| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn turned back [back]" | | 1 | "Eva asked again [again]" |
| | dialogueSentences | 69 | | tagDensity | 0.391 | | leniency | 0.783 | | rawRatio | 0.074 | | effectiveRatio | 0.058 | |
| 89.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1489 | | totalAiIsmAdverbs | 3 | | 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) | |
| 46.27% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1489 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "echoed" | | 1 | "dancing" | | 2 | "pumping" | | 3 | "weight" | | 4 | "silk" | | 5 | "gleaming" | | 6 | "perfect" | | 7 | "familiar" | | 8 | "pulse" | | 9 | "profound" | | 10 | "etched" | | 11 | "traced" | | 12 | "gloom" | | 13 | "silence" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
|
| | highlights | | 0 | "clenched frostbitten fist" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 129 | | matches | (empty) | |
| 98.56% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 170 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1480 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 48.45% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1034 | | uniqueNames | 13 | | maxNameDensity | 2.03 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Miller | 16 | | Male | 1 | | Iron | 1 | | Shoreditch | 1 | | British | 1 | | Museum | 1 | | Aurora | 1 | | Kowalski | 1 | | Veil | 2 | | Market | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Museum" | | 4 | "Kowalski" |
| | places | | 0 | "Shoreditch" | | 1 | "British" | | 2 | "London" |
| | globalScore | 0.485 | | windowScore | 0.5 | |
| 23.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 4 | | matches | | 0 | "smelled like wet penny and ozone" | | 1 | "tasted like three years ago" | | 2 | "felt like a countdown" | | 3 | "spiral that seemed to draw the torchlight into itself" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.676 | | wordCount | 1480 | | matches | | 0 | "not from fire, but froze-burnt" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 170 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 21.76 | | std | 18 | | cv | 0.827 | | sampleLengths | | 0 | 37 | | 1 | 28 | | 2 | 72 | | 3 | 16 | | 4 | 28 | | 5 | 35 | | 6 | 3 | | 7 | 35 | | 8 | 64 | | 9 | 3 | | 10 | 12 | | 11 | 42 | | 12 | 4 | | 13 | 27 | | 14 | 2 | | 15 | 9 | | 16 | 6 | | 17 | 10 | | 18 | 55 | | 19 | 28 | | 20 | 30 | | 21 | 1 | | 22 | 18 | | 23 | 25 | | 24 | 60 | | 25 | 17 | | 26 | 3 | | 27 | 17 | | 28 | 7 | | 29 | 44 | | 30 | 28 | | 31 | 9 | | 32 | 25 | | 33 | 5 | | 34 | 30 | | 35 | 5 | | 36 | 65 | | 37 | 21 | | 38 | 7 | | 39 | 5 | | 40 | 2 | | 41 | 13 | | 42 | 32 | | 43 | 14 | | 44 | 42 | | 45 | 8 | | 46 | 2 | | 47 | 38 | | 48 | 2 | | 49 | 22 |
| |
| 99.82% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 129 | | matches | | 0 | "were charred" | | 1 | "was gone" |
| |
| 55.86% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 185 | | matches | | 0 | "was getting" | | 1 | "were falling" | | 2 | "was getting" | | 3 | "were leaving" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 170 | | ratio | 0.012 | | matches | | 0 | "It was verdigris—copper oxidation." | | 1 | "She tapped the face of her leather watch three times—a nervous tic she couldn't break." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1042 | | adjectiveStacks | 1 | | stackExamples | | 0 | "slim, leather-bound notebook" |
| | adverbCount | 31 | | adverbRatio | 0.029750479846449136 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0067178502879078695 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 170 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 170 | | mean | 8.71 | | std | 5.89 | | cv | 0.676 | | sampleLengths | | 0 | 16 | | 1 | 21 | | 2 | 7 | | 3 | 13 | | 4 | 8 | | 5 | 24 | | 6 | 1 | | 7 | 1 | | 8 | 13 | | 9 | 19 | | 10 | 7 | | 11 | 7 | | 12 | 6 | | 13 | 6 | | 14 | 4 | | 15 | 16 | | 16 | 3 | | 17 | 9 | | 18 | 12 | | 19 | 5 | | 20 | 8 | | 21 | 10 | | 22 | 3 | | 23 | 22 | | 24 | 13 | | 25 | 4 | | 26 | 18 | | 27 | 12 | | 28 | 9 | | 29 | 10 | | 30 | 11 | | 31 | 3 | | 32 | 2 | | 33 | 10 | | 34 | 10 | | 35 | 10 | | 36 | 4 | | 37 | 18 | | 38 | 4 | | 39 | 7 | | 40 | 14 | | 41 | 6 | | 42 | 2 | | 43 | 2 | | 44 | 7 | | 45 | 6 | | 46 | 10 | | 47 | 31 | | 48 | 24 | | 49 | 16 |
| |
| 61.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.4294117647058823 | | totalSentences | 170 | | uniqueOpeners | 73 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 116 | | matches | (empty) | | ratio | 0 | |
| 82.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 116 | | matches | | 0 | "She snapped the wrist of" | | 1 | "His shadow retreated from the" | | 2 | "He lay spread-eagled on the" | | 3 | "It smelled like wet penny" | | 4 | "She hated the smell of" | | 5 | "It tasted like three years" | | 6 | "It tasted like failure." | | 7 | "His shoes squeaked." | | 8 | "She tapped the victim's lapel." | | 9 | "She moved to the shoes" | | 10 | "She leaned in closer." | | 11 | "He looked less like a" | | 12 | "She shone her light around" | | 13 | "He looked at the floor," | | 14 | "She reached out, hovering her" | | 15 | "She returned to the body." | | 16 | "It wasn't a button." | | 17 | "It was a token." | | 18 | "She’d seen one before." | | 19 | "She stood, stripping off her" |
| | ratio | 0.345 | |
| 11.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 116 | | matches | | 0 | "Detective Harlow Quinn didn't bother" | | 1 | "She snapped the wrist of" | | 2 | "DC Miller lied" | | 3 | "His shadow retreated from the" | | 4 | "Quinn surveyed the body" | | 5 | "He lay spread-eagled on the" | | 6 | "The air here didn't smell" | | 7 | "It smelled like wet penny" | | 8 | "She hated the smell of" | | 9 | "It tasted like three years" | | 10 | "It tasted like failure." | | 11 | "Miller said, shifting his weight" | | 12 | "His shoes squeaked." | | 13 | "Quinn crouched, her knees popping" | | 14 | "She tapped the victim's lapel." | | 15 | "She moved to the shoes" | | 16 | "Quinn pointed to the silver" | | 17 | "She leaned in closer." | | 18 | "The victim's skin possessed a" | | 19 | "He looked less like a" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 116 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 1 | | matches | | 0 | "She snapped the wrist of her latex glove, the rubber creating a sharp *thwack* that echoed against the damp concrete walls." |
| |
| 32.41% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 5 | | matches | | 0 | "Quinn crouched, her knees popping with the movement" | | 1 | "Quinn stood up, her spine stiffening with military precision" | | 2 | "Quinn corrected, her voice flat" | | 3 | "Quinn said, her voice dropping an octave" | | 4 | "Quinn said, her voice cold as the frostbitten dead man below" |
| |
| 92.03% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 4 | | fancyTags | | 0 | "DC Miller lied (lie)" | | 1 | "she muttered (mutter)" | | 2 | "Quinn corrected (correct)" | | 3 | "Miller announced (announce)" |
| | dialogueSentences | 69 | | tagDensity | 0.145 | | leniency | 0.29 | | rawRatio | 0.4 | | effectiveRatio | 0.116 | |