| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.211 | | leniency | 0.421 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 710 | | 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) | |
| 36.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 710 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoed" | | 1 | "depths" | | 2 | "resolved" | | 3 | "flickered" | | 4 | "wavering" | | 5 | "shattered" | | 6 | "glinting" | | 7 | "trembled" | | 8 | "pulse" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 87 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 16 | | totalWords | 697 | | ratio | 0.023 | | matches | | 0 | "Camden Transport Authority, No Entry" | | 1 | "Move or lose him." | | 2 | "Market Rules – No Bone, No Bargain" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 6.90% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 594 | | uniqueNames | 15 | | maxNameDensity | 2.86 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 17 | | Transport | 1 | | Authority | 1 | | Rules | 1 | | Bone | 1 | | Veil | 1 | | Market | 2 | | Tube | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Tomás | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Raven" | | 7 | "Nest" | | 8 | "Tomás" |
| | places | (empty) | | globalScore | 0.069 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 697 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 15.49 | | std | 11.21 | | cv | 0.724 | | sampleLengths | | 0 | 35 | | 1 | 4 | | 2 | 4 | | 3 | 33 | | 4 | 40 | | 5 | 8 | | 6 | 23 | | 7 | 15 | | 8 | 4 | | 9 | 2 | | 10 | 37 | | 11 | 12 | | 12 | 14 | | 13 | 1 | | 14 | 27 | | 15 | 8 | | 16 | 8 | | 17 | 25 | | 18 | 45 | | 19 | 21 | | 20 | 8 | | 21 | 12 | | 22 | 11 | | 23 | 3 | | 24 | 20 | | 25 | 5 | | 26 | 5 | | 27 | 24 | | 28 | 5 | | 29 | 10 | | 30 | 21 | | 31 | 13 | | 32 | 5 | | 33 | 28 | | 34 | 15 | | 35 | 11 | | 36 | 6 | | 37 | 33 | | 38 | 22 | | 39 | 17 | | 40 | 20 | | 41 | 8 | | 42 | 18 | | 43 | 4 | | 44 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 111 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 87 | | ratio | 0.115 | | matches | | 0 | "The suspect—male, late twenties, the collar of his jacket turned up—darted left into a narrow alley." | | 1 | "The air thickened with mildew and something sharper—incense, maybe, or charred meat." | | 2 | "A figure stood guard—tall, wearing a moth-eaten greatcoat." | | 3 | "He tapped a sign hammered into the brickwork: *Market Rules – No Bone, No Bargain*." | | 4 | "A shout erupted deeper in the market—the sharp, startled kind." | | 5 | "The crowd—a mix of too-pale faces and too-sharp grins—parted as Quinn barged through." | | 6 | "Quinn lost sight of the suspect in the sudden chaos—people lunging for exits, stalls collapsing under the surge of bodies." | | 7 | "\"Is gone. And so are we.\" He tossed something small and white to the guard now blocking their exit—a tooth, maybe." | | 8 | "A thought struck her—his clinic was four blocks from here." | | 9 | "Then a shadow peeled itself from the alley behind Tomás—tall, too tall, moving wrong—" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 607 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.019769357495881382 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 87 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 87 | | mean | 8.01 | | std | 5.27 | | cv | 0.658 | | sampleLengths | | 0 | 20 | | 1 | 15 | | 2 | 4 | | 3 | 4 | | 4 | 17 | | 5 | 16 | | 6 | 9 | | 7 | 13 | | 8 | 18 | | 9 | 5 | | 10 | 1 | | 11 | 2 | | 12 | 4 | | 13 | 12 | | 14 | 7 | | 15 | 9 | | 16 | 6 | | 17 | 4 | | 18 | 2 | | 19 | 12 | | 20 | 5 | | 21 | 12 | | 22 | 8 | | 23 | 5 | | 24 | 7 | | 25 | 10 | | 26 | 4 | | 27 | 1 | | 28 | 15 | | 29 | 12 | | 30 | 4 | | 31 | 4 | | 32 | 8 | | 33 | 10 | | 34 | 7 | | 35 | 8 | | 36 | 17 | | 37 | 15 | | 38 | 13 | | 39 | 21 | | 40 | 5 | | 41 | 3 | | 42 | 3 | | 43 | 6 | | 44 | 3 | | 45 | 7 | | 46 | 2 | | 47 | 2 | | 48 | 3 | | 49 | 20 |
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| 77.01% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4827586206896552 | | totalSentences | 87 | | uniqueOpeners | 42 | |
| 99.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 67 | | matches | | 0 | "Then a vendor tipped over" | | 1 | "Then a shadow peeled itself" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 67 | | matches | | 0 | "Her watch strap chafed against" | | 1 | "She pulled her torch." | | 2 | "He tapped a sign hammered" | | 3 | "His hand slid toward his" | | 4 | "She caught a glimpse of" | | 5 | "His Saint Christopher medallion swung" | | 6 | "He tossed something small and" | | 7 | "They burst onto the street" | | 8 | "His fingertips smelled of antiseptic" | | 9 | "She debated cuffing him." |
| | ratio | 0.149 | |
| 27.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 67 | | matches | | 0 | "Detective Harlow Quinn's boot slammed" | | 1 | "The figure didn't hesitate." | | 2 | "Quinn's breath came hard as" | | 3 | "The suspect—male, late twenties, the" | | 4 | "The alley stank of rotting" | | 5 | "Her watch strap chafed against" | | 6 | "Quinn skidded to a halt." | | 7 | "She pulled her torch." | | 8 | "The beam cut through the" | | 9 | "The door creaked when she" | | 10 | "A staircase yawned below, its" | | 11 | "Murmurs echoed up from the" | | 12 | "The air thickened with mildew" | | 13 | "The murmur resolved into voices." | | 14 | "A figure stood guard—tall, wearing" | | 15 | "Quinn flipped open her badge." | | 16 | "The guard leaned in, examining" | | 17 | "He tapped a sign hammered" | | 18 | "The shadows behind him shifted," | | 19 | "Quinn clenched her jaw." |
| | ratio | 0.866 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |