
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const pronounPenalty = Math.max(0, Math.round((pronounRatio - 0.4) / 0.4 * 40))
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const score = Math.max(0, 100 - runPenalty - pronounPenalty)
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return { score, issueCount: runs }
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}
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// ── Word variety ──────────────────────────────────────────────────────────────
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const SKIP_WORDS = new Set(['the','a','an','and','or','but','in','on','at','to','for','of','with','is','was','are','were','it','he','she','they','i','you','we','be','been','being','that','this','those','these','have','has','had','do','did','does','not','so','as','if','by','from','into','than','then','when','where','who','which','what','his','her','their','its','my','your','our','up','out','about','after','before','all','some','one','no','more','also'])
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function wordVarietyScore(text: string): { score: number; issueCount: number } {
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const words = text.toLowerCase().match(/\b[a-z']+\b/g) ?? []
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if (words.length < 20) return { score: 100, issueCount: 0 }
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const WINDOW = 100
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const SKIP_WORDS = new Set(['the','a','an','and','or','but','in','on','at','to','for','of','with','is','was','are','were','it','he','she','they','i','you','we','be','been','being','that','this','those','these','have','has','had','do','did','does','not','so','as','if','by','from','into','than','then','when','where','who','which','what','his','her','their','its','my','your','our','up','out','about','after','before','all','some','one','no','more','also'])
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const contentWords = words.filter(w => !SKIP_WORDS.has(w))
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if (contentWords.length < 20) return { score: 100, issueCount: 0 }
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const WINDOW = 100
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const ttrs: number[] = []
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for (let i = 0; i + WINDOW <= contentWords.length; i += Math.floor(WINDOW / 2)) {
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const window = contentWords.slice(i, i + WINDOW)
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const unique = new Set(window).size
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ttrs.push(unique / window.length)
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const win = contentWords.slice(i, i + WINDOW)
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ttrs.push(new Set(win).size / win.length)
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}
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const avgTTR = ttrs.reduce((a, b) => a + b, 0) / ttrs.length
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// TTR >= 0.7 = rich; 0.4 = acceptable; < 0.4 = repetitive
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const score = Math.min(100, Math.max(0, Math.round(((avgTTR - 0.4) / 0.3) * 100)))
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// Count repeated content words within 50-word windows
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let repeatedCount = 0
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for (let i = 0; i + 50 <= contentWords.length; i += 25) {
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const window = contentWords.slice(i, i + 50)
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const win = contentWords.slice(i, i + 50)
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const counts = new Map<string, number>()
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for (const w of window) counts.set(w, (counts.get(w) ?? 0) + 1)
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for (const w of win) counts.set(w, (counts.get(w) ?? 0) + 1)
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for (const [, count] of counts) if (count >= 3) repeatedCount++
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}
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const score = Math.min(100, Math.max(0, Math.round(((avgTTR - 0.4) / 0.3) * 100)))
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return { score, issueCount: repeatedCount }
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}
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// Dialogue tag quality: penalise said-bookisms and adverbs on tags
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// ── Clichés ───────────────────────────────────────────────────────────────────
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const CLICHES = [
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'heart pounding','blood ran cold','butterflies in','stomach churned','knees weak',
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'breath caught','time stood still','world fell away','tears streamed','voice cracked',
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'eyes widened','jaw dropped','skin crawled','hair stood on end','spine tingled',
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'heart sank','heart leapt','heart raced','mind went blank','hands trembled',
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'face drained','colour drained','blood froze','heart stopped','chest tightened',
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'lump in her throat','lump in his throat','pit of her stomach','pit of his stomach',
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'at the end of the day','all hell broke loose','couldn\'t believe her eyes',
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'couldn\'t believe his eyes','needle in a haystack','tip of the iceberg',
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'dead of night','ray of hope','last but not least','speak of the devil',
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'cold as ice','sharp as a knife','black as night','white as snow','red as blood',
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'silence was deafening','deafening silence','elephant in the room',
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]
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const CLICHE_PATTERN = new RegExp(CLICHES.map(c => c.replace(/[.*+?^${}()|[\]\\]/g, '\\$&')).join('|'), 'gi')
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function clicheScore(text: string, wordCount: number): { score: number; issueCount: number } {
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const matches = (text.match(CLICHE_PATTERN) ?? []).length
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return { score: rateScore(matches, wordCount, 6), issueCount: matches }
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}
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// ── Overused filler words ─────────────────────────────────────────────────────
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const OVERUSED_PATTERN = /\b(just|suddenly|started to|began to|that|somehow|somehow|anyway|whatever|stuff|things|got|get)\b/gi
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function overusedWordScore(text: string, wordCount: number): { score: number; issueCount: number } {
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const matches = (text.match(OVERUSED_PATTERN) ?? []).length
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return { score: rateScore(matches, wordCount, 25), issueCount: matches }
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}
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// ── Dialogue tags ─────────────────────────────────────────────────────────────
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const SAID_BOOKISMS = /\b(hissed|snapped|exclaimed|barked|spat|snarled|growled|shrieked|whined|croaked|breathed|murmured)\b/gi
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const ADV_TAG = /\b(said|asked|replied|whispered)\s+\w+ly\b/gi
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function dialogueTagScore(text: string, wordCount: number): { score: number; issueCount: number } {
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const bookisms = (text.match(SAID_BOOKISMS) ?? []).length
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const advTags = (text.match(ADV_TAG) ?? []).length
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const total = bookisms + advTags
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const total = (text.match(SAID_BOOKISMS) ?? []).length + (text.match(ADV_TAG) ?? []).length
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return { score: rateScore(total, wordCount, 8), issueCount: total }
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}
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// ── Main export ───────────────────────────────────────────────────────────────
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export function computePolishScore(rawText: string): PolishScore {
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const text = stripMarkdown(rawText)
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const wordCount = countWordsIn(text)
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const skip = wordCount <= 10
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const passiveIssues = wordCount > 10 ? detectPassiveVoice(text).length : 0
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const weaselIssues = wordCount > 10 ? (text.match(WEASEL_PATTERN) ?? []).length : 0
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const dim = (label: string, score: number, issueCount: number): PolishDimension => ({ label, score, issueCount })
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const passive: PolishDimension = {
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label: 'Passive voice',
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score: rateScore(passiveIssues, wordCount, 12),
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issueCount: passiveIssues
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}
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const passiveIssues = skip ? 0 : detectPassiveVoice(text).length
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const weaselIssues = skip ? 0 : (text.match(WEASEL_PATTERN) ?? []).length
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const weasel: PolishDimension = {
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label: 'Weasel words',
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score: rateScore(weaselIssues, wordCount, 10),
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issueCount: weaselIssues
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}
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const adverb = skip ? { score: 100, issueCount: 0 } : adverbDensityScore(text, wordCount)
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const filter = skip ? { score: 100, issueCount: 0 } : filterWordScore(text, wordCount)
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const showTell = skip ? { score: 100, issueCount: 0 } : showTellScore(text, wordCount)
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const sRhythm = skip ? { score: 100, issueCount: 0 } : sentenceRhythmScore(text)
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const pRhythm = skip ? { score: 100, issueCount: 0 } : paragraphRhythmScore(text)
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const starters = skip ? { score: 100, issueCount: 0 } : repeatedStarterScore(text)
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const variety = skip ? { score: 100, issueCount: 0 } : wordVarietyScore(text)
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const cliche = skip ? { score: 100, issueCount: 0 } : clicheScore(text, wordCount)
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const overused = skip ? { score: 100, issueCount: 0 } : overusedWordScore(text, wordCount)
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const dialogue = skip ? { score: 100, issueCount: 0 } : dialogueTagScore(text, wordCount)
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const rhythmResult = sentenceRhythmScore(text)
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const rhythm: PolishDimension = {
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label: 'Sentence rhythm',
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score: rhythmResult.score,
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issueCount: rhythmResult.issueCount
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}
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const varietyResult = wordVarietyScore(text)
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const variety: PolishDimension = {
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label: 'Word variety',
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score: varietyResult.score,
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issueCount: varietyResult.issueCount
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}
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const dialogueResult = dialogueTagScore(text, wordCount)
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const dialogue: PolishDimension = {
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label: 'Dialogue tags',
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score: dialogueResult.score,
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issueCount: dialogueResult.issueCount
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}
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const weights = { passive: 0.2, weasel: 0.2, rhythm: 0.25, variety: 0.2, dialogue: 0.15 }
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const overall = Math.round(
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passive.score * weights.passive +
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weasel.score * weights.weasel +
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rhythm.score * weights.rhythm +
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variety.score * weights.variety +
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dialogue.score * weights.dialogue
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)
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const scores = [
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rateScore(passiveIssues, wordCount, 12),
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rateScore(weaselIssues, wordCount, 10),
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adverb.score,
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filter.score,
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showTell.score,
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sRhythm.score,
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pRhythm.score,
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starters.score,
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variety.score,
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cliche.score,
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overused.score,
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dialogue.score,
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]
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const overall = Math.round(scores.reduce((a, b) => a + b, 0) / scores.length)
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return {
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overall,
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dimensions: {
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passiveVoice: passive,
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weaselWords: weasel,
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sentenceRhythm: rhythm,
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wordVariety: variety,
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dialogueTags: dialogue
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passiveVoice: dim('Passive voice', rateScore(passiveIssues, wordCount, 12), passiveIssues),
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weaselWords: dim('Weasel words', rateScore(weaselIssues, wordCount, 10), weaselIssues),
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adverbDensity: dim('Adverb density', adverb.score, adverb.issueCount),
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filterWords: dim('Filter words', filter.score, filter.issueCount),
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showTell: dim('Show / tell', showTell.score, showTell.issueCount),
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sentenceRhythm: dim('Sentence rhythm', sRhythm.score, sRhythm.issueCount),
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paragraphRhythm: dim('Paragraph rhythm', pRhythm.score, pRhythm.issueCount),
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repeatedStarters: dim('Sentence starters', starters.score, starters.issueCount),
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wordVariety: dim('Word variety', variety.score, variety.issueCount),
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cliches: dim('Clichés', cliche.score, cliche.issueCount),
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overusedWords: dim('Filler words', overused.score, overused.issueCount),
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dialogueTags: dim('Dialogue tags', dialogue.score, dialogue.issueCount),
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}
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}
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}
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