| 25.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "the bartender lied smoothly [smoothly]" |
| | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.25 | | effectiveRatio | 0.25 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 877 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 82.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 877 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "glinting" | | 1 | "stomach" | | 2 | "pulse" |
| |
| 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 | 68 | | matches | (empty) | |
| 58.82% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 68 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "seemed to" | | 2 | "happened to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 860 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.95% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 793 | | uniqueNames | 13 | | maxNameDensity | 1.26 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 10 | | Raven | 1 | | Nest | 1 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | Camden | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | | globalScore | 0.869 | | windowScore | 1 | |
| 59.09% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like carved bone—and slipped it in" | | 1 | "looked like human skin" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 860 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 72 | | matches | | |
| 54.20% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 47.78 | | std | 16.23 | | cv | 0.34 | | sampleLengths | | 0 | 56 | | 1 | 73 | | 2 | 42 | | 3 | 18 | | 4 | 56 | | 5 | 56 | | 6 | 58 | | 7 | 57 | | 8 | 59 | | 9 | 42 | | 10 | 78 | | 11 | 44 | | 12 | 24 | | 13 | 22 | | 14 | 40 | | 15 | 39 | | 16 | 35 | | 17 | 61 |
| |
| 94.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 68 | | matches | | 0 | "was honeyed" | | 1 | "were gone" |
| |
| 61.11% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 144 | | matches | | 0 | "was heading" | | 1 | "was speaking" | | 2 | "was saying" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 72 | | ratio | 0.097 | | matches | | 0 | "The suspect—some low-level dealer she'd been tailing for weeks—had finally led her somewhere interesting." | | 1 | "Every eye in the place turned toward her—some curious, most hostile." | | 2 | "Twenty-three minutes later—she counted—a figure slipped out the back." | | 3 | "The former paramedic had crossed her path before—always on the edges of cases that never quite added up." | | 4 | "Quinn watched from behind a stack of pallets as he pulled something from his pocket—a small white object that looked like carved bone—and slipped it into a nearly invisible slot beside the door." | | 5 | "The air smelled of damp stone and something sharper—ozone and burnt herbs." | | 6 | "She'd seen that gesture before—right before things went sideways in an interrogation room two years back." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 827 | | adjectiveStacks | 1 | | stackExamples | | 0 | "other over bad station" |
| | adverbCount | 26 | | adverbRatio | 0.03143893591293833 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010882708585247884 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 11.94 | | std | 6.08 | | cv | 0.509 | | sampleLengths | | 0 | 17 | | 1 | 17 | | 2 | 14 | | 3 | 8 | | 4 | 11 | | 5 | 14 | | 6 | 26 | | 7 | 4 | | 8 | 18 | | 9 | 16 | | 10 | 11 | | 11 | 5 | | 12 | 10 | | 13 | 18 | | 14 | 14 | | 15 | 10 | | 16 | 17 | | 17 | 15 | | 18 | 4 | | 19 | 19 | | 20 | 15 | | 21 | 9 | | 22 | 3 | | 23 | 2 | | 24 | 1 | | 25 | 3 | | 26 | 23 | | 27 | 9 | | 28 | 18 | | 29 | 8 | | 30 | 21 | | 31 | 33 | | 32 | 3 | | 33 | 3 | | 34 | 18 | | 35 | 5 | | 36 | 16 | | 37 | 12 | | 38 | 5 | | 39 | 10 | | 40 | 19 | | 41 | 10 | | 42 | 3 | | 43 | 15 | | 44 | 12 | | 45 | 17 | | 46 | 13 | | 47 | 5 | | 48 | 8 | | 49 | 8 |
| |
| 80.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5138888888888888 | | totalSentences | 72 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 66 | | matches | | 0 | "Right to the doorstep of" | | 1 | "Just around the corner, into" | | 2 | "Somewhere in this den of" |
| | ratio | 0.045 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 66 | | matches | | 0 | "She skidded to a stop" | | 1 | "She'd heard rumors about" | | 2 | "She flashed her badge anyway." | | 3 | "She could push it, demand" | | 4 | "She nodded curtly and turned" | | 5 | "She didn't go far." | | 6 | "They wound through backstreets, down" | | 7 | "She'd heard the whispers in" | | 8 | "They'd never found his body." | | 9 | "She reached into her coat" | | 10 | "She kept her badge hidden," | | 11 | "He rubbed the scar on" | | 12 | "She'd seen that gesture before—right" | | 13 | "She turned to find a" | | 14 | "She took a deep breath" |
| | ratio | 0.227 | |
| 66.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 66 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn sprinted after" | | 2 | "The suspect—some low-level dealer she'd" | | 3 | "She skidded to a stop" | | 4 | "The green neon sign buzzed" | | 5 | "Quinn's sharp jaw tightened." | | 6 | "She'd heard rumors about" | | 7 | "The door creaked as she" | | 8 | "Every eye in the place" | | 9 | "She flashed her badge anyway." | | 10 | "the bartender lied smoothly, polishing" | | 11 | "Quinn's worn leather watch ticked" | | 12 | "She could push it, demand" | | 13 | "She nodded curtly and turned" | | 14 | "She didn't go far." | | 15 | "The rain soaked through her" | | 16 | "Tomás Herrera pulled his collar" | | 17 | "Quinn fell into step behind" | | 18 | "The former paramedic had crossed" | | 19 | "Tonight, he was heading somewhere" |
| | ratio | 0.788 | |
| 75.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 66 | | matches | | | ratio | 0.015 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "Detective Harlow Quinn sprinted after the figure disappearing around the corner, her boots splashing through oily puddles." | | 1 | "Through the rain-streaked window, she saw the dealer exchange quick words with the bartender before vanishing behind a bookshelf that didn't look like it should…" | | 2 | "Quinn watched from behind a stack of pallets as he pulled something from his pocket—a small white object that looked like carved bone—and slipped it into a near…" | | 3 | "Stalls lined the walls, their proprietors hawking wares that made her stomach turn." | | 4 | "She took a deep breath and stepped deeper into the market, following the trail of whispers that curled around her like smoke." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "the bartender lied smoothly (lie)" |
| | dialogueSentences | 8 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 1 | | effectiveRatio | 0.25 | |