| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1129 | | 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) | |
| 77.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1129 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "throbbed" | | 1 | "structure" | | 2 | "silence" | | 3 | "electric" | | 4 | "flickered" |
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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 | 103 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | 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 | 1134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1095 | | uniqueNames | 20 | | maxNameDensity | 0.55 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Herrera" | | discoveredNames | | Herrera | 5 | | Camden | 2 | | High | 1 | | Street | 1 | | Town | 2 | | Tube | 2 | | Kew | 1 | | Morris | 3 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Kentish | 1 | | Edwardian | 1 | | English | 1 | | Detective | 1 | | Chief | 1 | | Inspector | 1 | | Harlow | 1 | | Quinn | 6 | | Somebody | 4 |
| | persons | | 0 | "Herrera" | | 1 | "Morris" | | 2 | "Quinn" | | 3 | "Somebody" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Town" | | 4 | "Tube" | | 5 | "Kew" | | 6 | "Raven" | | 7 | "Soho" | | 8 | "Kentish" |
| | globalScore | 1 | | windowScore | 1 | |
| 74.24% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like cold iron and something sweet" | | 1 | "smelled like that" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.882 | | wordCount | 1134 | | matches | | 0 | "not away from something but toward it" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 30.65 | | std | 23.7 | | cv | 0.773 | | sampleLengths | | 0 | 37 | | 1 | 3 | | 2 | 40 | | 3 | 5 | | 4 | 31 | | 5 | 23 | | 6 | 39 | | 7 | 68 | | 8 | 19 | | 9 | 61 | | 10 | 9 | | 11 | 55 | | 12 | 15 | | 13 | 9 | | 14 | 66 | | 15 | 7 | | 16 | 23 | | 17 | 17 | | 18 | 9 | | 19 | 69 | | 20 | 18 | | 21 | 4 | | 22 | 72 | | 23 | 7 | | 24 | 36 | | 25 | 6 | | 26 | 25 | | 27 | 70 | | 28 | 3 | | 29 | 55 | | 30 | 7 | | 31 | 74 | | 32 | 44 | | 33 | 6 | | 34 | 51 | | 35 | 41 | | 36 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 103 | | matches | | |
| 91.30% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 184 | | matches | | 0 | "were hammering" | | 1 | "was looking" | | 2 | "was telling" |
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| 61.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 106 | | ratio | 0.028 | | matches | | 0 | "A draught from somewhere further in, carrying voices — not English, not any one language, a low murmur that braided together and came apart." | | 1 | "Another figure — tall, wrong somehow in the shoulders — leaning on a pillar with a cigarette that gave off blue smoke." | | 2 | "The other part of her — the part that still heard Morris on the phone three years ago, saying Harlow, it's not what we think it is, it's not — that part took a slow breath and thumbed the torch off." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 378 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.015873015873015872 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0026455026455026454 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 10.7 | | std | 8.56 | | cv | 0.8 | | sampleLengths | | 0 | 18 | | 1 | 19 | | 2 | 3 | | 3 | 17 | | 4 | 17 | | 5 | 3 | | 6 | 3 | | 7 | 5 | | 8 | 3 | | 9 | 3 | | 10 | 25 | | 11 | 4 | | 12 | 13 | | 13 | 6 | | 14 | 6 | | 15 | 8 | | 16 | 25 | | 17 | 5 | | 18 | 15 | | 19 | 6 | | 20 | 4 | | 21 | 25 | | 22 | 13 | | 23 | 10 | | 24 | 9 | | 25 | 9 | | 26 | 21 | | 27 | 13 | | 28 | 4 | | 29 | 3 | | 30 | 11 | | 31 | 9 | | 32 | 4 | | 33 | 28 | | 34 | 6 | | 35 | 17 | | 36 | 15 | | 37 | 9 | | 38 | 6 | | 39 | 25 | | 40 | 12 | | 41 | 23 | | 42 | 7 | | 43 | 23 | | 44 | 3 | | 45 | 4 | | 46 | 8 | | 47 | 2 | | 48 | 3 | | 49 | 1 |
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| 58.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.42452830188679247 | | totalSentences | 106 | | uniqueOpeners | 45 | |
| 70.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 94 | | matches | | 0 | "Just vaulted the low railing" | | 1 | "Then to her sidearm, which" |
| | ratio | 0.021 | |
| 75.32% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 94 | | matches | | 0 | "He had twenty metres on" | | 1 | "She clocked the way he" | | 2 | "He didn't turn." | | 3 | "You ran, or you lost" | | 4 | "Her radio crackled at her" | | 5 | "She'd stopped listening to it" | | 6 | "She followed, shoulder scraping brick," | | 7 | "He didn't look back." | | 8 | "He ran like he had" | | 9 | "She kept her voice low" | | 10 | "She lost him for three" | | 11 | "He slipped through a gap" | | 12 | "Her hand went to her" | | 13 | "Her left wrist throbbed where" | | 14 | "She tried again." | | 15 | "She stood on the top" | | 16 | "It didn't smell like an" | | 17 | "It didn't smell like damp" | | 18 | "It smelled like cold iron" | | 19 | "She'd put Herrera on the" |
| | ratio | 0.362 | |
| 61.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 94 | | matches | | 0 | "Rain came down in sheets," | | 1 | "Quinn moved at a hard" | | 2 | "He had twenty metres on" | | 3 | "She clocked the way he" | | 4 | "A paramedic's legs." | | 5 | "A runner's lungs." | | 6 | "He didn't turn." | | 7 | "Quinn went after him." | | 8 | "You ran, or you lost" | | 9 | "Her radio crackled at her" | | 10 | "She'd stopped listening to it" | | 11 | "Dispatch wanted a unit, dispatch" | | 12 | "Herrera ducked into an alley." | | 13 | "She followed, shoulder scraping brick," | | 14 | "A bin went over behind" | | 15 | "He didn't look back." | | 16 | "He ran like he had" | | 17 | "A man with a plan" | | 18 | "She kept her voice low" | | 19 | "The alley spat them out" |
| | ratio | 0.798 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 6 | | matches | | 0 | "Just vaulted the low railing outside the shuttered chemist and cut south, his trainers kicking up a spray that caught the light like struck flint." | | 1 | "He ran like he had somewhere specific to be, not away from something but toward it, and that was the thing that bothered her most." | | 2 | "It smelled like cold iron and something sweet underneath, something that reminded her, absurdly, of the greenhouse at Kew where her mother had taken her as a ch…" | | 3 | "Posters half-peeled from the walls, advertising films that had come out when she was at school." | | 4 | "A draught from somewhere further in, carrying voices — not English, not any one language, a low murmur that braided together and came apart." | | 5 | "She didn't have a name for what she was looking at, and the part of her that had made Detective Chief Inspector at thirty-eight was telling her, very clearly, t…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |