| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.278 | | leniency | 0.556 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1043 | | 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) | |
| 80.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1043 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "resolve" | | 1 | "maw" | | 2 | "footsteps" | | 3 | "flickered" |
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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 | 113 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 113 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | 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 | 2 | | markdownWords | 16 | | totalWords | 1043 | | ratio | 0.015 | | matches | | 0 | "later" | | 1 | "Pursuit on foot, vicinity Camden Town, suspect entered derelict Underground entrance off towpath, request backup." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 963 | | uniqueNames | 27 | | maxNameDensity | 0.83 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Old | 1 | | Compton | 1 | | Street | 2 | | Dispatch | 1 | | Herrera | 5 | | Quinn | 8 | | Charing | 1 | | Cross | 1 | | Road | 3 | | Seville | 1 | | London | 1 | | Bloomsbury | 1 | | Morris | 3 | | Saint | 1 | | Christopher | 1 | | Seven | 1 | | Dials | 1 | | Court | 1 | | Camden | 3 | | Tube | 1 | | Airwave | 1 | | Town | 1 | | Underground | 1 | | Send | 1 | | Commissioner | 1 | | Whitechapel | 1 | | Service | 1 |
| | persons | | 0 | "Dispatch" | | 1 | "Herrera" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Dials" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" | | 6 | "Seville" | | 7 | "London" | | 8 | "Bloomsbury" | | 9 | "Seven" | | 10 | "Court" | | 11 | "Camden" | | 12 | "Town" | | 13 | "Commissioner" | | 14 | "Whitechapel" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | 0 | "mer paramedics apparently trained like bloody" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1043 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 21.73 | | std | 18.47 | | cv | 0.85 | | sampleLengths | | 0 | 26 | | 1 | 2 | | 2 | 5 | | 3 | 40 | | 4 | 30 | | 5 | 5 | | 6 | 27 | | 7 | 29 | | 8 | 42 | | 9 | 44 | | 10 | 5 | | 11 | 21 | | 12 | 86 | | 13 | 29 | | 14 | 11 | | 15 | 14 | | 16 | 5 | | 17 | 43 | | 18 | 12 | | 19 | 25 | | 20 | 3 | | 21 | 44 | | 22 | 7 | | 23 | 33 | | 24 | 42 | | 25 | 2 | | 26 | 2 | | 27 | 25 | | 28 | 47 | | 29 | 2 | | 30 | 44 | | 31 | 23 | | 32 | 15 | | 33 | 1 | | 34 | 6 | | 35 | 17 | | 36 | 67 | | 37 | 30 | | 38 | 1 | | 39 | 24 | | 40 | 9 | | 41 | 2 | | 42 | 5 | | 43 | 10 | | 44 | 22 | | 45 | 30 | | 46 | 24 | | 47 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 113 | | matches | | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 177 | | matches | | 0 | "was heading" | | 1 | "was running" | | 2 | "were cramping" | | 3 | "was slowing" | | 4 | "was staring" | | 5 | "was still spinning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 126 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 974 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.03593429158110883 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00513347022587269 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 8.28 | | std | 7.25 | | cv | 0.876 | | sampleLengths | | 0 | 26 | | 1 | 2 | | 2 | 2 | | 3 | 3 | | 4 | 17 | | 5 | 6 | | 6 | 17 | | 7 | 10 | | 8 | 13 | | 9 | 3 | | 10 | 4 | | 11 | 5 | | 12 | 2 | | 13 | 8 | | 14 | 17 | | 15 | 10 | | 16 | 19 | | 17 | 14 | | 18 | 9 | | 19 | 7 | | 20 | 6 | | 21 | 6 | | 22 | 25 | | 23 | 19 | | 24 | 5 | | 25 | 8 | | 26 | 3 | | 27 | 10 | | 28 | 2 | | 29 | 23 | | 30 | 29 | | 31 | 32 | | 32 | 3 | | 33 | 7 | | 34 | 12 | | 35 | 2 | | 36 | 1 | | 37 | 4 | | 38 | 11 | | 39 | 10 | | 40 | 4 | | 41 | 5 | | 42 | 26 | | 43 | 3 | | 44 | 14 | | 45 | 4 | | 46 | 2 | | 47 | 2 | | 48 | 4 | | 49 | 9 |
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| 75.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.48412698412698413 | | totalSentences | 126 | | uniqueOpeners | 61 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 88 | | matches | | 0 | "Somewhere a door slammed." | | 1 | "Actually laughed, the sound ricocheting" | | 2 | "Then his face changed, something" | | 3 | "Somewhere below, a bell rang." |
| | ratio | 0.045 | |
| 74.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 88 | | matches | | 0 | "They never did." | | 1 | "Her radio crackled against her" | | 2 | "She followed, shoulder clipping a" | | 3 | "She hit the railing shoulder-first," | | 4 | "Her left hand slapped concrete" | | 5 | "She filed the damage under" | | 6 | "His accent thickened under adrenaline" | | 7 | "She wanted him in a" | | 8 | "She wanted to know what" | | 9 | "She wanted to know why" | | 10 | "He darted north." | | 11 | "She cut the corner, gaining" | | 12 | "He skidded round a scaffolding" | | 13 | "They blurred through Seven Dials," | | 14 | "Her lungs burned." | | 15 | "Her radio was useless now," | | 16 | "He was heading north." | | 17 | "He had a destination." | | 18 | "She kept running." | | 19 | "He was running them toward" |
| | ratio | 0.364 | |
| 62.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 88 | | matches | | 0 | "Quinn's boots hit the pavement" | | 1 | "They never did." | | 2 | "Quinn's jacket was already a" | | 3 | "Her radio crackled against her" | | 4 | "Tomás Herrera cut left down" | | 5 | "She followed, shoulder clipping a" | | 6 | "A cat shrieked." | | 7 | "The scar on his forearm" | | 8 | "She hit the railing shoulder-first," | | 9 | "Her left hand slapped concrete" | | 10 | "The watch face cracked against" | | 11 | "Morris had given her that" | | 12 | "She filed the damage under" | | 13 | "Herrera was already across Charing" | | 14 | "Quinn went through the gap" | | 15 | "His accent thickened under adrenaline" | | 16 | "Seville, not London." | | 17 | "She wanted him in a" | | 18 | "She wanted to know what" | | 19 | "She wanted to know why" |
| | ratio | 0.795 | |
| 56.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 88 | | matches | | | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 2 | | matches | | 0 | "Herrera was already across Charing Cross Road, weaving between a night bus and a minicab that blared its horn long enough to wake the dead." | | 1 | "A breath of warm air in the cold rain, carrying smells that had no business in a disused station." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 0 | | effectiveRatio | 0 | |