| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1032 | | 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) | |
| 51.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1032 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "glistening" | | 2 | "stomach" | | 3 | "lurch" | | 4 | "echoing" | | 5 | "wavering" | | 6 | "pulsed" | | 7 | "flicked" | | 8 | "electric" | | 9 | "silence" |
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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 | 1 | | narrationSentences | 85 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 85 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 10 | | markdownWords | 17 | | totalWords | 1018 | | ratio | 0.017 | | matches | | 0 | "Tomás Herrera." | | 1 | "The Raven’s Nest" | | 2 | "Too easy." | | 3 | "Camden Tube station." | | 4 | "that" | | 5 | "fun" | | 6 | "shimmer" | | 7 | "arrest" | | 8 | "wrong" | | 9 | "no idea" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 928 | | uniqueNames | 6 | | maxNameDensity | 1.19 | | worstName | "Herrera" | | maxWindowNameDensity | 3 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Quinn | 9 | | Raven | 1 | | Herrera | 11 | | Tube | 1 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Morris" |
| | places | | | globalScore | 0.907 | | windowScore | 0.667 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like burnt sugar and copper" | | 1 | "looked like teeth—too many, too sharp, ar" |
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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 | 1018 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 98 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 27.51 | | std | 22.04 | | cv | 0.801 | | sampleLengths | | 0 | 60 | | 1 | 67 | | 2 | 57 | | 3 | 64 | | 4 | 10 | | 5 | 45 | | 6 | 38 | | 7 | 16 | | 8 | 75 | | 9 | 9 | | 10 | 65 | | 11 | 29 | | 12 | 7 | | 13 | 7 | | 14 | 16 | | 15 | 22 | | 16 | 7 | | 17 | 62 | | 18 | 56 | | 19 | 15 | | 20 | 5 | | 21 | 10 | | 22 | 27 | | 23 | 3 | | 24 | 21 | | 25 | 5 | | 26 | 41 | | 27 | 12 | | 28 | 11 | | 29 | 52 | | 30 | 13 | | 31 | 12 | | 32 | 9 | | 33 | 17 | | 34 | 27 | | 35 | 5 | | 36 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 85 | | matches | | |
| 74.21% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 159 | | matches | | 0 | "was slipping was crawling" | | 1 | "was trying" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 98 | | ratio | 0.082 | | matches | | 0 | "The suspect—lean, wiry, moving like a rat with a firecracker up its arse—ducked around a corner, his dark jacket flapping behind him like a broken wing." | | 1 | "Then she saw it—the manhole cover, half-pried open, its edges glistening with fresh scratches." | | 2 | "The drop was shorter than she expected—maybe six feet—but the impact still jarred her knees." | | 3 | "The tunnel sloped downward, the tiles giving way to bare concrete, the posters to graffiti—symbols she didn’t recognise, jagged and angular, like something scratched into stone by desperate hands." | | 4 | "Then she heard it—a scuffle, the clatter of something metal hitting the ground." | | 5 | "A table covered in what looked like teeth—too many, too sharp, arranged in neat little rows." | | 6 | "His eyes—pale, pupil-less—locked onto Herrera’s face." | | 7 | "But this—this was different." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 942 | | adjectiveStacks | 1 | | stackExamples | | 0 | "suspect—lean, wiry, moving like" |
| | adverbCount | 36 | | adverbRatio | 0.03821656050955414 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 10.39 | | std | 7.95 | | cv | 0.766 | | sampleLengths | | 0 | 18 | | 1 | 16 | | 2 | 26 | | 3 | 7 | | 4 | 17 | | 5 | 17 | | 6 | 26 | | 7 | 7 | | 8 | 50 | | 9 | 15 | | 10 | 21 | | 11 | 24 | | 12 | 4 | | 13 | 8 | | 14 | 2 | | 15 | 14 | | 16 | 20 | | 17 | 11 | | 18 | 7 | | 19 | 6 | | 20 | 4 | | 21 | 21 | | 22 | 16 | | 23 | 15 | | 24 | 20 | | 25 | 15 | | 26 | 25 | | 27 | 9 | | 28 | 13 | | 29 | 29 | | 30 | 4 | | 31 | 19 | | 32 | 13 | | 33 | 11 | | 34 | 1 | | 35 | 2 | | 36 | 2 | | 37 | 7 | | 38 | 7 | | 39 | 7 | | 40 | 9 | | 41 | 6 | | 42 | 11 | | 43 | 5 | | 44 | 7 | | 45 | 12 | | 46 | 15 | | 47 | 9 | | 48 | 10 | | 49 | 16 |
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| 45.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.336734693877551 | | totalSentences | 98 | | uniqueOpeners | 33 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 76 | | matches | | 0 | "Then she saw it—the manhole" | | 1 | "Then she heard it—a scuffle," | | 2 | "Then the tall man smiled." |
| | ratio | 0.039 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 76 | | matches | | 0 | "She didn’t need to see" | | 1 | "She knew that gait, the" | | 2 | "She skidded around the corner," | | 3 | "She crouched, fingers brushing the" | | 4 | "Her boots splashed into an" | | 5 | "She moved fast, her breath" | | 6 | "She didn’t need to see" | | 7 | "She risked a glance around" | | 8 | "He stood with his back" | | 9 | "His eyes—pale, pupil-less—locked onto Herrera’s" | | 10 | "His voice was like dry" | | 11 | "She didn’t hesitate." | | 12 | "She stepped out, her baton" | | 13 | "His teeth were too white," | | 14 | "She didn’t look at him." | | 15 | "Her focus was on the" | | 16 | "She’d faced down armed robbers," | | 17 | "She didn’t take her eyes" | | 18 | "She’d lost Morris to the" | | 19 | "She wasn’t about to let" |
| | ratio | 0.276 | |
| 38.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 76 | | matches | | 0 | "The rain hammered down like" | | 1 | "Harlow Quinn’s boots splashed through" | | 2 | "The suspect—lean, wiry, moving like" | | 3 | "She didn’t need to see" | | 4 | "She knew that gait, the" | | 5 | "*Tomás Herrera.* The paramedic who’d" | | 6 | "The one who’d been seen" | | 7 | "Quinn’s lungs burned, but she" | | 8 | "The streetlights bled yellow smears" | | 9 | "She skidded around the corner," | | 10 | "The alley reeked of stale" | | 11 | "Quinn exhaled through her nose," | | 12 | "A dark, wet hole yawned" | | 13 | "The rain hissed into the" | | 14 | "She crouched, fingers brushing the" | | 15 | "Herrera was slipping through her" | | 16 | "Quinn holstered her torch, gripped" | | 17 | "The drop was shorter than" | | 18 | "The air down here was" | | 19 | "Her boots splashed into an" |
| | ratio | 0.842 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 84.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 3 | | matches | | 0 | "*Tomás Herrera.* The paramedic who’d lost his license after patching up things that bled the wrong colour." | | 1 | "The alley reeked of stale urine and fried food, the kind of stink that clung to the back of your throat." | | 2 | "Stalls lined the walls, their wares lit by flickering lanterns that cast long, wavering shadows." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |