| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 3 | | adverbTags | | 0 | "He gestured again [again]" | | 1 | "She gestured around [around]" | | 2 | "Harlow turned back [back]" |
| | dialogueSentences | 23 | | tagDensity | 0.522 | | leniency | 1 | | rawRatio | 0.25 | | effectiveRatio | 0.25 | |
| 73.61% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1137 | | totalAiIsmAdverbs | 6 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | |
| | highlights | | 0 | "deliberately" | | 1 | "slightly" | | 2 | "carefully" | | 3 | "gently" | | 4 | "suddenly" |
| |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1137 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | word | "down her spine" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "familiar" | | 1 | "chill" | | 2 | "down her spine" | | 3 | "shattered" | | 4 | "furrowed" | | 5 | "scanned" | | 6 | "whisper" | | 7 | "silence" | | 8 | "dancing" | | 9 | "echoing" | | 10 | "etched" | | 11 | "quivered" | | 12 | "intensity" | | 13 | "weight" | | 14 | "vibrated" | | 15 | "echoed" | | 16 | "flickered" | | 17 | "maw" | | 18 | "hulking" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 88 | | matches | (empty) | |
| 94.16% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1135 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.04% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 916 | | uniqueNames | 12 | | maxNameDensity | 1.42 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Underground | 1 | | Detective | 2 | | Harlow | 13 | | Quinn | 1 | | Strap | 1 | | Morris | 1 | | Sergeant | 1 | | Davies | 9 | | Veil | 4 | | Market | 2 | | Compass | 2 | | Undergound | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Strap" | | 3 | "Morris" | | 4 | "Sergeant" | | 5 | "Davies" | | 6 | "Compass" |
| | places | | | globalScore | 0.79 | | windowScore | 0.833 | |
| 34.62% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 3 | | matches | | 0 | "smelled like old blood, but wrong, too cle" | | 1 | "seemed permanently strained across his navel, looked up from the chalk outline near the tracks" | | 2 | "looked like dried leaves, out of place in" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.881 | | wordCount | 1135 | | matches | | 0 | "not north, but down a dark, unlit tunnel leading further into the bowels of" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 54.05 | | std | 35.57 | | cv | 0.658 | | sampleLengths | | 0 | 124 | | 1 | 28 | | 2 | 52 | | 3 | 82 | | 4 | 8 | | 5 | 43 | | 6 | 90 | | 7 | 50 | | 8 | 22 | | 9 | 139 | | 10 | 17 | | 11 | 34 | | 12 | 43 | | 13 | 29 | | 14 | 84 | | 15 | 12 | | 16 | 7 | | 17 | 71 | | 18 | 72 | | 19 | 74 | | 20 | 54 |
| |
| 81.34% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 88 | | matches | | 0 | "been dragged" | | 1 | "was etched" | | 2 | "was curled" | | 3 | "was followed" | | 4 | "being dragged" | | 5 | "been mugged" | | 6 | "been *taken" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 139 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 98 | | ratio | 0 | | matches | (empty) | |
| 88.71% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 918 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.05119825708061002 | | lyAdverbCount | 20 | | lyAdverbRatio | 0.02178649237472767 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 11.58 | | std | 6.82 | | cv | 0.589 | | sampleLengths | | 0 | 21 | | 1 | 14 | | 2 | 4 | | 3 | 10 | | 4 | 19 | | 5 | 18 | | 6 | 3 | | 7 | 17 | | 8 | 2 | | 9 | 6 | | 10 | 10 | | 11 | 13 | | 12 | 15 | | 13 | 23 | | 14 | 12 | | 15 | 17 | | 16 | 12 | | 17 | 28 | | 18 | 21 | | 19 | 21 | | 20 | 4 | | 21 | 4 | | 22 | 6 | | 23 | 25 | | 24 | 12 | | 25 | 9 | | 26 | 10 | | 27 | 14 | | 28 | 9 | | 29 | 6 | | 30 | 15 | | 31 | 15 | | 32 | 7 | | 33 | 5 | | 34 | 8 | | 35 | 18 | | 36 | 5 | | 37 | 14 | | 38 | 5 | | 39 | 7 | | 40 | 15 | | 41 | 9 | | 42 | 16 | | 43 | 13 | | 44 | 12 | | 45 | 11 | | 46 | 6 | | 47 | 3 | | 48 | 13 | | 49 | 11 |
| |
| 56.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.40816326530612246 | | totalSentences | 98 | | uniqueOpeners | 40 | |
| 78.43% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 85 | | matches | | 0 | "Nearly to the day, since" | | 1 | "Suddenly, a deafening clang echoed" |
| | ratio | 0.024 | |
| 83.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 85 | | matches | | 0 | "Her sharp jaw tightened." | | 1 | "It smelled like old blood," | | 2 | "She ignored him." | | 3 | "She’d learned to modulate it" | | 4 | "He gestured with a gloved" | | 5 | "His eyes, wide and glassy," | | 6 | "He gestured again at the" | | 7 | "She held up a hand," | | 8 | "Her eyes, sharp and brown," | | 9 | "She’d seen it before, three" | | 10 | "she murmured, her voice barely" | | 11 | "She ran a fingertip along" | | 12 | "It wasn't a knife mark." | | 13 | "It was too wide, too" | | 14 | "She rose, turning her back" | | 15 | "She pulled a small, tarnished" | | 16 | "Its face was etched with" | | 17 | "It quivered slightly in her" | | 18 | "She gestured around them" | | 19 | "She gestured for the forensics" |
| | ratio | 0.341 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 85 | | matches | | 0 | "The air in the abandoned" | | 1 | "something metallic and acrid that" | | 2 | "Her sharp jaw tightened." | | 3 | "It smelled like old blood," | | 4 | "A uniformed officer, barely more" | | 5 | "She ignored him." | | 6 | "Years of military precision had" | | 7 | "The thought sent a familiar" | | 8 | "Harlow’s voice was a low" | | 9 | "She’d learned to modulate it" | | 10 | "Detective Sergeant Davies, a portly" | | 11 | "He gestured with a gloved" | | 12 | "Harlow strode deliberately towards the" | | 13 | "Debris littered the platform, mostly" | | 14 | "The victim, a man of" | | 15 | "His eyes, wide and glassy," | | 16 | "Harlow’s brow furrowed" | | 17 | "Davies shrugged, a slow, rolling" | | 18 | "He gestured again at the" | | 19 | "Harlow crouched, her movements fluid" |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 2 | | matches | | 0 | "Debris littered the platform, mostly old newspapers and shattered glass, but there were deeper gouges in the concrete around the outline, as if something heavy …" | | 1 | "The flickering generator lights threw dancing shadows that played tricks on the eyes." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 4 | | matches | | 0 | "He gestured, his movements ponderous" | | 1 | "she murmured, her voice barely audible" | | 2 | "Harlow repeated, a grim amusement touching her lips" | | 3 | "Harlow asked, her voice hardening with a new intensity" |
| |
| 19.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 3 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Harlow repeated (repeat)" | | 2 | "Harlow roared (roar)" |
| | dialogueSentences | 23 | | tagDensity | 0.174 | | leniency | 0.348 | | rawRatio | 0.75 | | effectiveRatio | 0.261 | |