| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 41 | | tagDensity | 0.195 | | leniency | 0.39 | | rawRatio | 0.125 | | effectiveRatio | 0.049 | |
| 96.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1268 | | 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) | |
| 64.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1268 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "measured" | | 1 | "familiar" | | 2 | "weight" | | 3 | "complex" | | 4 | "footsteps" | | 5 | "echoed" | | 6 | "charged" | | 7 | "gloom" | | 8 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 99 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1258 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.72% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 887 | | uniqueNames | 21 | | maxNameDensity | 1.47 | | worstName | "Herrera" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 12 | | Frith | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 13 | | Elephant | 1 | | Castle | 1 | | God | 1 | | Saint | 1 | | Christopher | 1 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Tube | 1 | | Wednesday | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Frith" | | 2 | "Street" | | 3 | "Elephant" | | 4 | "Castle" | | 5 | "God" | | 6 | "Chalk" | | 7 | "Farm" | | 8 | "Road" |
| | globalScore | 0.767 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.795 | | wordCount | 1258 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 22.46 | | std | 17.55 | | cv | 0.781 | | sampleLengths | | 0 | 57 | | 1 | 2 | | 2 | 49 | | 3 | 57 | | 4 | 52 | | 5 | 62 | | 6 | 22 | | 7 | 17 | | 8 | 46 | | 9 | 31 | | 10 | 31 | | 11 | 40 | | 12 | 22 | | 13 | 4 | | 14 | 17 | | 15 | 26 | | 16 | 6 | | 17 | 6 | | 18 | 27 | | 19 | 20 | | 20 | 19 | | 21 | 3 | | 22 | 33 | | 23 | 65 | | 24 | 4 | | 25 | 27 | | 26 | 2 | | 27 | 6 | | 28 | 2 | | 29 | 30 | | 30 | 35 | | 31 | 3 | | 32 | 7 | | 33 | 6 | | 34 | 18 | | 35 | 14 | | 36 | 10 | | 37 | 12 | | 38 | 22 | | 39 | 6 | | 40 | 45 | | 41 | 3 | | 42 | 13 | | 43 | 37 | | 44 | 4 | | 45 | 1 | | 46 | 35 | | 47 | 29 | | 48 | 8 | | 49 | 31 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 148 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 132 | | ratio | 0.053 | | matches | | 0 | "The figure glanced back—olive skin, dark curls plastered to his forehead—and picked up speed." | | 1 | "Tiles lined the walls—cream with a dark green border, the kind they'd used in the thirties." | | 2 | "A faint hum reached her ears—not electrical, something else." | | 3 | "The scar on his left forearm caught the light—pale against olive skin." | | 4 | "And this man—this unlicensed medic who treated the impossible—stood five metres away, holding the key." | | 5 | "The voices from the tunnel grew louder—someone calling out prices, haggling over goods." | | 6 | "The tunnel ahead glowed faintly—green and gold, like foxfire, like something from a dream she couldn't quite remember." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 896 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.022321428571428572 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0033482142857142855 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 9.53 | | std | 6.88 | | cv | 0.722 | | sampleLengths | | 0 | 15 | | 1 | 21 | | 2 | 21 | | 3 | 2 | | 4 | 14 | | 5 | 3 | | 6 | 12 | | 7 | 2 | | 8 | 10 | | 9 | 8 | | 10 | 3 | | 11 | 18 | | 12 | 11 | | 13 | 15 | | 14 | 10 | | 15 | 23 | | 16 | 11 | | 17 | 8 | | 18 | 10 | | 19 | 6 | | 20 | 17 | | 21 | 14 | | 22 | 11 | | 23 | 14 | | 24 | 19 | | 25 | 3 | | 26 | 5 | | 27 | 12 | | 28 | 7 | | 29 | 3 | | 30 | 14 | | 31 | 14 | | 32 | 8 | | 33 | 4 | | 34 | 16 | | 35 | 4 | | 36 | 7 | | 37 | 8 | | 38 | 14 | | 39 | 5 | | 40 | 4 | | 41 | 7 | | 42 | 14 | | 43 | 9 | | 44 | 10 | | 45 | 9 | | 46 | 5 | | 47 | 1 | | 48 | 1 | | 49 | 6 |
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| 48.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3409090909090909 | | totalSentences | 132 | | uniqueOpeners | 45 | |
| 35.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 94 | | matches | | 0 | "Instead, he slipped through the" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 94 | | matches | | 0 | "She'd staked out The Raven's" | | 1 | "She pushed harder." | | 2 | "Her worn leather watch slid" | | 3 | "She'd run down crackheads through" | | 4 | "She emerged onto Chalk Farm" | | 5 | "She sprinted across the road," | | 6 | "He didn't even look back." | | 7 | "It wasn't locked." | | 8 | "Her free hand found the" | | 9 | "Her footsteps echoed off the" | | 10 | "She moved forward, keeping her" | | 11 | "His jacket was soaked through," | | 12 | "His warm brown eyes met" | | 13 | "It was pity." | | 14 | "He lowered his hands slowly" | | 15 | "His voice dropped" | | 16 | "She thought of Morris." | | 17 | "Her partner's face when they'd" | | 18 | "She'd seen the terror frozen" | | 19 | "He studied her for a" |
| | ratio | 0.255 | |
| 23.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 94 | | matches | | 0 | "Detective Harlow Quinn's boots slapped" | | 1 | "The figure glanced back—olive skin," | | 2 | "Quinn's jaw tightened." | | 3 | "She'd staked out The Raven's" | | 4 | "The medic who'd vanished from" | | 5 | "The man who treated people" | | 6 | "She pushed harder." | | 7 | "Her worn leather watch slid" | | 8 | "Herrera was fast, but she'd" | | 9 | "She'd run down crackheads through" | | 10 | "Some former paramedic with a" | | 11 | "Herrera cut left down an" | | 12 | "Quinn followed, her hand moving" | | 13 | "The alley stank of rubbish" | | 14 | "Steam rose from a vent," | | 15 | "She emerged onto Chalk Farm" | | 16 | "The rain had thinned to" | | 17 | "Herrera was thirty metres ahead," | | 18 | "The one that had been" | | 19 | "The one that shouldn't have" |
| | ratio | 0.872 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 86.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 3 | | matches | | 0 | "His warm brown eyes met hers, and she saw something there that wasn't fear." | | 1 | "And this man—this unlicensed medic who treated the impossible—stood five metres away, holding the key." | | 2 | "Quinn followed, her torch cutting a path through the black, her heart hammering against her ribs." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "Herrera's hand moved, fingers closing around the small metal disc" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 41 | | tagDensity | 0.049 | | leniency | 0.098 | | rawRatio | 0.5 | | effectiveRatio | 0.049 | |