| 66.67% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "She turned slowly [slowly]" | | 1 | "Webb said flatly [flatly]" |
| | dialogueSentences | 30 | | tagDensity | 0.467 | | leniency | 0.933 | | rawRatio | 0.143 | | effectiveRatio | 0.133 | |
| 78.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1386 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "utterly" | | 1 | "precisely" | | 2 | "gently" | | 3 | "lazily" | | 4 | "slowly" | | 5 | "carefully" |
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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) | |
| 56.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1386 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "footsteps" | | 1 | "gleaming" | | 2 | "weight" | | 3 | "implication" | | 4 | "etched" | | 5 | "standard" | | 6 | "intricate" | | 7 | "coded" | | 8 | "structure" | | 9 | "echoed" |
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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 | 93 | | matches | (empty) | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1394 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1047 | | uniqueNames | 13 | | maxNameDensity | 1.15 | | worstName | "Webb" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Tube | 1 | | Camden | 1 | | Blitz | 1 | | Webb | 12 | | Edwardian | 1 | | London | 1 | | Marsh | 3 | | Morris | 2 | | Met | 1 | | Canning | 1 | | Town | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Webb" | | 4 | "Marsh" | | 5 | "Morris" | | 6 | "Met" |
| | places | | 0 | "London" | | 1 | "Canning" | | 2 | "Town" |
| | globalScore | 0.927 | | windowScore | 0.833 | |
| 36.36% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 3 | | matches | | 0 | "lanced around, visibly irritated that she'" | | 1 | "quite name" | | 2 | "quite penetrate" |
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| 56.53% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.435 | | wordCount | 1394 | | matches | | 0 | "not snapped, not forced, but sheared through with something" | | 1 | "not forced, but sheared through with something" |
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| 74.92% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 109 | | matches | | 0 | "see that the" | | 1 | "irritated that she'd" | | 2 | "lost that certainty" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 41 | | std | 27.03 | | cv | 0.659 | | sampleLengths | | 0 | 66 | | 1 | 63 | | 2 | 48 | | 3 | 18 | | 4 | 77 | | 5 | 5 | | 6 | 45 | | 7 | 82 | | 8 | 14 | | 9 | 7 | | 10 | 1 | | 11 | 19 | | 12 | 71 | | 13 | 13 | | 14 | 56 | | 15 | 34 | | 16 | 60 | | 17 | 2 | | 18 | 50 | | 19 | 9 | | 20 | 71 | | 21 | 93 | | 22 | 6 | | 23 | 48 | | 24 | 14 | | 25 | 38 | | 26 | 85 | | 27 | 44 | | 28 | 61 | | 29 | 28 | | 30 | 70 | | 31 | 50 | | 32 | 34 | | 33 | 12 |
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| 75.08% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 93 | | matches | | 0 | "been sealed" | | 1 | "been ground" | | 2 | "was curved" | | 3 | "been disturbed" | | 4 | "was discovered" | | 5 | "been broken" | | 6 | "was etched" | | 7 | "been scraped" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 177 | | matches | | 0 | "was already fighting" | | 1 | "were already tracking" | | 2 | "wasn't looking" | | 3 | "was looking" | | 4 | "was going" | | 5 | "was already watching" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 109 | | ratio | 0.073 | | matches | | 0 | "The padlock on the gate had been cut cleanly — not snapped, not forced, but sheared through with something that left a gleaming edge like melted butter." | | 1 | "His clothes were expensive — a cashmere coat, leather shoes that had barely been broken in." | | 2 | "Something had been scraped into the mortar — a symbol, semicircular with three lines descending from its arc." | | 3 | "In eighteen years she had learned to read London's criminal dialects — the tags, the signs, the coded language of territory." | | 4 | "She'd been twenty feet away when it came down, and she knew what she'd seen in the half-second before the dust swallowed everything — a shape in the air that wasn't smoke, wasn't shadow, wasn't anything her training had a category for." | | 5 | "Following threads that led to names like the ones in Marsh's expensive coat pocket — names she'd seen in Morris's case notes, names connected to a clique that operated in spaces the Met didn't map." | | 6 | "She remembered being him — younger, certain that every mystery had a mundane explanation if you looked hard enough." | | 7 | "Not a train — the line had been dead for eighty years." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1042 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.03838771593090211 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.016314779270633396 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 12.79 | | std | 9.78 | | cv | 0.765 | | sampleLengths | | 0 | 22 | | 1 | 17 | | 2 | 27 | | 3 | 12 | | 4 | 9 | | 5 | 27 | | 6 | 15 | | 7 | 15 | | 8 | 26 | | 9 | 7 | | 10 | 18 | | 11 | 6 | | 12 | 14 | | 13 | 15 | | 14 | 6 | | 15 | 1 | | 16 | 24 | | 17 | 11 | | 18 | 5 | | 19 | 18 | | 20 | 27 | | 21 | 11 | | 22 | 23 | | 23 | 16 | | 24 | 7 | | 25 | 25 | | 26 | 7 | | 27 | 7 | | 28 | 7 | | 29 | 1 | | 30 | 19 | | 31 | 5 | | 32 | 22 | | 33 | 17 | | 34 | 20 | | 35 | 7 | | 36 | 5 | | 37 | 8 | | 38 | 5 | | 39 | 47 | | 40 | 4 | | 41 | 13 | | 42 | 21 | | 43 | 11 | | 44 | 17 | | 45 | 2 | | 46 | 1 | | 47 | 7 | | 48 | 22 | | 49 | 2 |
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| 67.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.44036697247706424 | | totalSentences | 109 | | uniqueOpeners | 48 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 82 | | matches | | 0 | "At least, that's what the" | | 1 | "At least thirty distinct sets" | | 2 | "Gently, she pried open Marsh's" | | 3 | "Quietly, carefully, off the books." |
| | ratio | 0.049 | |
| 93.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 82 | | matches | | 0 | "She clicked her penlight on" | | 1 | "He was a decade younger" | | 2 | "He looked up as her" | | 3 | "She didn't respond to the" | | 4 | "Her brown eyes were already" | | 5 | "His clothes were expensive —" | | 6 | "His face held the waxy" | | 7 | "She was looking at his" | | 8 | "His fingers were locked around" | | 9 | "They were intricate, deliberate, and" | | 10 | "She repeated the words without" | | 11 | "She returned to the body" | | 12 | "She turned it over." | | 13 | "She checked her watch." | | 14 | "She tilted the brass case." | | 15 | "It held its direction with" | | 16 | "She turned slowly, keeping the" | | 17 | "Her mind went to Morris." | | 18 | "She'd been twenty feet away" | | 19 | "She'd buried her partner and" |
| | ratio | 0.317 | |
| 63.66% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 82 | | matches | | 0 | "The stairs down smelled of" | | 1 | "She clicked her penlight on" | | 2 | "The abandoned Tube station beneath" | | 3 | "The padlock on the gate" | | 4 | "Quinn paused at the bottom" | | 5 | "He was a decade younger" | | 6 | "He looked up as her" | | 7 | "She didn't respond to the" | | 8 | "Her brown eyes were already" | | 9 | "The platform was curved, original" | | 10 | "Dozens of footprints marked the" | | 11 | "The crowd had come and" | | 12 | "Webb stood, knees cracking" | | 13 | "Quinn moved closer to the" | | 14 | "Marsh lay on his back" | | 15 | "His clothes were expensive —" | | 16 | "His face held the waxy" | | 17 | "She was looking at his" | | 18 | "Webb frowned and stepped closer." | | 19 | "Quinn crouched, her worn leather" |
| | ratio | 0.793 | |
| 60.98% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 82 | | matches | | 0 | "Even in the poor light," |
| | ratio | 0.012 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 6 | | matches | | 0 | "The stairs down smelled of damp concrete and something older, something that coated the back of Harlow Quinn's throat like copper pennies." | | 1 | "He was a decade younger than her, ambitious in that eager way that hadn't yet been ground down by the weight of things that didn't fit." | | 2 | "Dozens of footprints marked the dust in patterns that spoke of heavy traffic, far more than a single victim and a killer would produce." | | 3 | "It held its direction with strange certainty, as though anchored to something she couldn't see." | | 4 | "She'd been twenty feet away when it came down, and she knew what she'd seen in the half-second before the dust swallowed everything — a shape in the air that wa…" | | 5 | "Following threads that led to names like the ones in Marsh's expensive coat pocket — names she'd seen in Morris's case notes, names connected to a clique that o…" |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "Webb stood, knees cracking" |
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| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "She repeated (repeat)" | | 1 | "she murmured (murmur)" |
| | dialogueSentences | 30 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0.5 | | effectiveRatio | 0.133 | |