| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.16 | | leniency | 0.32 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1453 | | 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) | |
| 72.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1453 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "stark" | | 1 | "chill" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "measured" | | 5 | "grave" | | 6 | "vibrated" | | 7 | "velvet" |
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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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 7 | | totalWords | 1453 | | ratio | 0.005 | | matches | | 0 | "Madwoman," | | 1 | "Morris," | | 2 | "This is what killed him." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.37% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1236 | | uniqueNames | 27 | | maxNameDensity | 1.13 | | worstName | "Tomás" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | London | 2 | | Charing | 1 | | Cross | 1 | | Old | 1 | | Compton | 1 | | Street | 1 | | Camden | 2 | | Met | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Tube | 2 | | Tomás | 14 | | Town | 1 | | Victoria | 1 | | Blitz | 1 | | Saint | 1 | | Christopher | 1 | | Victorian | 2 | | Veil | 1 | | Market | 1 | | Herrera | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Met" | | 4 | "Tube" | | 5 | "Tomás" | | 6 | "Victoria" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Market" | | 10 | "Herrera" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Charing" | | 3 | "Old" | | 4 | "Compton" | | 5 | "Street" | | 6 | "Camden" | | 7 | "Tottenham" | | 8 | "Court" | | 9 | "Road" | | 10 | "Town" |
| | globalScore | 0.934 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | 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 | 1453 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 24.63 | | std | 21.53 | | cv | 0.874 | | sampleLengths | | 0 | 24 | | 1 | 2 | | 2 | 44 | | 3 | 77 | | 4 | 8 | | 5 | 75 | | 6 | 60 | | 7 | 6 | | 8 | 71 | | 9 | 11 | | 10 | 58 | | 11 | 6 | | 12 | 15 | | 13 | 31 | | 14 | 36 | | 15 | 5 | | 16 | 5 | | 17 | 14 | | 18 | 9 | | 19 | 13 | | 20 | 11 | | 21 | 6 | | 22 | 52 | | 23 | 7 | | 24 | 3 | | 25 | 3 | | 26 | 12 | | 27 | 2 | | 28 | 1 | | 29 | 13 | | 30 | 45 | | 31 | 39 | | 32 | 20 | | 33 | 9 | | 34 | 3 | | 35 | 6 | | 36 | 45 | | 37 | 13 | | 38 | 47 | | 39 | 9 | | 40 | 7 | | 41 | 30 | | 42 | 15 | | 43 | 64 | | 44 | 10 | | 45 | 76 | | 46 | 22 | | 47 | 29 | | 48 | 20 | | 49 | 58 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 208 | | matches | | 0 | "was already spitting" | | 1 | "were already fanning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 116 | | ratio | 0.009 | | matches | | 0 | "Behind him, Quinn did not go over; she darted around the cab's rear bumper, never breaking stride." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1252 | | adjectiveStacks | 1 | | stackExamples | | 0 | "narrow, rubbish-strewn passage" |
| | adverbCount | 23 | | adverbRatio | 0.018370607028753993 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004792332268370607 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 12.53 | | std | 7.91 | | cv | 0.631 | | sampleLengths | | 0 | 15 | | 1 | 9 | | 2 | 2 | | 3 | 13 | | 4 | 15 | | 5 | 16 | | 6 | 6 | | 7 | 34 | | 8 | 15 | | 9 | 22 | | 10 | 8 | | 11 | 3 | | 12 | 21 | | 13 | 9 | | 14 | 17 | | 15 | 8 | | 16 | 17 | | 17 | 9 | | 18 | 15 | | 19 | 17 | | 20 | 19 | | 21 | 6 | | 22 | 22 | | 23 | 14 | | 24 | 6 | | 25 | 8 | | 26 | 21 | | 27 | 6 | | 28 | 5 | | 29 | 24 | | 30 | 17 | | 31 | 17 | | 32 | 6 | | 33 | 8 | | 34 | 1 | | 35 | 1 | | 36 | 5 | | 37 | 10 | | 38 | 21 | | 39 | 8 | | 40 | 14 | | 41 | 14 | | 42 | 5 | | 43 | 5 | | 44 | 14 | | 45 | 9 | | 46 | 13 | | 47 | 2 | | 48 | 9 | | 49 | 6 |
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| 70.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.46551724137931033 | | totalSentences | 116 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 93.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 92 | | matches | | 0 | "She vaulted the same barrier" | | 1 | "Her boots hit the puddle" | | 2 | "He clutched the strap of" | | 3 | "He ignored her." | | 4 | "He rounded a corner onto" | | 5 | "He needed to go to" | | 6 | "He reached Tottenham Court Road" | | 7 | "Her brown eyes locked onto" | | 8 | "She did not curse or" | | 9 | "She simply checked the worn" | | 10 | "She knew where the line" | | 11 | "His fingers fumbled beneath his" | | 12 | "She held no weapon drawn," | | 13 | "His hand tightened around the" | | 14 | "She took a step forward," | | 15 | "She stepped closer, closing the" | | 16 | "It was a dummy lock." | | 17 | "He pressed the small, yellow" | | 18 | "Her sharp jaw slackened by" | | 19 | "He threw himself backward through" |
| | ratio | 0.315 | |
| 57.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 92 | | matches | | 0 | "Tomás vaulted the rusted iron" | | 1 | "Boots pounded the slick cobbles" | | 2 | "Detective Harlow Quinn did not" | | 3 | "She vaulted the same barrier" | | 4 | "Her boots hit the puddle" | | 5 | "Tomás ducked his head and" | | 6 | "The back alleys of Soho" | | 7 | "That distinctive green neon sign" | | 8 | "He clutched the strap of" | | 9 | "He ignored her." | | 10 | "He rounded a corner onto" | | 11 | "Tomás kicked through the scattered," | | 12 | "A black cab screeched to" | | 13 | "Tomás slapped the wet bonnet," | | 14 | "He needed to go to" | | 15 | "He reached Tottenham Court Road" | | 16 | "Her brown eyes locked onto" | | 17 | "She did not curse or" | | 18 | "She simply checked the worn" | | 19 | "She knew where the line" |
| | ratio | 0.804 | |
| 54.35% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 92 | | matches | | 0 | "Because the full moon was" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 2 | | matches | | 0 | "His fingers fumbled beneath his collar, brushing the cool silver of his Saint Christopher medallion before dropping into his coat pocket." | | 1 | "She spotted the dark curls of Herrera's head bobbing between a stall selling antique silver blades and a butcher carving meat that looked far too pale to be por…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "She took, the torch beam rising to his chest" | | 1 | "Tomás said, his chest heaving" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.08 | | leniency | 0.16 | | rawRatio | 0 | | effectiveRatio | 0 | |