| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.10% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1726 | | totalAiIsmAdverbs | 1 | | 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) | |
| 65.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1726 | | totalAiIsms | 12 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | word | "down her spine" | | count | 1 |
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| | highlights | | 0 | "fractured" | | 1 | "roaring" | | 2 | "familiar" | | 3 | "flickered" | | 4 | "weight" | | 5 | "gloom" | | 6 | "pulse" | | 7 | "traced" | | 8 | "down her spine" |
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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 | 1 | | narrationSentences | 169 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 169 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 176 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1724 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 1625 | | uniqueNames | 28 | | maxNameDensity | 0.8 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 3 | | Soho | 1 | | Harlow | 1 | | Quinn | 13 | | Herrera | 13 | | Saint | 1 | | Christopher | 1 | | Greek | 1 | | Street | 1 | | Tomás | 1 | | Morris | 7 | | Avenue | 1 | | Faces | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Camden | 3 | | Veil | 2 | | Market | 2 | | Underground | 1 | | Tube | 1 | | English | 1 | | Heads | 1 | | Seville | 1 | | London | 2 | | Metropolitan | 1 | | Rain | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Morris" | | 9 | "Market" | | 10 | "Heads" | | 11 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "Avenue" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Road" | | 7 | "Tube" | | 8 | "Seville" | | 9 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.58 | | wordCount | 1724 | | matches | | 0 | "not with fear exactly but with calculation" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 176 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 43.1 | | std | 29.52 | | cv | 0.685 | | sampleLengths | | 0 | 86 | | 1 | 2 | | 2 | 3 | | 3 | 78 | | 4 | 9 | | 5 | 86 | | 6 | 72 | | 7 | 59 | | 8 | 88 | | 9 | 6 | | 10 | 58 | | 11 | 54 | | 12 | 78 | | 13 | 22 | | 14 | 79 | | 15 | 3 | | 16 | 74 | | 17 | 5 | | 18 | 77 | | 19 | 70 | | 20 | 23 | | 21 | 57 | | 22 | 3 | | 23 | 69 | | 24 | 48 | | 25 | 70 | | 26 | 39 | | 27 | 14 | | 28 | 58 | | 29 | 29 | | 30 | 28 | | 31 | 52 | | 32 | 19 | | 33 | 25 | | 34 | 65 | | 35 | 25 | | 36 | 9 | | 37 | 7 | | 38 | 72 | | 39 | 3 |
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| 92.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 169 | | matches | | 0 | "been locked" | | 1 | "were gone" | | 2 | "been mopped" | | 3 | "was allowed" | | 4 | "been made" | | 5 | "was meant" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 271 | | matches | | 0 | "was making" | | 1 | "was going" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 176 | | ratio | 0.011 | | matches | | 0 | "The scar along his left forearm—knife work, according to the file—caught the light as he pumped his arms." | | 1 | "Maps and black-and-white photographs lined the walls of the Nest; down here the walls were bare brick and old advertising posters bleached to ghosts." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1637 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.02687843616371411 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006108735491753207 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 176 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 176 | | mean | 9.8 | | std | 7.28 | | cv | 0.743 | | sampleLengths | | 0 | 21 | | 1 | 16 | | 2 | 12 | | 3 | 26 | | 4 | 6 | | 5 | 5 | | 6 | 2 | | 7 | 3 | | 8 | 21 | | 9 | 16 | | 10 | 7 | | 11 | 8 | | 12 | 16 | | 13 | 4 | | 14 | 3 | | 15 | 3 | | 16 | 7 | | 17 | 2 | | 18 | 4 | | 19 | 4 | | 20 | 14 | | 21 | 25 | | 22 | 2 | | 23 | 3 | | 24 | 4 | | 25 | 14 | | 26 | 16 | | 27 | 13 | | 28 | 16 | | 29 | 8 | | 30 | 3 | | 31 | 13 | | 32 | 19 | | 33 | 2 | | 34 | 1 | | 35 | 1 | | 36 | 14 | | 37 | 13 | | 38 | 22 | | 39 | 6 | | 40 | 15 | | 41 | 18 | | 42 | 25 | | 43 | 4 | | 44 | 8 | | 45 | 7 | | 46 | 11 | | 47 | 1 | | 48 | 5 | | 49 | 10 |
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| 49.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.375 | | totalSentences | 176 | | uniqueOpeners | 66 | |
| 69.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 144 | | matches | | 0 | "Then he had looked back." | | 1 | "Somewhere deeper, a bell rang" | | 2 | "Somewhere a bone token clicked" |
| | ratio | 0.021 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 144 | | matches | | 0 | "She had spotted Herrera leaving" | | 1 | "She had given him a" | | 2 | "She had his file memorised:" | | 3 | "Her voice carried, flat and" | | 4 | "He did not stop." | | 5 | "He never even slowed." | | 6 | "She wiped her face with" | | 7 | "Her cropped salt-and-pepper hair was" | | 8 | "She ignored it." | | 9 | "She took the same jump," | | 10 | "She weaved through them, badge" | | 11 | "He was fast for a" | | 12 | "Her lungs burned in a" | | 13 | "She had chased men through" | | 14 | "She had never chased one" | | 15 | "He was making for Camden." | | 16 | "She had dismissed most of" | | 17 | "He knew the streets." | | 18 | "He knew she did not" | | 19 | "He put on a burst" |
| | ratio | 0.333 | |
| 60.69% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 115 | | totalSentences | 144 | | matches | | 0 | "Rain sheeted off the green" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "She had spotted Herrera leaving" | | 3 | "She had given him a" | | 4 | "Quinn cut the corner onto" | | 5 | "Herrera was already thirty metres" | | 6 | "Olive skin gone sallow under" | | 7 | "She had his file memorised:" | | 8 | "The clique’s pet medic." | | 9 | "Her voice carried, flat and" | | 10 | "He did not stop." | | 11 | "He never even slowed." | | 12 | "A delivery van growled past," | | 13 | "She wiped her face with" | | 14 | "DS Morris used to say" | | 15 | "Morris was three years dead" | | 16 | "Herrera ducked left into an" | | 17 | "Quinn followed, shoulder clipping brick," | | 18 | "Her cropped salt-and-pepper hair was" | | 19 | "She ignored it." |
| | ratio | 0.799 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 144 | | matches | | 0 | "Now he ran." | | 1 | "Now the seals were gone." | | 2 | "Now there was only the" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 4 | | matches | | 0 | "Herrera was already at the far end, speaking low to a figure in a long coat who stood beside a rusted barrier." | | 1 | "Forty-one years old, eighteen of them decorated, and still she stood on a platform that should not exist, staring at a market that sold enchanted goods and bann…" | | 2 | "Quinn thought of Herrera’s medallion, the saint who protected travellers." | | 3 | "For a moment the former paramedic showed through the runner: tired, precise, a man who had once saved lives by breaking rules." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |