| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "Tomás said softly [softly]" |
| | dialogueSentences | 35 | | tagDensity | 0.371 | | leniency | 0.743 | | rawRatio | 0.077 | | effectiveRatio | 0.057 | |
| 85.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1726 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "really" | | 1 | "softly" | | 2 | "slowly" | | 3 | "gently" | | 4 | "sharply" |
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| 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) | |
| 56.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1726 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "standard" | | 1 | "gloom" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "rhythmic" | | 5 | "constructed" | | 6 | "silence" | | 7 | "lilt" | | 8 | "warmth" | | 9 | "pulsed" | | 10 | "pulse" | | 11 | "vibrated" |
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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 | 169 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 169 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "started to" | | 2 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 190 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1718 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1474 | | uniqueNames | 16 | | maxNameDensity | 1.56 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 23 | | Covent | 1 | | Garden | 1 | | Force | 1 | | London | 1 | | Camden | 2 | | Victorian | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Spanish | 1 | | St | 1 | | Christopher | 1 | | Herrera | 1 | | Met | 1 | | Tomás | 15 |
| | persons | | 0 | "Harlow" | | 1 | "Force" | | 2 | "Camden" | | 3 | "Market" | | 4 | "Herrera" | | 5 | "Met" | | 6 | "Tomás" |
| | places | | 0 | "Covent" | | 1 | "Garden" | | 2 | "London" | | 3 | "St" | | 4 | "Christopher" |
| | globalScore | 0.72 | | windowScore | 0.333 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | glossingSentenceCount | 4 | | matches | | 0 | "chest heaving visibly under the soaked fa" | | 1 | "looked like a standard Victorian drain co" | | 2 | "looked like they had been plucked from a" | | 3 | "sounded like wind rushing through a tunnel" |
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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 | 1718 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 190 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 22.91 | | std | 17.68 | | cv | 0.772 | | sampleLengths | | 0 | 50 | | 1 | 64 | | 2 | 3 | | 3 | 41 | | 4 | 45 | | 5 | 53 | | 6 | 52 | | 7 | 23 | | 8 | 40 | | 9 | 13 | | 10 | 60 | | 11 | 42 | | 12 | 41 | | 13 | 36 | | 14 | 22 | | 15 | 13 | | 16 | 10 | | 17 | 3 | | 18 | 42 | | 19 | 86 | | 20 | 26 | | 21 | 21 | | 22 | 37 | | 23 | 11 | | 24 | 5 | | 25 | 13 | | 26 | 46 | | 27 | 6 | | 28 | 28 | | 29 | 4 | | 30 | 19 | | 31 | 2 | | 32 | 21 | | 33 | 6 | | 34 | 52 | | 35 | 28 | | 36 | 15 | | 37 | 7 | | 38 | 27 | | 39 | 17 | | 40 | 8 | | 41 | 2 | | 42 | 22 | | 43 | 17 | | 44 | 43 | | 45 | 16 | | 46 | 21 | | 47 | 6 | | 48 | 22 | | 49 | 5 |
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| 96.96% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 169 | | matches | | 0 | "was speckled" | | 1 | "was believed" | | 2 | "been plucked" | | 3 | "was matted" |
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| 8.15% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 278 | | matches | | 0 | "was slowing" | | 1 | "was fighting" | | 2 | "weren't selling" | | 3 | "was running" | | 4 | "was trembling" | | 5 | "were watching" | | 6 | "wasn't looking" | | 7 | "was looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 190 | | ratio | 0.011 | | matches | | 0 | "The boy—he was just a boy, really—didn't even look back." | | 1 | "Dozens of eyes—some glowing, some pupil-less—locked onto her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 935 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.026737967914438502 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.012834224598930482 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 190 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 190 | | mean | 9.04 | | std | 6.05 | | cv | 0.669 | | sampleLengths | | 0 | 9 | | 1 | 20 | | 2 | 21 | | 3 | 12 | | 4 | 17 | | 5 | 15 | | 6 | 13 | | 7 | 7 | | 8 | 3 | | 9 | 16 | | 10 | 10 | | 11 | 15 | | 12 | 12 | | 13 | 18 | | 14 | 15 | | 15 | 5 | | 16 | 16 | | 17 | 5 | | 18 | 21 | | 19 | 6 | | 20 | 15 | | 21 | 12 | | 22 | 10 | | 23 | 15 | | 24 | 10 | | 25 | 7 | | 26 | 6 | | 27 | 7 | | 28 | 5 | | 29 | 7 | | 30 | 2 | | 31 | 6 | | 32 | 10 | | 33 | 1 | | 34 | 2 | | 35 | 4 | | 36 | 7 | | 37 | 2 | | 38 | 5 | | 39 | 3 | | 40 | 11 | | 41 | 23 | | 42 | 18 | | 43 | 4 | | 44 | 21 | | 45 | 17 | | 46 | 8 | | 47 | 17 | | 48 | 16 | | 49 | 4 |
