| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "a Tuesday night looking like [like]" |
| | dialogueSentences | 60 | | tagDensity | 0.267 | | leniency | 0.533 | | rawRatio | 0.063 | | effectiveRatio | 0.033 | |
| 96.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1393 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 82.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1393 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanning" | | 1 | "weight" | | 2 | "navigate" | | 3 | "flicker" | | 4 | "silence" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 79 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1409 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 825 | | uniqueNames | 12 | | maxNameDensity | 1.33 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Rory | 4 | | Eva | 3 | | Moreau | 1 | | Ptolemy | 2 | | Lucien | 11 | | Watched | 1 | | Tuesday | 2 | | East | 1 | | London | 1 | | Evan | 1 | | Professional | 1 | | Three | 3 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Moreau" | | 3 | "Ptolemy" | | 4 | "Lucien" | | 5 | "Evan" |
| | places | | | globalScore | 0.833 | | windowScore | 1 | |
| 38.89% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like he'd walked out of a magazine" | | 1 | "looked like he'd stepped out of a boardro" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.71 | | wordCount | 1409 | | matches | | 0 | "not just from me, but from everything I offered afterward" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 122 | | matches | | 0 | "chose that moment" | | 1 | "hated that he" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 22.73 | | std | 20.48 | | cv | 0.901 | | sampleLengths | | 0 | 50 | | 1 | 5 | | 2 | 57 | | 3 | 1 | | 4 | 4 | | 5 | 24 | | 6 | 1 | | 7 | 24 | | 8 | 34 | | 9 | 5 | | 10 | 23 | | 11 | 10 | | 12 | 6 | | 13 | 68 | | 14 | 9 | | 15 | 21 | | 16 | 6 | | 17 | 43 | | 18 | 4 | | 19 | 3 | | 20 | 57 | | 21 | 55 | | 22 | 7 | | 23 | 9 | | 24 | 2 | | 25 | 39 | | 26 | 23 | | 27 | 5 | | 28 | 55 | | 29 | 6 | | 30 | 52 | | 31 | 14 | | 32 | 21 | | 33 | 3 | | 34 | 2 | | 35 | 13 | | 36 | 10 | | 37 | 49 | | 38 | 35 | | 39 | 9 | | 40 | 56 | | 41 | 9 | | 42 | 15 | | 43 | 64 | | 44 | 3 | | 45 | 1 | | 46 | 7 | | 47 | 22 | | 48 | 47 | | 49 | 15 |
| |
| 96.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 79 | | matches | | 0 | "was smoothed" | | 1 | "being told" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 148 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 122 | | ratio | 0.098 | | matches | | 0 | "She'd been mid-sentence in her head — rehearsing what to say to Eva about the overdue rent — when the knock came." | | 1 | "His mismatched eyes — that unsettling amber and black — lifted to meet hers." | | 2 | "Lucien moved past her into the flat with the kind of ease that made her teeth ache — like he belonged in cramped spaces surrounded by other people's research, like the clutter of Eva's life was something he could navigate without disturbing." | | 3 | "She crossed her arms and leaned against the kitchen counter, watching him take in the flat — the towers of linguistics texts, the corkboard pinned with news clippings in three languages, the sleeping bag rumpled on the sofa where Eva crashed most nights." | | 4 | "Watched the micro-expression — the tightening around his mouth, the brief flicker of something that might have been concern before it was smoothed away." | | 5 | "Without the coat, his suit was immaculate as ever — fitted, dark, wrong for a rain-soaked Tuesday in East London." | | 6 | "She laughed — short, brittle, nothing like a real laugh." | | 7 | "Rain dripped from his hair — that ridiculous platinum hair, slicked back and barely disturbed — and landed on the shoulder of his jacket." | | 8 | "\"—nine forty-five on a Tuesday night, looking like you just came from accepting a BAFTA.\"" | | 9 | "Not soft — Lucien's voice never went soft — but lower, more controlled." | | 10 | "Rory felt the crescent scar on her wrist itch — that old ghost-pain that surfaced whenever Evan's name came up, whenever the past pressed too close." | | 11 | "For a moment, he looked almost human — almost like a man standing in a rain-soaked jacket in a flat that smelled of curry, being told things he didn't want to hear." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 839 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.028605482717520857 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0023837902264600714 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 11.55 | | std | 9.95 | | cv | 0.861 | | sampleLengths | | 0 | 20 | | 1 | 22 | | 2 | 3 | | 3 | 5 | | 4 | 5 | | 5 | 38 | | 6 | 10 | | 7 | 3 | | 8 | 6 | | 9 | 1 | | 10 | 4 | | 11 | 20 | | 12 | 4 | | 13 | 1 | | 14 | 22 | | 15 | 2 | | 16 | 10 | | 17 | 6 | | 18 | 14 | | 19 | 4 | | 20 | 5 | | 21 | 18 | | 22 | 5 | | 23 | 6 | | 24 | 4 | | 25 | 6 | | 26 | 8 | | 27 | 4 | | 28 | 2 | | 29 | 30 | | 30 | 9 | | 31 | 2 | | 32 | 8 | | 33 | 5 | | 34 | 9 | | 35 | 9 | | 36 | 5 | | 37 | 7 | | 38 | 6 | | 39 | 5 | | 40 | 34 | | 41 | 4 | | 42 | 4 | | 43 | 3 | | 44 | 42 | | 45 | 15 | | 46 | 6 | | 47 | 6 | | 48 | 43 | | 49 | 7 |
| |
| 56.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3770491803278688 | | totalSentences | 122 | | uniqueOpeners | 46 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 72 | | matches | | 0 | "She'd been mid-sentence in her" | | 1 | "She pulled the door open." | | 2 | "He looked like he'd walked" | | 3 | "He always did." | | 4 | "His gaze moved past her" | | 5 | "His mismatched eyes — that" | | 6 | "He straightened, adjusting his cuff" | | 7 | "She gripped the door's edge" | | 8 | "Her knuckles went white." | | 9 | "She'd told herself that was" | | 10 | "She felt her jaw tighten." | | 11 | "She stared at him." | | 12 | "She stepped aside." | | 13 | "He set his cane against" | | 14 | "She didn't offer to take" | | 15 | "She didn't offer him a" | | 16 | "She crossed her arms and" | | 17 | "She watched his face" | | 18 | "He looked like he'd stepped" | | 19 | "He took a step closer" |
| | ratio | 0.5 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 72 | | matches | | 0 | "The third deadbolt stuck the" | | 1 | "She'd been mid-sentence in her" | | 2 | "The kind that didn't falter." | | 3 | "She pulled the door open." | | 4 | "Lucien Moreau stood in the" | | 5 | "He looked like he'd walked" | | 6 | "He always did." | | 7 | "That was part of the" | | 8 | "His gaze moved past her" | | 9 | "Ptolemy chose that moment to" | | 10 | "Lucien crouched, one gloved hand" | | 11 | "The motion was precise, almost" | | 12 | "His mismatched eyes — that" | | 13 | "He straightened, adjusting his cuff" | | 14 | "Something cold moved through Rory's" | | 15 | "She gripped the door's edge" | | 16 | "Her knuckles went white." | | 17 | "She'd told herself that was" | | 18 | "The name landed like a" | | 19 | "She felt her jaw tighten." |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 4 | | matches | | 0 | "Lucien moved past her into the flat with the kind of ease that made her teeth ache — like he belonged in cramped spaces surrounded by other people's research, l…" | | 1 | "Watched the micro-expression — the tightening around his mouth, the brief flicker of something that might have been concern before it was smoothed away." | | 2 | "Rory felt the crescent scar on her wrist itch — that old ghost-pain that surfaced whenever Evan's name came up, whenever the past pressed too close." | | 3 | "For a moment, he looked almost human — almost like a man standing in a rain-soaked jacket in a flat that smelled of curry, being told things he didn't want to h…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 60 | | tagDensity | 0.017 | | leniency | 0.033 | | rawRatio | 0 | | effectiveRatio | 0 | |