| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 2 | | adverbTags | | 0 | "\"—say again [again]" | | 1 | "he said softly [softly]" |
| | dialogueSentences | 17 | | tagDensity | 0.529 | | leniency | 1 | | rawRatio | 0.222 | | effectiveRatio | 0.222 | |
| 81.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1608 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "gently" | | 1 | "precisely" | | 2 | "very" | | 3 | "softly" |
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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) | |
| 78.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1608 | | totalAiIsms | 7 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "down her spine" | | count | 1 |
| | 4 | | | 5 | |
| | highlights | | 0 | "weight" | | 1 | "pumping" | | 2 | "absolutely" | | 3 | "down her spine" | | 4 | "resolved" | | 5 | "gloom" |
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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 | 112 | | matches | | |
| 40.82% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 3 | | narrationSentences | 112 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 71 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 12 | | totalWords | 1614 | | ratio | 0.007 | | matches | | 0 | "He's herding you" | | 1 | "lost contact with subject" | | 2 | "Radio it in." | | 3 | "Please" | | 4 | "Dios" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1477 | | uniqueNames | 25 | | maxNameDensity | 0.61 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Morris" | | discoveredNames | | Berwick | 1 | | Street | 3 | | Raven | 1 | | Nest | 1 | | Tomás | 1 | | Herrera | 4 | | Deptford | 1 | | Soho | 1 | | Cantonese | 1 | | Wardour | 1 | | Quinn | 9 | | Oxford | 1 | | Camden | 1 | | Lord | 1 | | Palmerston | 1 | | Mornington | 1 | | Crescent | 1 | | Morris | 5 | | Radio | 2 | | Rotherhithe | 2 | | English | 1 | | London | 2 | | Spanish | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Quinn" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Berwick" | | 1 | "Street" | | 2 | "Deptford" | | 3 | "Soho" | | 4 | "Cantonese" | | 5 | "Wardour" | | 6 | "Oxford" | | 7 | "Mornington" | | 8 | "Crescent" | | 9 | "Rotherhithe" | | 10 | "English" | | 11 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 77.54% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like DS Morris" | | 1 | "looked like plywood" |
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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 | 1614 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 120 | | matches | | 0 | "hate that colour" | | 1 | "left that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 37.53 | | std | 30.1 | | cv | 0.802 | | sampleLengths | | 0 | 62 | | 1 | 8 | | 2 | 84 | | 3 | 19 | | 4 | 69 | | 5 | 15 | | 6 | 8 | | 7 | 44 | | 8 | 10 | | 9 | 116 | | 10 | 12 | | 11 | 25 | | 12 | 6 | | 13 | 32 | | 14 | 89 | | 15 | 95 | | 16 | 14 | | 17 | 76 | | 18 | 23 | | 19 | 91 | | 20 | 2 | | 21 | 47 | | 22 | 54 | | 23 | 45 | | 24 | 55 | | 25 | 10 | | 26 | 70 | | 27 | 2 | | 28 | 16 | | 29 | 51 | | 30 | 5 | | 31 | 71 | | 32 | 40 | | 33 | 15 | | 34 | 26 | | 35 | 40 | | 36 | 3 | | 37 | 56 | | 38 | 53 | | 39 | 25 | | 40 | 9 | | 41 | 15 | | 42 | 6 |
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| 89.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 112 | | matches | | 0 | "been taken" | | 1 | "being prepared" | | 2 | "been taught" | | 3 | "was covered" | | 4 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 237 | | matches | | 0 | "was sleeping" | | 1 | "was coming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 120 | | ratio | 0.05 | | matches | | 0 | "Eleven nights of takeaway coffee and numb feet and a notebook full of faces she couldn't put names to, of people who went in and didn't come out, or came out looking wrong — pale, or too bright-eyed, or moving like something had been taken out of them and something else put back." | | 1 | "She'd built him out of scraps — a hospital HR file, a struck-off notice from the HCPC, a witness statement from a stabbing in Deptford where the victim had survived injuries the paramedic's own notes described as unsurvivable." | | 2 | "There was a half-second — she'd think about it later, that half-second, the way you think about a stair you nearly missed — where he simply looked at her and she looked back, two people acknowledging a shared piece of information." | | 3 | "\"—say again, Quinn, you're breaking up—\"" | | 4 | "Quinn followed and nearly went down on her hands — the tarmac was covered in something slick and dark, and the smell hit her, wet iron and burnt sugar." | | 5 | "Concrete stairs went down into the dark, and the dark was not empty — there was a glow at the bottom, low and amber and shifting, and the noise of it came up at her all at once now that the wood wasn't in the way: voices, hundreds, and something being hammered, and something laughing, and under it all a low bell-note like a finger on the rim of a glass." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 543 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Left, right, upper windows." |
| | adverbCount | 18 | | adverbRatio | 0.03314917127071823 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.014732965009208104 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 13.45 | | std | 13.3 | | cv | 0.989 | | sampleLengths | | 0 | 22 | | 1 | 40 | | 2 | 2 | | 3 | 6 | | 4 | 7 | | 5 | 53 | | 6 | 12 | | 7 | 5 | | 8 | 7 | | 9 | 19 | | 10 | 4 | | 11 | 38 | | 12 | 1 | | 13 | 9 | | 14 | 17 | | 15 | 11 | | 16 | 4 | | 17 | 8 | | 18 | 41 | | 19 | 3 | | 20 | 5 | | 21 | 5 | | 22 | 13 | | 23 | 34 | | 24 | 28 | | 25 | 41 | | 26 | 12 | | 27 | 7 | | 28 | 18 | | 29 | 6 | | 30 | 4 | | 31 | 7 | | 32 | 21 | | 33 | 9 | | 34 | 26 | | 35 | 17 | | 36 | 13 | | 37 | 24 | | 38 | 1 | | 39 | 1 | | 40 | 38 | | 41 | 6 | | 42 | 32 | | 43 | 4 | | 44 | 3 | | 45 | 5 | | 46 | 5 | | 47 | 14 | | 48 | 16 | | 49 | 31 |
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| 81.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.5416666666666666 | | totalSentences | 120 | | uniqueOpeners | 65 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 96 | | matches | | 0 | "Gently was worse than shouting." | | 1 | "Gently meant paperwork was being" | | 2 | "Then he ran." | | 3 | "Of course she was." | | 4 | "Then the long dark stretch" |
| | ratio | 0.052 | |
| 57.50% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 96 | | matches | | 0 | "She'd started to hate that" | | 1 | "Her governor had asked her" | | 2 | "She knew him instantly." | | 3 | "She'd built him out of" | | 4 | "He carried a canvas satchel" | | 5 | "He also, she noticed, checked" | | 6 | "His eyes stopped on the" | | 7 | "she shouted, already moving" | | 8 | "He was fast and he" | | 9 | "He went right, into the" | | 10 | "She kept her feet on" | | 11 | "He was thirty metres up," | | 12 | "She hit the radio at" | | 13 | "She always broke up around" | | 14 | "She'd stopped believing in coincidence" | | 15 | "He took Oxford Street at" | | 16 | "Her coat was three stone" | | 17 | "She'd smoked for eleven years" | | 18 | "He didn't try to lose" | | 19 | "He kept looking back." |
| | ratio | 0.406 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 96 | | matches | | 0 | "The rain had been going" | | 1 | "Quinn stood in the doorway" | | 2 | "She'd started to hate that" | | 3 | "Her governor had asked her" | | 4 | "She knew him instantly." | | 5 | "She'd built him out of" | | 6 | "Olive-skinned, short curly hair going" | | 7 | "He carried a canvas satchel" | | 8 | "He also, she noticed, checked" | | 9 | "His eyes stopped on the" | | 10 | "she shouted, already moving" | | 11 | "He was fast and he" | | 12 | "He went right, into the" | | 13 | "Quinn came through six seconds" | | 14 | "She kept her feet on" | | 15 | "He was thirty metres up," | | 16 | "She hit the radio at" | | 17 | "\"—say again, Quinn, you're breaking" | | 18 | "She always broke up around" | | 19 | "She'd stopped believing in coincidence" |
| | ratio | 0.688 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 51.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 6 | | matches | | 0 | "Eleven nights of takeaway coffee and numb feet and a notebook full of faces she couldn't put names to, of people who went in and didn't come out, or came out lo…" | | 1 | "Then the long dark stretch up towards Camden, past the shuttered fried chicken shops and the scaffolding and the men in the doorway of the Lord Palmerston who c…" | | 2 | "That was the thing that started to bother her, somewhere around Mornington Crescent, when the stitch in her side had become an actual knife and her breath was c…" | | 3 | "A dry basement, four walls, and Morris's warrant card sitting in the middle of the floor, absolutely flat, absolutely centred, as if placed." | | 4 | "Everyone who'd worked London long enough knew about the disused stations, the war-time deep shelters, the tunnels that didn't appear on any public map." | | 5 | "She looked at her phone: no bars, and the clock reading 11:34, which was wrong, which was the time she'd looked at her watch outside the bar, and she felt somet…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 17 | | tagDensity | 0.294 | | leniency | 0.588 | | rawRatio | 0.2 | | effectiveRatio | 0.118 | |