| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1105 | | totalAiIsmAdverbs | 3 | | 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) | |
| 72.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1105 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "lilt" | | 1 | "flicker" | | 2 | "footsteps" | | 3 | "flicked" | | 4 | "eyebrow" | | 5 | "scanned" |
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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 | 89 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 89 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1032 | | uniqueNames | 14 | | maxNameDensity | 0.87 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 9 | | Saint | 1 | | Christopher | 1 | | Spanish | 1 | | Herrera | 5 | | Road | 1 | | Tube | 2 | | Deptford | 1 | | Morris | 4 | | Yard | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Road" | | 4 | "Tube" | | 5 | "Deptford" |
| | globalScore | 1 | | windowScore | 1 | |
| 71.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like — she'd walked a dozen of the" | | 1 | "looked like teeth on a brass scale" |
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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 | 1111 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 29.24 | | std | 26.48 | | cv | 0.906 | | sampleLengths | | 0 | 75 | | 1 | 13 | | 2 | 53 | | 3 | 43 | | 4 | 46 | | 5 | 13 | | 6 | 30 | | 7 | 3 | | 8 | 2 | | 9 | 77 | | 10 | 12 | | 11 | 55 | | 12 | 3 | | 13 | 8 | | 14 | 9 | | 15 | 45 | | 16 | 5 | | 17 | 75 | | 18 | 12 | | 19 | 93 | | 20 | 21 | | 21 | 5 | | 22 | 13 | | 23 | 66 | | 24 | 7 | | 25 | 86 | | 26 | 19 | | 27 | 30 | | 28 | 3 | | 29 | 4 | | 30 | 7 | | 31 | 37 | | 32 | 34 | | 33 | 4 | | 34 | 5 | | 35 | 23 | | 36 | 31 | | 37 | 44 |
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| 89.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 89 | | matches | | 0 | "being chased" | | 1 | "been hacked" | | 2 | "was gone" | | 3 | "been lifted" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 183 | | matches | | 0 | "wasn't carrying" | | 1 | "was making" | | 2 | "wasn't looking" | | 3 | "was looking" | | 4 | "was weighing" | | 5 | "was watching" | | 6 | "was cutting" | | 7 | "wasn't running" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 99 | | ratio | 0.061 | | matches | | 0 | "Her lungs burned but her legs remembered how to do this — eighteen years of remembering." | | 1 | "When she straightened, Herrera was already at the mouth of the service stairs that plunged into the old Camden Road Tube entrance — the one boarded up since before she'd joined the force." | | 2 | "Her radio crackled — dispatch wanting an update, her DI wanting an update, the whole orderly architecture of the job wanting her to confirm she was still inside it." | | 3 | "She knew what those looked like — she'd walked a dozen of them on evidence runs." | | 4 | "Neither of those things explained what she had seen on the floor where Morris had been standing — the shape of him, burned into the concrete, as if he had been lifted out of his own outline and carried somewhere else." | | 5 | "She noted the temperature — warmer down here, like walking into a kitchen." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1027 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.021421616358325218 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006815968841285297 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 11.22 | | std | 8.54 | | cv | 0.761 | | sampleLengths | | 0 | 27 | | 1 | 29 | | 2 | 19 | | 3 | 7 | | 4 | 6 | | 5 | 3 | | 6 | 5 | | 7 | 23 | | 8 | 22 | | 9 | 3 | | 10 | 16 | | 11 | 15 | | 12 | 9 | | 13 | 3 | | 14 | 17 | | 15 | 11 | | 16 | 9 | | 17 | 6 | | 18 | 13 | | 19 | 24 | | 20 | 6 | | 21 | 3 | | 22 | 2 | | 23 | 10 | | 24 | 18 | | 25 | 33 | | 26 | 7 | | 27 | 2 | | 28 | 7 | | 29 | 6 | | 30 | 4 | | 31 | 2 | | 32 | 11 | | 33 | 14 | | 34 | 13 | | 35 | 17 | | 36 | 3 | | 37 | 8 | | 38 | 9 | | 39 | 7 | | 40 | 9 | | 41 | 29 | | 42 | 5 | | 43 | 9 | | 44 | 16 | | 45 | 6 | | 46 | 11 | | 47 | 16 | | 48 | 17 | | 49 | 5 |
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| 54.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.41414141414141414 | | totalSentences | 99 | | uniqueOpeners | 41 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 84 | | matches | | 0 | "Just for a breath." | | 1 | "Then he was gone, down" | | 2 | "Somewhere above, a night bus" |
| | ratio | 0.036 | |
| 62.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 84 | | matches | | 0 | "Her voice cracked against the" | | 1 | "He didn't stop." | | 2 | "He didn't even look back." | | 3 | "He jinked left around a" | | 4 | "Her lungs burned but her" | | 5 | "She bit down on the" | | 6 | "He threw it over his" | | 7 | "He vaulted a low fence" | | 8 | "He paused on the top" | | 9 | "He wasn't looking at her" | | 10 | "He was looking at her" | | 11 | "Her radio crackled — dispatch" | | 12 | "She looked down the stairs." | | 13 | "They weren't the stairs of" | | 14 | "She knew what those looked" | | 15 | "Her hand found the radio." | | 16 | "She thumbed the button." | | 17 | "She drew her baton, flicked" | | 18 | "She took it anyway." | | 19 | "She noted it." |
| | ratio | 0.393 | |
| 37.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 84 | | matches | | 0 | "Rain came down in sheets" | | 1 | "Detective Harlow Quinn ran with" | | 2 | "Her voice cracked against the" | | 3 | "He didn't stop." | | 4 | "He didn't even look back." | | 5 | "He jinked left around a" | | 6 | "The Saint Christopher she'd noted" | | 7 | "Quinn pushed harder." | | 8 | "Her lungs burned but her" | | 9 | "Morris used to joke that" | | 10 | "Morris wasn't here to joke" | | 11 | "The street narrowed." | | 12 | "Herrera cut between two parked" | | 13 | "Quinn slammed her shoulder off" | | 14 | "Water sluiced off a broken" | | 15 | "She bit down on the" | | 16 | "He threw it over his" | | 17 | "He vaulted a low fence" | | 18 | "Quinn cleared it less cleanly," | | 19 | "The plywood hoarding had been" |
| | ratio | 0.845 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 73.17% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 4 | | matches | | 0 | "Rain came down in sheets over Camden High Street, slicking the pavement into a black mirror that swallowed the neon and spat it back in broken colour." | | 1 | "Detective Harlow Quinn ran with her coat flapping at her knees, one hand steadying the radio clipped to her belt, the other braced for the sidearm she wasn't ca…" | | 2 | "The smell drifting up was woodsmoke and something sweeter, something that prickled the back of her throat." | | 3 | "Neither of those things explained what she had seen on the floor where Morris had been standing — the shape of him, burned into the concrete, as if he had been …" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 2 | | matches | | 0 | "He threw, the words clipped by his breathing, a Spanish lilt underneath" | | 1 | "He smiled, not unkindly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0 | | effectiveRatio | 0 | |