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| 41.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3105263157894737 | | totalSentences | 190 | | uniqueOpeners | 59 | |
| 88.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 151 | | matches | | 0 | "Then he dropped into the" | | 1 | "Just the faint, sickly smell" | | 2 | "Maybe a squat for homeless" | | 3 | "Then, slowly, he undid the" |
| | ratio | 0.026 | |
| 58.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 151 | | matches | | 0 | "She ignored the cold, keeping" | | 1 | "Her shoulder slammed into the" | | 2 | "She landed in a puddle," | | 3 | "Her voice boomed, swallowed instantly" | | 4 | "He veered sharp left around" | | 5 | "She corrected her balance, fighting" | | 6 | "He was fighting for breath," | | 7 | "He reached a dead end." | | 8 | "It looked like a standard" | | 9 | "He gripped the iron bars" | | 10 | "He paused for a fraction" | | 11 | "His eyes were wide, white-rimmed" | | 12 | "She peered over the edge." | | 13 | "She checked her wrist." | | 14 | "Her leather watch was speckled" | | 15 | "He had moved with the" | | 16 | "She swung a leg over" | | 17 | "She started to run." | | 18 | "Her footsteps echoed, metallic and" | | 19 | "She reached the edge and" |
| | ratio | 0.404 | |
| 19.60% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 133 | | totalSentences | 151 | | matches | | 0 | "Watersurged over Harlow's boot-tops, soaking" | | 1 | "She ignored the cold, keeping" | | 2 | "The suspect vaulted a stack" | | 3 | "Harlow hit the obstacle with" | | 4 | "Her shoulder slammed into the" | | 5 | "She landed in a puddle," | | 6 | "Data you ignored until the" | | 7 | "Her voice boomed, swallowed instantly" | | 8 | "The boy—he was just a" | | 9 | "He veered sharp left around" | | 10 | "Harlow skidded around the bend," | | 11 | "She corrected her balance, fighting" | | 12 | "The alley here was narrower," | | 13 | "The suspect was slowing down." | | 14 | "He was fighting for breath," | | 15 | "He reached a dead end." | | 16 | "A high brick wall blocked" | | 17 | "The boy dropped to his" | | 18 | "It looked like a standard" | | 19 | "He gripped the iron bars" |
| | ratio | 0.881 | |
| 66.23% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 151 | | matches | | 0 | "To Harlow's surprise, the metal" | | 1 | "If she went down there" |
| | ratio | 0.013 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 6 | | matches | | 0 | "She corrected her balance, fighting the centrifugal force that tried to plant her face against a brick wall." | | 1 | "He was fighting for breath, his chest heaving visibly under the soaked fabric of his hoodie." | | 2 | "A high brick wall blocked the path, covered in graffiti that glowed dully under the flickering light of a failing streetlamp." | | 3 | "Another sold cages containing birds that looked like they had been plucked from a nightmare, their feathers replaced by quills that writhed independently." | | 4 | "Harlow twisted her head, keeping the gun trained on Tomás but looking back towards the way she came." | | 5 | "It lowered its head, locking its burning eyes onto her." |
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| 48.08% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 2 | | matches | | 0 | "she shouted, her voice cutting through the ambient noise" | | 1 | "Tomás said, his voice dropping an octave" |
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| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 5 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she repeated (repeat)" | | 2 | "she whispered (whisper)" | | 3 | "Tomás corrected (correct)" | | 4 | "Tomás muttered (mutter)" |
| | dialogueSentences | 35 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.5 | | effectiveRatio | 0.286 | |