| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 943 | | 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) | |
| 36.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 943 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "clandestine" | | 1 | "gleaming" | | 2 | "measured" | | 3 | "weight" | | 4 | "echoed" | | 5 | "grave" | | 6 | "vibrated" | | 7 | "resonance" | | 8 | "chilled" | | 9 | "etched" |
| |
| 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 | 1 | | narrationSentences | 93 | | matches | | |
| 81.41% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | 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 | 0 | | markdownWords | 0 | | totalWords | 925 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 907 | | uniqueNames | 22 | | maxNameDensity | 0.77 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 7 | | Morris | 4 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Oxford | 1 | | Street | 2 | | Camden | 2 | | High | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Underground | 1 | | London | 1 | | Metropolitan | 1 |
| | persons | | 0 | "Nest" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Tottenham" | | 3 | "Court" | | 4 | "Road" | | 5 | "Oxford" | | 6 | "Street" | | 7 | "Camden" | | 8 | "High" | | 9 | "London" | | 10 | "Metropolitan" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 925 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 97 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 28.91 | | std | 24.74 | | cv | 0.856 | | sampleLengths | | 0 | 87 | | 1 | 20 | | 2 | 59 | | 3 | 5 | | 4 | 28 | | 5 | 2 | | 6 | 7 | | 7 | 2 | | 8 | 2 | | 9 | 70 | | 10 | 67 | | 11 | 53 | | 12 | 5 | | 13 | 7 | | 14 | 4 | | 15 | 9 | | 16 | 7 | | 17 | 48 | | 18 | 5 | | 19 | 40 | | 20 | 51 | | 21 | 11 | | 22 | 61 | | 23 | 28 | | 24 | 56 | | 25 | 24 | | 26 | 21 | | 27 | 47 | | 28 | 34 | | 29 | 53 | | 30 | 2 | | 31 | 10 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 151 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 97 | | ratio | 0.052 | | matches | | 0 | "Neon from The Raven's Nest bled across the pavement—green light pooling against the curb like spilled coolant." | | 1 | "Herrera ran with the efficiency of a former paramedic—no wasted motion, pure survival instinct—but Quinn had eighteen years of decorated service and a sharper jaw set with determination." | | 2 | "Herrera's silhouette darted across the empty road, heading toward the old Tube entrance—the abandoned station beneath Camden mentioned only in pre-war ledgers." | | 3 | "It vibrated against her palm—wrong resonance, like holding a tuning fork struck against the fabric of reality itself." | | 4 | "Her torch illuminated symbols etched into the concrete—protection wards or invitations to predators, depending on the reader's knowledge." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 925 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.012972972972972972 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002162162162162162 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 9.54 | | std | 6.08 | | cv | 0.638 | | sampleLengths | | 0 | 6 | | 1 | 17 | | 2 | 22 | | 3 | 13 | | 4 | 29 | | 5 | 8 | | 6 | 1 | | 7 | 11 | | 8 | 4 | | 9 | 17 | | 10 | 10 | | 11 | 20 | | 12 | 8 | | 13 | 5 | | 14 | 4 | | 15 | 10 | | 16 | 5 | | 17 | 9 | | 18 | 2 | | 19 | 7 | | 20 | 2 | | 21 | 2 | | 22 | 12 | | 23 | 8 | | 24 | 15 | | 25 | 2 | | 26 | 10 | | 27 | 14 | | 28 | 9 | | 29 | 3 | | 30 | 4 | | 31 | 11 | | 32 | 9 | | 33 | 4 | | 34 | 8 | | 35 | 8 | | 36 | 20 | | 37 | 16 | | 38 | 28 | | 39 | 9 | | 40 | 5 | | 41 | 7 | | 42 | 4 | | 43 | 9 | | 44 | 7 | | 45 | 10 | | 46 | 22 | | 47 | 16 | | 48 | 5 | | 49 | 7 |
| |
| 78.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4845360824742268 | | totalSentences | 97 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 74.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 88 | | matches | | 0 | "She knew the layout inside:" | | 1 | "Her worn leather watch drank" | | 2 | "His left forearm showed the" | | 3 | "He spun and bolted north," | | 4 | "Her voice cracked across the" | | 5 | "Her breath came in sharp" | | 6 | "She poured on speed, her" | | 7 | "Her leather watch band dug" | | 8 | "He glanced back." | | 9 | "He cut right, down a" | | 10 | "She heard his boots below," | | 11 | "He burst through a fire" | | 12 | "She had tracked men through" | | 13 | "His shout echoed off the" | | 14 | "Her boots hit the tarmac" | | 15 | "He descended into the earth." | | 16 | "She reached the threshold forty" | | 17 | "Her torch beam sliced through" | | 18 | "It moved every full moon." | | 19 | "She reached for it, hesitated." |
| | ratio | 0.364 | |
| 28.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 88 | | matches | | 0 | "Rain transformed Soho into slick" | | 1 | "Neon from The Raven's Nest" | | 2 | "Detective Harlow Quinn stood beneath" | | 3 | "The distinctive green neon sign" | | 4 | "She knew the layout inside:" | | 5 | "Her worn leather watch drank" | | 6 | "The door swung open." | | 7 | "Tomás Herrera emerged, his five-foot-ten" | | 8 | "His left forearm showed the" | | 9 | "The Saint Christopher medallion bounced" | | 10 | "Quinn pushed off the wall." | | 11 | "Herrera's head snapped up." | | 12 | "Recognition detonated across his features." | | 13 | "He spun and bolted north," | | 14 | "Her voice cracked across the" | | 15 | "Rain drilled into her scalp," | | 16 | "Her breath came in sharp" | | 17 | "Herrera's boots skidded on the" | | 18 | "The street was wide, exposed," | | 19 | "She poured on speed, her" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 1 | | matches | | 0 | "Forty-one years old, eighteen of them spent chasing criminals through the Metropolitan streets, and she stood at the threshold of something that defied her unde…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |