| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.46% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1414 | | 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) | |
| 57.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1414 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "echoes" | | 1 | "echoed" | | 2 | "shimmered" | | 3 | "depths" | | 4 | "chill" | | 5 | "electric" | | 6 | "scanned" | | 7 | "dancing" | | 8 | "glinting" | | 9 | "eyebrow" | | 10 | "pulse" | | 11 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 152 | | matches | (empty) | |
| 95.86% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 176 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1405 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.17% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1211 | | uniqueNames | 18 | | maxNameDensity | 1.82 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | London | 1 | | Quinn | 1 | | West | 1 | | End | 1 | | Morris | 1 | | Camden | 1 | | Blitz | 1 | | Tube | 1 | | Tomás | 9 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Harlow | 22 | | Casual | 1 | | Queen | 1 | | Swords | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Harlow" | | 7 | "Queen" |
| | places | | 0 | "London" | | 1 | "West" | | 2 | "End" | | 3 | "Market" |
| | globalScore | 0.592 | | windowScore | 0.833 | |
| 64.77% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like it hadn't opened since the Bl" | | 1 | "knife that seemed to absorb the light around it" | | 2 | "felt like it was burning a hole through" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.712 | | wordCount | 1405 | | matches | | 0 | "not a storage room, but a set of concrete stairs spiralling down into darkness" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 176 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 18.49 | | std | 16.62 | | cv | 0.899 | | sampleLengths | | 0 | 57 | | 1 | 3 | | 2 | 39 | | 3 | 28 | | 4 | 47 | | 5 | 52 | | 6 | 27 | | 7 | 10 | | 8 | 4 | | 9 | 37 | | 10 | 29 | | 11 | 3 | | 12 | 7 | | 13 | 17 | | 14 | 27 | | 15 | 7 | | 16 | 5 | | 17 | 12 | | 18 | 3 | | 19 | 21 | | 20 | 7 | | 21 | 47 | | 22 | 14 | | 23 | 7 | | 24 | 8 | | 25 | 79 | | 26 | 22 | | 27 | 7 | | 28 | 2 | | 29 | 5 | | 30 | 41 | | 31 | 22 | | 32 | 13 | | 33 | 12 | | 34 | 4 | | 35 | 68 | | 36 | 15 | | 37 | 4 | | 38 | 22 | | 39 | 18 | | 40 | 56 | | 41 | 29 | | 42 | 15 | | 43 | 30 | | 44 | 11 | | 45 | 5 | | 46 | 22 | | 47 | 13 | | 48 | 19 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 217 | | matches | | 0 | "were heading" | | 1 | "was burning" | | 2 | "was vanishing" |
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| 94.16% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 176 | | ratio | 0.017 | | matches | | 0 | "The suspect—a hooded figure clutching a heavy canvas satchel—vaulted a bollard and scrambled over the bonnet of a parked taxi." | | 1 | "The metal door didn't swing open; it dissolved." | | 2 | "Figures moved between the stalls—some human, others..." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1220 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.021311475409836064 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005737704918032787 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 176 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 176 | | mean | 7.98 | | std | 5.39 | | cv | 0.675 | | sampleLengths | | 0 | 14 | | 1 | 23 | | 2 | 20 | | 3 | 3 | | 4 | 3 | | 5 | 5 | | 6 | 15 | | 7 | 16 | | 8 | 4 | | 9 | 11 | | 10 | 1 | | 11 | 12 | | 12 | 12 | | 13 | 10 | | 14 | 11 | | 15 | 6 | | 16 | 5 | | 17 | 3 | | 18 | 14 | | 19 | 9 | | 20 | 9 | | 21 | 20 | | 22 | 19 | | 23 | 8 | | 24 | 6 | | 25 | 4 | | 26 | 4 | | 27 | 17 | | 28 | 10 | | 29 | 10 | | 30 | 12 | | 31 | 9 | | 32 | 8 | | 33 | 3 | | 34 | 7 | | 35 | 5 | | 36 | 5 | | 37 | 2 | | 38 | 5 | | 39 | 5 | | 40 | 22 | | 41 | 3 | | 42 | 4 | | 43 | 5 | | 44 | 5 | | 45 | 7 | | 46 | 3 | | 47 | 6 | | 48 | 7 | | 49 | 2 |
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| 37.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.3063583815028902 | | totalSentences | 173 | | uniqueOpeners | 53 | |
| 24.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 137 | | matches | | | ratio | 0.007 | |
| 85.69% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 137 | | matches | | 0 | "She didn't have the breath." | | 1 | "She followed the splashing echoes" | | 2 | "She wouldn't lose this one." | | 3 | "They were heading north, toward" | | 4 | "He held the satchel against" | | 5 | "She kept it holstered, but" | | 6 | "She took a step forward." | | 7 | "He reached into his pocket." | | 8 | "He slammed it against the" | | 9 | "Her finger tightened on the" | | 10 | "Her heart hammered against her" | | 11 | "She looked at the space" | | 12 | "She holstered her weapon." | | 13 | "They'd suspend her." | | 14 | "They'd bury the file." | | 15 | "She looked at her watch" | | 16 | "She stepped through the haze." | | 17 | "It was a market." | | 18 | "Her brain scrambled for a" | | 19 | "She had arrested him twice" |
| | ratio | 0.336 | |
| 11.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 123 | | totalSentences | 137 | | matches | | 0 | "Rain lashed against the asphalt," | | 1 | "Harlow Quinn pumped her arms," | | 2 | "The suspect—a hooded figure clutching" | | 3 | "Harlow didn't shout." | | 4 | "She didn't have the breath." | | 5 | "The words barely left her" | | 6 | "The hood slipped between two" | | 7 | "Harlow checked her wrist." | | 8 | "The worn leather of her" | | 9 | "The city was asleep, but" | | 10 | "She followed the splashing echoes" | | 11 | "The smell of rot and" | | 12 | "The suspect was fast, unnaturally" | | 13 | "Harlow grit her teeth, jawline" | | 14 | "She wouldn't lose this one." | | 15 | "The hooded figure skidded around" | | 16 | "Harlow pushed harder, ignoring the" | | 17 | "They were heading north, toward" | | 18 | "The ambient noise of the" | | 19 | "The suspect ducked through a" |
| | ratio | 0.898 | |
| 36.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 137 | | matches | | 0 | "If she called this in," |
| | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 3 | | matches | | 0 | "Harlow Quinn pumped her arms, her boots hammering the pavement with a rhythm that belonged on a drill field, not a midnight pursuit." | | 1 | "The alley dead-ended against a rusted metal door that looked like it hadn't opened since the Blitz." | | 2 | "The stairs were steep, lit by flickering bulbs that buzzed with an angry electric hum." |
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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 | |