| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.207 | | leniency | 0.414 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1334 | | 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) | |
| 96.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1334 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 123 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 123 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 146 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1334 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 42.83% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 75 | | wordCount | 1213 | | uniqueNames | 20 | | maxNameDensity | 2.14 | | worstName | "Herrera" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 3 | | Harlow | 1 | | Quinn | 24 | | Tomás | 1 | | Herrera | 26 | | Soho | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 4 | | Wardour | 1 | | Street | 2 | | Two | 1 | | Tottenham | 1 | | Court | 1 | | Road | 2 | | Euston | 1 | | Camden | 1 | | High | 1 | | Tube | 1 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Tube" |
| | places | | 0 | "Nest" | | 1 | "Tomás" | | 2 | "Soho" | | 3 | "Wardour" | | 4 | "Street" | | 5 | "Tottenham" | | 6 | "Court" | | 7 | "Road" | | 8 | "Euston" | | 9 | "Camden" | | 10 | "High" |
| | globalScore | 0.428 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 93 | | 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 | 1334 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 146 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 20.21 | | std | 17.32 | | cv | 0.857 | | sampleLengths | | 0 | 36 | | 1 | 2 | | 2 | 15 | | 3 | 37 | | 4 | 68 | | 5 | 6 | | 6 | 39 | | 7 | 4 | | 8 | 62 | | 9 | 12 | | 10 | 45 | | 11 | 16 | | 12 | 21 | | 13 | 26 | | 14 | 4 | | 15 | 36 | | 16 | 41 | | 17 | 17 | | 18 | 4 | | 19 | 10 | | 20 | 6 | | 21 | 27 | | 22 | 46 | | 23 | 33 | | 24 | 22 | | 25 | 17 | | 26 | 2 | | 27 | 11 | | 28 | 49 | | 29 | 52 | | 30 | 36 | | 31 | 9 | | 32 | 5 | | 33 | 4 | | 34 | 12 | | 35 | 4 | | 36 | 18 | | 37 | 4 | | 38 | 2 | | 39 | 16 | | 40 | 46 | | 41 | 28 | | 42 | 7 | | 43 | 53 | | 44 | 28 | | 45 | 45 | | 46 | 11 | | 47 | 5 | | 48 | 6 | | 49 | 4 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 123 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 204 | | matches | | |
| 84.15% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 3 | | flaggedSentences | 3 | | totalSentences | 146 | | ratio | 0.021 | | matches | | 0 | "Its bonnet blocked Quinn’s path; she slapped it as she passed and followed Herrera through a break in the traffic." | | 1 | "Her grip slipped; he backed into the street and ran north." | | 2 | "Not at the name; at the date." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1215 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.01316872427983539 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0008230452674897119 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 146 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 146 | | mean | 9.14 | | std | 5.37 | | cv | 0.588 | | sampleLengths | | 0 | 11 | | 1 | 25 | | 2 | 2 | | 3 | 3 | | 4 | 9 | | 5 | 3 | | 6 | 14 | | 7 | 11 | | 8 | 12 | | 9 | 13 | | 10 | 16 | | 11 | 21 | | 12 | 18 | | 13 | 6 | | 14 | 12 | | 15 | 7 | | 16 | 20 | | 17 | 3 | | 18 | 1 | | 19 | 10 | | 20 | 18 | | 21 | 16 | | 22 | 18 | | 23 | 3 | | 24 | 9 | | 25 | 4 | | 26 | 5 | | 27 | 15 | | 28 | 10 | | 29 | 11 | | 30 | 16 | | 31 | 4 | | 32 | 8 | | 33 | 9 | | 34 | 15 | | 35 | 5 | | 36 | 6 | | 37 | 4 | | 38 | 5 | | 39 | 18 | | 40 | 13 | | 41 | 11 | | 42 | 12 | | 43 | 11 | | 44 | 7 | | 45 | 10 | | 46 | 7 | | 47 | 4 | | 48 | 10 | | 49 | 6 |
| |
| 56.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.3493150684931507 | | totalSentences | 146 | | uniqueOpeners | 51 | |
| 28.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 119 | | matches | | | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 119 | | matches | | 0 | "He glanced back." | | 1 | "His Saint Christopher medallion flashed" | | 2 | "She caught the van’s wing" | | 3 | "He had given her an" | | 4 | "Its bonnet blocked Quinn’s path;" | | 5 | "He saw her and changed" | | 6 | "She stopped short." | | 7 | "He burst out less than" | | 8 | "He struck her wrist with" | | 9 | "Her grip slipped; he backed" | | 10 | "His chest rose and fell." | | 11 | "She shoved the bone disc" | | 12 | "He ran with his left" | | 13 | "He checked over his shoulder" | | 14 | "He turned down a narrow" | | 15 | "She braced a hand on" | | 16 | "He could have taken a" | | 17 | "He passed three with their" | | 18 | "Her radio hissed when she" | | 19 | "She gave her location, heard" |
| | ratio | 0.252 | |
| 52.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 119 | | matches | | 0 | "Rain chewed the green neon" | | 1 | "Detective Harlow Quinn pushed through" | | 2 | "He glanced back." | | 3 | "His Saint Christopher medallion flashed" | | 4 | "Quinn cleared a row of" | | 5 | "A chair struck her shin" | | 6 | "She caught the van’s wing" | | 7 | "Herrera had spent twelve minutes" | | 8 | "He had given her an" | | 9 | "The envelope bore Morris’s case" | | 10 | "Herrera reached the end of" | | 11 | "A taxi’s horn cracked through" | | 12 | "Its bonnet blocked Quinn’s path;" | | 13 | "Herrera knocked through the doorway" | | 14 | "Quinn took the pavement, gained" | | 15 | "He saw her and changed" | | 16 | "She stopped short." | | 17 | "He burst out less than" | | 18 | "Quinn grabbed his coat." | | 19 | "Fabric tore beneath her fingers." |
| | ratio | 0.815 | |
| 42.02% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 119 | | matches | | 0 | "By the time she reached" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "Herrera had, water dripping from his curls" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "the woman observed (observe)" |
| | dialogueSentences | 29 | | tagDensity | 0.138 | | leniency | 0.276 | | rawRatio | 0.25 | | effectiveRatio | 0.069 | |