| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn said quietly [quietly]" |
| | dialogueSentences | 25 | | tagDensity | 0.52 | | leniency | 1 | | rawRatio | 0.077 | | effectiveRatio | 0.077 | |
| 87.23% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1175 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "slowly" | | 2 | "slightly" |
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| 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) | |
| 74.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1175 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "echoing" | | 1 | "footsteps" | | 2 | "etched" | | 3 | "perfect" | | 4 | "trembled" |
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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 | 65 | | matches | (empty) | |
| 98.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 65 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 77 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1186 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.48% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 894 | | uniqueNames | 12 | | maxNameDensity | 1.23 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 11 | | Tube | 1 | | Camden | 1 | | Metropolitan | 1 | | Police | 1 | | Transport | 1 | | London | 1 | | Ferro | 8 | | Careful | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Police" | | 4 | "Ferro" | | 5 | "Morris" |
| | places | | | globalScore | 0.885 | | windowScore | 1 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed almost deliberate" | | 1 | "w her decide — visibly, a small nod — to b" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.843 | | wordCount | 1186 | | matches | | 0 | "not the points of the compass rose but sigils, angular" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 77 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 39.53 | | std | 24.66 | | cv | 0.624 | | sampleLengths | | 0 | 33 | | 1 | 79 | | 2 | 32 | | 3 | 34 | | 4 | 54 | | 5 | 8 | | 6 | 72 | | 7 | 38 | | 8 | 74 | | 9 | 23 | | 10 | 79 | | 11 | 8 | | 12 | 5 | | 13 | 60 | | 14 | 59 | | 15 | 47 | | 16 | 16 | | 17 | 72 | | 18 | 73 | | 19 | 33 | | 20 | 69 | | 21 | 13 | | 22 | 39 | | 23 | 2 | | 24 | 55 | | 25 | 22 | | 26 | 44 | | 27 | 12 | | 28 | 15 | | 29 | 16 |
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| 72.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 65 | | matches | | 0 | "been sealed" | | 1 | "been left" | | 2 | "was composed" | | 3 | "was etched" | | 4 | "been closed" | | 5 | "was scorched" |
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| 62.07% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 145 | | matches | | 0 | "was already moving" | | 1 | "was going" | | 2 | "was watching" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 77 | | ratio | 0.104 | | matches | | 0 | "The Metropolitan Police had no jurisdiction down here, technically — Transport for London owned the bones of it, and the bones had been left to rot." | | 1 | "The water was shallow — three inches, maybe four, pooled where the drainage had failed." | | 2 | "His suit jacket sat heavy and dark, soaked through — but only to the shoulders." | | 3 | "Not gripping — cradling, the way you'd hold a bird." | | 4 | "The face was etched with markings she didn't recognize — not the points of the compass rose but sigils, angular and deliberate, like a script." | | 5 | "On the dead man's left wrist, worn leather, its face cracked but running — still running, after days in the water." | | 6 | "Nothing reached the end of it — she'd worked three years in this city and thought she knew its geography, its bones, everything under its skin." | | 7 | "Ferro stared at her, then at the water, and Quinn saw her decide — visibly, a small nod — to believe her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 889 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.02924634420697413 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010123734533183352 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 77 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 77 | | mean | 15.4 | | std | 11.48 | | cv | 0.746 | | sampleLengths | | 0 | 33 | | 1 | 14 | | 2 | 26 | | 3 | 26 | | 4 | 13 | | 5 | 18 | | 6 | 14 | | 7 | 30 | | 8 | 4 | | 9 | 7 | | 10 | 39 | | 11 | 3 | | 12 | 5 | | 13 | 8 | | 14 | 22 | | 15 | 50 | | 16 | 6 | | 17 | 32 | | 18 | 15 | | 19 | 6 | | 20 | 28 | | 21 | 1 | | 22 | 24 | | 23 | 14 | | 24 | 9 | | 25 | 37 | | 26 | 15 | | 27 | 7 | | 28 | 2 | | 29 | 18 | | 30 | 5 | | 31 | 3 | | 32 | 3 | | 33 | 2 | | 34 | 4 | | 35 | 5 | | 36 | 19 | | 37 | 10 | | 38 | 22 | | 39 | 2 | | 40 | 17 | | 41 | 25 | | 42 | 15 | | 43 | 3 | | 44 | 6 | | 45 | 13 | | 46 | 25 | | 47 | 6 | | 48 | 1 | | 49 | 9 |
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| 58.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.42857142857142855 | | totalSentences | 77 | | uniqueOpeners | 33 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 58 | | matches | | 0 | "Then DS Morris had died" | | 1 | "Just clean brick, and a" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 58 | | matches | | 0 | "His suit jacket sat heavy" | | 1 | "She circled the body." | | 2 | "She followed it with her" | | 3 | "She pulled on nitrile gloves" | | 4 | "She tilted it." | | 5 | "It didn't respond to the" | | 6 | "It didn't respond when she" | | 7 | "She noted it and filed" | | 8 | "She stood, slipping the compass" | | 9 | "Her torch couldn't reach the" | | 10 | "She swept her beam across" | | 11 | "It lay flat as poured" | | 12 | "she said, too low for" |
| | ratio | 0.224 | |
| 63.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 58 | | matches | | 0 | "The dead man floated face-down" | | 1 | "The abandoned Tube station beneath" | | 2 | "The Metropolitan Police had no" | | 3 | "Something sweet and wrong, like" | | 4 | "Quinn clicked her torch to" | | 5 | "Ferro leaned over the edge" | | 6 | "The platform had collapsed in" | | 7 | "The other tucked beneath him." | | 8 | "Quinn was already moving toward" | | 9 | "The track bed swallowed their" | | 10 | "Quinn stopped three metres from" | | 11 | "The water was shallow —" | | 12 | "A man doesn't drown in" | | 13 | "The hands were wrinkle-soft but" | | 14 | "Ferro said, crouching to take" | | 15 | "Quinn agreed, She pointed with" | | 16 | "His suit jacket sat heavy" | | 17 | "Ferro straightened, lowering her camera." | | 18 | "She circled the body." | | 19 | "The extended arm bothered her." |
| | ratio | 0.793 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "The platform had collapsed in one section, spilling a fan of brick rubble down to the track bed, and the dead man had come to rest against the largest chunk, hi…" | | 1 | "The needle held one heading with the certainty of a creature that knew where it was going: northwest, through the dead wall of the tunnel." |
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| 48.08% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 2 | | matches | | 0 | "said DS Ferro behind her, her voice echoing off tiles furred with black mould" | | 1 | "Ferro said, crouching to take photographs" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 25 | | tagDensity | 0.28 | | leniency | 0.56 | | rawRatio | 0.143 | | effectiveRatio | 0.08 | |