| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.625 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1174 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "gently" | | 1 | "completely" | | 2 | "quickly" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 78.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1174 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "database" | | 2 | "stomach" | | 3 | "electric" | | 4 | "depths" |
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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 | 48 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 48 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 51 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 75 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1169 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1065 | | uniqueNames | 14 | | maxNameDensity | 0.56 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 6 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Eighteen | 1 | | Webb | 4 | | Tuesday | 1 | | Camden | 1 | | Morris | 1 | | Mayfair | 1 | | Tube | 1 | | London | 2 | | Transport | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Webb" | | 3 | "Morris" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 25.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 2 | | matches | | 0 | "d up and then, evidently, un-bricked by some" | | 1 | "something like copper, and beneath that some" |
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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 | 1169 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 51 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 55.67 | | std | 37.1 | | cv | 0.666 | | sampleLengths | | 0 | 70 | | 1 | 45 | | 2 | 9 | | 3 | 115 | | 4 | 8 | | 5 | 69 | | 6 | 76 | | 7 | 117 | | 8 | 10 | | 9 | 125 | | 10 | 4 | | 11 | 84 | | 12 | 108 | | 13 | 32 | | 14 | 38 | | 15 | 29 | | 16 | 44 | | 17 | 39 | | 18 | 75 | | 19 | 38 | | 20 | 34 |
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| 90.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 48 | | matches | | 0 | "been bricked" | | 1 | "got written" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 166 | | matches | | 0 | "wasn't laughing" | | 1 | "was still functioning" |
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| 30.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 51 | | ratio | 0.039 | | matches | | 0 | "Ahead of her, a shape in a dark hoodie cut between two parked cars, vaulted a low wall with an ease that struck her as wrong—too fluid, too fast for a man his size—and vanished into the alley behind a shuttered chip shop." | | 1 | "There were people down there—if they were people—moving between the stalls with unhurried purpose, examining jars and bundled herbs and things that glittered wrong in the lantern light." |
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| 88.08% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 317 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.05362776025236593 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.00946372239747634 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 51 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 51 | | mean | 22.92 | | std | 16.98 | | cv | 0.741 | | sampleLengths | | 0 | 27 | | 1 | 43 | | 2 | 17 | | 3 | 28 | | 4 | 3 | | 5 | 6 | | 6 | 30 | | 7 | 2 | | 8 | 33 | | 9 | 8 | | 10 | 3 | | 11 | 39 | | 12 | 3 | | 13 | 5 | | 14 | 58 | | 15 | 7 | | 16 | 4 | | 17 | 13 | | 18 | 32 | | 19 | 23 | | 20 | 8 | | 21 | 37 | | 22 | 24 | | 23 | 7 | | 24 | 49 | | 25 | 10 | | 26 | 29 | | 27 | 17 | | 28 | 53 | | 29 | 26 | | 30 | 4 | | 31 | 42 | | 32 | 20 | | 33 | 22 | | 34 | 59 | | 35 | 28 | | 36 | 21 | | 37 | 9 | | 38 | 19 | | 39 | 4 | | 40 | 20 | | 41 | 18 | | 42 | 22 | | 43 | 7 | | 44 | 37 | | 45 | 7 | | 46 | 39 | | 47 | 75 | | 48 | 6 | | 49 | 32 |
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| 61.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.45098039215686275 | | totalSentences | 51 | | uniqueOpeners | 23 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 47 | | matches | | 0 | "Maybe it did." | | 1 | "Maybe more than her career." | | 2 | "Somewhere three years ago, in" | | 3 | "Then he turned and walked" | | 4 | "Then she put her hand" |
| | ratio | 0.106 | |
| 32.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 47 | | matches | | 0 | "Her voice cracked against the" | | 1 | "He didn't stop." | | 2 | "He never did, the good" | | 3 | "It had, eventually." | | 4 | "It was why she was" | | 5 | "She'd pulled his name from" | | 6 | "She'd laughed at that in" | | 7 | "She wasn't laughing now." | | 8 | "He went up it like" | | 9 | "She landed hard, jarred her" | | 10 | "She'd found what was left" | | 11 | "She was not going to" | | 12 | "She went in anyway." | | 13 | "She could smell woodsmoke, and" | | 14 | "She heard a bell ring," | | 15 | "He turned, once, and looked" | | 16 | "It was almost pity." | | 17 | "he said, and his voice" | | 18 | "Her hand found the grip" | | 19 | "he said, almost gently, He" |
| | ratio | 0.468 | |
| 77.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 47 | | matches | | 0 | "The rain came down in" | | 1 | "Her voice cracked against the" | | 2 | "He didn't stop." | | 3 | "He never did, the good" | | 4 | "Quinn's boots hit standing water" | | 5 | "That last one should have" | | 6 | "It had, eventually." | | 7 | "It was why she was" | | 8 | "She'd pulled his name from" | | 9 | "She'd laughed at that in" | | 10 | "She wasn't laughing now." | | 11 | "Webb hit the end of" | | 12 | "He went up it like" | | 13 | "Quinn swore under her breath" | | 14 | "She landed hard, jarred her" | | 15 | "The rain was worse down" | | 16 | "She'd found what was left" | | 17 | "She was not going to" | | 18 | "Webb ducked through a gap" | | 19 | "Quinn slowed at the mouth" |
| | ratio | 0.766 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 9 | | matches | | 0 | "Ahead of her, a shape in a dark hoodie cut between two parked cars, vaulted a low wall with an ease that struck her as wrong—too fluid, too fast for a man his s…" | | 1 | "Petty thief, sometimes police informant, a man she'd hauled in twice for receiving stolen goods and once, memorably, for possession of a substance that hadn't s…" | | 2 | "Quinn swore under her breath and followed, her hands raw against the wet chain-link, her jacket catching and tearing on the top wire." | | 3 | "She'd found what was left of him in a stairwell that smelled the same way that Mayfair club had smelled, and the official report had called it an animal attack,…" | | 4 | "There were disused stations all over London, mothballed relics of lines rerouted decades ago, and she knew, in the dim procedural part of her brain that was sti…" | | 5 | "The kind of decision that got detectives killed, or worse, got them found six months later with no explanation anyone could stomach putting in a file." | | 6 | "Quinn stood at the top of a set of stairs that shouldn't have existed on any Transport for London schematic she'd ever pulled, and looked down into a platform l…" | | 7 | "There were people down there—if they were people—moving between the stalls with unhurried purpose, examining jars and bundled herbs and things that glittered wr…" | | 8 | "Then he turned and walked into the crowd, unhurried now, and within three seconds she'd lost him completely among the stalls and the shifting lantern light, as …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |