| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 20 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.125 | | effectiveRatio | 0.1 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1534 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 64.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1534 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "weight" | | 1 | "clandestine" | | 2 | "efficient" | | 3 | "warmth" | | 4 | "glint" | | 5 | "echoed" | | 6 | "flickered" | | 7 | "could feel" | | 8 | "whisper" | | 9 | "vibrated" |
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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 | 121 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 121 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1534 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 90.34% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1341 | | uniqueNames | 20 | | maxNameDensity | 1.19 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 2 | | Street | 2 | | Quinn | 16 | | Met | 1 | | Herrera | 9 | | Seville-born | 1 | | Raven | 2 | | Nest | 2 | | Soho | 2 | | Ferrier | 1 | | Estate | 1 | | Morris | 2 | | Northern | 1 | | London | 2 | | Transport | 1 | | Saint | 1 | | Christopher | 1 | | Seville | 2 | | Vice | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Met" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Morris" | | 5 | "Transport" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Seville-born" | | 4 | "Soho" | | 5 | "London" | | 6 | "Seville" | | 7 | "Vice" |
| | globalScore | 0.903 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.652 | | wordCount | 1534 | | matches | | 0 | "not like sewers but like incense" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 34.86 | | std | 26.75 | | cv | 0.767 | | sampleLengths | | 0 | 72 | | 1 | 65 | | 2 | 77 | | 3 | 86 | | 4 | 25 | | 5 | 14 | | 6 | 5 | | 7 | 30 | | 8 | 61 | | 9 | 2 | | 10 | 53 | | 11 | 55 | | 12 | 27 | | 13 | 16 | | 14 | 3 | | 15 | 85 | | 16 | 22 | | 17 | 69 | | 18 | 14 | | 19 | 53 | | 20 | 13 | | 21 | 49 | | 22 | 37 | | 23 | 9 | | 24 | 63 | | 25 | 7 | | 26 | 22 | | 27 | 30 | | 28 | 31 | | 29 | 2 | | 30 | 53 | | 31 | 13 | | 32 | 44 | | 33 | 108 | | 34 | 19 | | 35 | 37 | | 36 | 10 | | 37 | 6 | | 38 | 2 | | 39 | 44 | | 40 | 27 | | 41 | 52 | | 42 | 12 | | 43 | 10 |
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| 99.46% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 121 | | matches | | 0 | "were soaked" | | 1 | "been pried" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 221 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 133 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1353 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.018477457501847747 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004434589800443459 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 11.53 | | std | 9.2 | | cv | 0.797 | | sampleLengths | | 0 | 11 | | 1 | 28 | | 2 | 33 | | 3 | 8 | | 4 | 11 | | 5 | 6 | | 6 | 17 | | 7 | 7 | | 8 | 16 | | 9 | 25 | | 10 | 9 | | 11 | 7 | | 12 | 12 | | 13 | 24 | | 14 | 42 | | 15 | 22 | | 16 | 7 | | 17 | 5 | | 18 | 5 | | 19 | 5 | | 20 | 25 | | 21 | 5 | | 22 | 9 | | 23 | 5 | | 24 | 4 | | 25 | 26 | | 26 | 6 | | 27 | 20 | | 28 | 18 | | 29 | 4 | | 30 | 10 | | 31 | 3 | | 32 | 2 | | 33 | 23 | | 34 | 13 | | 35 | 13 | | 36 | 4 | | 37 | 30 | | 38 | 6 | | 39 | 19 | | 40 | 12 | | 41 | 15 | | 42 | 4 | | 43 | 9 | | 44 | 2 | | 45 | 1 | | 46 | 3 | | 47 | 2 | | 48 | 3 | | 49 | 41 |
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| 57.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.38345864661654133 | | totalSentences | 133 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 110 | | matches | (empty) | | ratio | 0 | |
| 74.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 110 | | matches | | 0 | "It hammered the shuttered stalls" | | 1 | "She had been standing there" | | 2 | "Her trousers were soaked to" | | 3 | "She had watched him two" | | 4 | "She had watched him come" | | 5 | "She knew what that meant." | | 6 | "She was sure of it." | | 7 | "His head snapped up." | | 8 | "He didn't run like a" | | 9 | "He ran like a man" | | 10 | "Her sharp jaw set." | | 11 | "Her boots hit the wet" | | 12 | "Her breath burned." | | 13 | "He cut left through a" | | 14 | "She didn't break stride." | | 15 | "He vaulted a low barrier" | | 16 | "Her brown eyes watered from" | | 17 | "she shouted, using his first" | | 18 | "It was a mistake." | | 19 | "He glanced back, and something" |
| | ratio | 0.364 | |
| 37.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 93 | | totalSentences | 110 | | matches | | 0 | "The rain hit Camden High" | | 1 | "It hammered the shuttered stalls" | | 2 | "Harlow Quinn stood under the" | | 3 | "She had been standing there" | | 4 | "Military precision, her old sergeant" | | 5 | "Her trousers were soaked to" | | 6 | "The worn leather watch on" | | 7 | "The man who came out" | | 8 | "Olive skin gone pale under" | | 9 | "Quinn knew the face from" | | 10 | "Tomas Herrera, twenty-nine, Seville-born, former" | | 11 | "She had watched him two" | | 12 | "She had watched him come" | | 13 | "A hidden back room for" | | 14 | "She knew what that meant." | | 15 | "The clique used the place." | | 16 | "She was sure of it." | | 17 | "Quinn stepped out from cover." | | 18 | "The rain found her instantly," | | 19 | "His head snapped up." |
| | ratio | 0.845 | |
| 90.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 110 | | matches | | 0 | "Now here he was in" | | 1 | "If she lost it tonight," |
| | ratio | 0.018 | |
| 44.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 7 | | matches | | 0 | "The man who came out of the rain at 23:43 was an inch taller than her own five-nine and moving fast with his head down." | | 1 | "Tomas Herrera, twenty-nine, Seville-born, former NHS paramedic struck off for administering unauthorized treatments to patients who, according to the reports, h…" | | 2 | "She had watched him come from behind the bookshelf, not the toilets, the bookshelf that didn't wobble right when the bass hit." | | 3 | "He ran like a man who carried weight for a living, efficient and low, bag strapped tight across his chest." | | 4 | "Quinn ran like a copper who had chased seventeen-year-olds over the Ferrier Estate and learned to love it." | | 5 | "Found in a service tunnel off the Northern line with his throat closed as if he had drowned on dry land and no water in his lungs and a look on his face Quinn s…" | | 6 | "Quinn heard a woman laughing, and something else beneath the music, a low hum that vibrated in her molars." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 20 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.143 | | effectiveRatio | 0.1 | |