| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 18 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.167 | | effectiveRatio | 0.111 | |
| 94.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1747 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 88.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1747 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "clandestine" | | 1 | "silence" | | 2 | "stomach" | | 3 | "glint" |
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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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 151 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 163 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1747 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 64 | | wordCount | 1674 | | uniqueNames | 20 | | maxNameDensity | 1.19 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 2 | | Nest | 4 | | Quinn | 20 | | Metropolitan | 1 | | Police | 1 | | Soho | 1 | | Herrera | 12 | | Seville | 1 | | Spain | 1 | | London | 2 | | Saint | 2 | | Christopher | 2 | | Frith | 1 | | Street | 2 | | Morris | 5 | | Wardour | 1 | | Camden | 2 | | Tube | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Police" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Veil" | | 9 | "Market" |
| | places | | 0 | "Metropolitan" | | 1 | "Soho" | | 2 | "Seville" | | 3 | "Spain" | | 4 | "London" | | 5 | "Frith" | | 6 | "Street" | | 7 | "Wardour" |
| | globalScore | 0.903 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 106 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a pub that had lost several a" | | 1 | "laughter that seemed to come from several directions at once" |
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| 85.52% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.145 | | wordCount | 1747 | | matches | | 0 | "not defiance but warning" | | 1 | "Not from patrol maps, but from whispers" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 163 | | matches | | 0 | "ending that fit" | | 1 | "understand that London" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 29.61 | | std | 29.23 | | cv | 0.987 | | sampleLengths | | 0 | 145 | | 1 | 80 | | 2 | 8 | | 3 | 75 | | 4 | 65 | | 5 | 5 | | 6 | 2 | | 7 | 6 | | 8 | 13 | | 9 | 80 | | 10 | 39 | | 11 | 88 | | 12 | 8 | | 13 | 42 | | 14 | 15 | | 15 | 30 | | 16 | 31 | | 17 | 64 | | 18 | 18 | | 19 | 5 | | 20 | 57 | | 21 | 69 | | 22 | 7 | | 23 | 23 | | 24 | 17 | | 25 | 16 | | 26 | 43 | | 27 | 3 | | 28 | 9 | | 29 | 8 | | 30 | 16 | | 31 | 5 | | 32 | 5 | | 33 | 8 | | 34 | 68 | | 35 | 3 | | 36 | 19 | | 37 | 56 | | 38 | 20 | | 39 | 44 | | 40 | 4 | | 41 | 12 | | 42 | 2 | | 43 | 50 | | 44 | 10 | | 45 | 62 | | 46 | 53 | | 47 | 7 | | 48 | 55 | | 49 | 4 |
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| 95.97% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 151 | | matches | | 0 | "was involved" | | 1 | "being called" | | 2 | "been carried" | | 3 | "were soaked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 304 | | matches | | 0 | "was going" | | 1 | "was not fleeing" | | 2 | "was not hiding" | | 3 | "was working" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 1 | | totalSentences | 163 | | ratio | 0.006 | | matches | | 0 | "Quinn knew his file: born in Seville, Spain; former paramedic; brought to London by the NHS until he lost his license after administering unauthorized treatments to patients no hospital would classify as human." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1682 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.027348394768133173 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.0077288941736028535 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 163 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 163 | | mean | 10.72 | | std | 7.86 | | cv | 0.734 | | sampleLengths | | 0 | 19 | | 1 | 24 | | 2 | 7 | | 3 | 27 | | 4 | 12 | | 5 | 17 | | 6 | 18 | | 7 | 21 | | 8 | 27 | | 9 | 13 | | 10 | 22 | | 11 | 3 | | 12 | 2 | | 13 | 13 | | 14 | 8 | | 15 | 33 | | 16 | 30 | | 17 | 6 | | 18 | 3 | | 19 | 3 | | 20 | 22 | | 21 | 15 | | 22 | 18 | | 23 | 10 | | 24 | 5 | | 25 | 2 | | 26 | 4 | | 27 | 2 | | 28 | 11 | | 29 | 2 | | 30 | 12 | | 31 | 6 | | 32 | 14 | | 33 | 12 | | 34 | 6 | | 35 | 30 | | 36 | 10 | | 37 | 3 | | 38 | 7 | | 39 | 19 | | 40 | 2 | | 41 | 4 | | 42 | 4 | | 43 | 20 | | 44 | 27 | | 45 | 31 | | 46 | 8 | | 47 | 9 | | 48 | 2 | | 49 | 1 |
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| 48.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3496932515337423 | | totalSentences | 163 | | uniqueOpeners | 57 | |
| 48.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 137 | | matches | | 0 | "Then he swerved around a" | | 1 | "Then it moved to her" |
| | ratio | 0.015 | |
| 82.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 137 | | matches | | 0 | "She held herself with the" | | 1 | "She had noticed the bookshelf" | | 2 | "He was worse." | | 3 | "He was useful." | | 4 | "He came out of the" | | 5 | "He looked left, right, and" | | 6 | "She stepped from the alley." | | 7 | "He did not answer." | | 8 | "He shoved through a knot" | | 9 | "He was fast, but he" | | 10 | "She saw his left forearm" | | 11 | "she shouted, but the city" | | 12 | "Her shoulder struck brick." | | 13 | "She had kept her bearing" | | 14 | "She drew closer when he" | | 15 | "He glanced back, warm brown" | | 16 | "She thumbed the emergency number" | | 17 | "She swore and pocketed it." | | 18 | "He knew where he was" | | 19 | "He was not fleeing blindly." |
| | ratio | 0.343 | |
| 22.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 120 | | totalSentences | 137 | | matches | | 0 | "The green neon sign above" | | 1 | "Harlow Quinn stood in the" | | 2 | "A wet bus passed, its" | | 3 | "The worn leather watch on" | | 4 | "She held herself with the" | | 5 | "Tonight, waiting on a Soho" | | 6 | "The Nest looked like a" | | 7 | "She had noticed the bookshelf" | | 8 | "The sort of place where" | | 9 | "Tomás Herrera had been one" | | 10 | "Quinn knew his file: born" | | 11 | "Herrera was not a killer," | | 12 | "He was worse." | | 13 | "He was useful." | | 14 | "He came out of the" | | 15 | "Rain darkened his olive skin" | | 16 | "The Saint Christopher medallion at" | | 17 | "He looked left, right, and" | | 18 | "She stepped from the alley." | | 19 | "He did not answer." |
| | ratio | 0.876 | |
| 36.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 137 | | matches | | 0 | "Now he provided off-the-books medical" |
| | ratio | 0.007 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 6 | | matches | | 0 | "In the window glass she saw herself only in fragments: closely cropped salt-and-pepper hair, the sharp line of her jaw, brown eyes that had learned to wait." | | 1 | "Tonight, waiting on a Soho street for a man who treated criminals in back rooms, patience felt more like a wound." | | 2 | "Three years since she had lost her partner in a case that had no clean edges, no sensible forensic trail, no ending that fit inside a report." | | 3 | "Three years since she had begun to understand that London hid rooms beneath its rooms, and that the thing that had taken Morris had supernatural origins she did…" | | 4 | "A sound rose from below: low voices, the chime of glass, a distant laughter that seemed to come from several directions at once." | | 5 | "It was pale, weightless, and faintly warm, as if it had been carried in a living mouth." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "she shouted, but the city swallowed the words" |
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| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 18 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.167 | | effectiveRatio | 0.111 | |