| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 104 | | tagDensity | 0.019 | | leniency | 0.038 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2461 | | 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) | |
| 89.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2461 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulse" | | 1 | "thundered" | | 2 | "mechanical" | | 3 | "shattered" | | 4 | "electric" |
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| 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 | 236 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 236 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 338 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2459 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 49.31% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 101 | | wordCount | 1887 | | uniqueNames | 25 | | maxNameDensity | 2.01 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 38 | | Tomás | 1 | | Herrera | 34 | | Raven | 1 | | Nest | 1 | | Wardour | 1 | | Street | 2 | | Tommy | 1 | | Saint | 2 | | Christopher | 2 | | Nathan | 1 | | Morris | 2 | | Shaftesbury | 1 | | Avenue | 1 | | Camden | 1 | | High | 1 | | Night | 1 | | Tube | 1 | | Underground | 1 | | English | 1 | | Arabic | 1 | | Spanish | 1 | | Rain | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Tommy" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Nathan" | | 9 | "Morris" | | 10 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Shaftesbury" | | 4 | "Avenue" | | 5 | "Camden" | | 6 | "High" | | 7 | "English" |
| | globalScore | 0.493 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 145 | | 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 | 2459 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 338 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 224 | | mean | 10.98 | | std | 11.89 | | cv | 1.083 | | sampleLengths | | 0 | 9 | | 1 | 35 | | 2 | 11 | | 3 | 3 | | 4 | 2 | | 5 | 41 | | 6 | 2 | | 7 | 29 | | 8 | 4 | | 9 | 1 | | 10 | 3 | | 11 | 8 | | 12 | 8 | | 13 | 24 | | 14 | 5 | | 15 | 7 | | 16 | 16 | | 17 | 9 | | 18 | 51 | | 19 | 7 | | 20 | 2 | | 21 | 15 | | 22 | 6 | | 23 | 47 | | 24 | 7 | | 25 | 4 | | 26 | 28 | | 27 | 3 | | 28 | 15 | | 29 | 5 | | 30 | 35 | | 31 | 6 | | 32 | 7 | | 33 | 11 | | 34 | 22 | | 35 | 27 | | 36 | 8 | | 37 | 1 | | 38 | 2 | | 39 | 12 | | 40 | 5 | | 41 | 5 | | 42 | 15 | | 43 | 5 | | 44 | 4 | | 45 | 3 | | 46 | 3 | | 47 | 14 | | 48 | 57 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 236 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 356 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 338 | | ratio | 0.006 | | matches | | 0 | "Its mother—or the tall shape holding its hand—covered its eyes." | | 1 | "Beside her, a butcher arranged red objects on ice; each one beat at a different rhythm." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1890 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.01164021164021164 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0015873015873015873 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 338 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 338 | | mean | 7.28 | | std | 4.96 | | cv | 0.681 | | sampleLengths | | 0 | 9 | | 1 | 17 | | 2 | 11 | | 3 | 7 | | 4 | 11 | | 5 | 3 | | 6 | 2 | | 7 | 8 | | 8 | 4 | | 9 | 13 | | 10 | 16 | | 11 | 2 | | 12 | 12 | | 13 | 6 | | 14 | 11 | | 15 | 4 | | 16 | 1 | | 17 | 3 | | 18 | 8 | | 19 | 8 | | 20 | 24 | | 21 | 5 | | 22 | 7 | | 23 | 16 | | 24 | 9 | | 25 | 5 | | 26 | 22 | | 27 | 15 | | 28 | 9 | | 29 | 7 | | 30 | 2 | | 31 | 6 | | 32 | 9 | | 33 | 6 | | 34 | 6 | | 35 | 11 | | 36 | 9 | | 37 | 14 | | 38 | 7 | | 39 | 7 | | 40 | 4 | | 41 | 6 | | 42 | 10 | | 43 | 12 | | 44 | 3 | | 45 | 15 | | 46 | 2 | | 47 | 3 | | 48 | 6 | | 49 | 2 |
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| 56.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3520710059171598 | | totalSentences | 338 | | uniqueOpeners | 119 | |
| 46.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 216 | | matches | | 0 | "Then he ran." | | 1 | "Somewhere behind the wall, plates" | | 2 | "Only static answered." |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 216 | | matches | | 0 | "Her shoes struck water hard" | | 1 | "She caught his wrist, turned" | | 2 | "She released him and crossed" | | 3 | "Its driver leaned on the" | | 4 | "She left out the rest." | | 5 | "She drew her baton and" | | 6 | "His arm locked across her" | | 7 | "His other hand seized her" | | 8 | "His grip broke." | | 9 | "She drove him into the" | | 10 | "He twisted aside before the" | | 11 | "His Saint Christopher medallion had" | | 12 | "It lay against his throat," | | 13 | "He hit the tarmac, rolled" | | 14 | "His gaze shifted past her" | | 15 | "It swung on its hinge." | | 16 | "He vaulted the central barrier," | | 17 | "He limped now." | | 18 | "Her sweep had done some" | | 19 | "He turned east, cut through" |
| | ratio | 0.231 | |
| 36.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 183 | | totalSentences | 216 | | matches | | 0 | "Detective Harlow Quinn cut through" | | 1 | "Her shoes struck water hard" | | 2 | "Herrera shoved between two men" | | 3 | "The other threw an elbow," | | 4 | "She caught his wrist, turned" | | 5 | "She released him and crossed" | | 6 | "A cab’s bonnet clipped her" | | 7 | "Its driver leaned on the" | | 8 | "Herrera vanished round the corner" | | 9 | "Quinn touched the radio clipped" | | 10 | "Static crackled in her earpiece." | | 11 | "Quinn rounded the corner and" | | 12 | "She left out the rest." | | 13 | "The victims had arrived at" | | 14 | "Herrera’s name appeared in two" | | 15 | "The third victim had spoken" | | 16 | "A service alley split two" | | 17 | "She drew her baton and" | | 18 | "Kitchen heat breathed through metal" | | 19 | "Quinn passed a delivery door," |
| | ratio | 0.847 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 216 | | matches | (empty) | | ratio | 0 | |
| 95.86% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 76 | | technicalSentenceCount | 5 | | matches | | 0 | "The victims had arrived at different hospitals over six months, each with blood beneath their fingernails that didn’t match any human profile." | | 1 | "DS Nathan Morris had disappeared from a locked warehouse room with one door, no windows and a blood trail that ended at a brick wall." | | 2 | "From below came a wash of smells that had no business sharing air: incense, hot copper, wet fur, hospital disinfectant." | | 3 | "The suspect stood within reach, but every instinct built over eighteen years warned her that stepping across would turn a pursuit into something without procedu…" | | 4 | "Below, the abandoned station clock showed thirteen minutes past a number that wasn’t there." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |