| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1286 | | 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) | |
| 49.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1286 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "silence" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "vibrated" | | 6 | "electric" | | 7 | "scanning" | | 8 | "glint" | | 9 | "intricate" | | 10 | "fleeting" |
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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 | 0 | | narrationSentences | 114 | | matches | (empty) | |
| 92.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | 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 | 1279 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1269 | | uniqueNames | 13 | | maxNameDensity | 0.63 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Shaftesbury | 1 | | Avenue | 1 | | Harlow | 1 | | Quinn | 8 | | Camden | 2 | | Market | 2 | | Tube | 2 | | Morris | 2 | | Veil | 1 | | Tomás | 1 | | Herrera | 3 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" |
| | places | | 0 | "Shaftesbury" | | 1 | "Avenue" | | 2 | "Camden" | | 3 | "Market" | | 4 | "Tube" |
| | globalScore | 1 | | windowScore | 1 | |
| 59.64% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like a shuttered, forgotten entran" | | 1 | "Daggers that seemed to drink the lantern glow" | | 2 | "looked like a mouse in a cage of snakes" |
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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 | 1279 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 115 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 79.94 | | std | 42.64 | | cv | 0.533 | | sampleLengths | | 0 | 125 | | 1 | 120 | | 2 | 106 | | 3 | 68 | | 4 | 82 | | 5 | 88 | | 6 | 127 | | 7 | 47 | | 8 | 154 | | 9 | 74 | | 10 | 117 | | 11 | 48 | | 12 | 81 | | 13 | 4 | | 14 | 10 | | 15 | 28 |
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| 86.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 114 | | matches | | 0 | "was lost" | | 1 | "was replaced" | | 2 | "was gone" | | 3 | "was carved" | | 4 | "was packed" | | 5 | "was gone" |
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| 70.55% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 206 | | matches | | 0 | "was leading" | | 1 | "was tiring" | | 2 | "was investigating" | | 3 | "was fleeting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 115 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1276 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, canvas-wrapped package." |
| | adverbCount | 32 | | adverbRatio | 0.025078369905956112 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006269592476489028 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 11.12 | | std | 7.21 | | cv | 0.648 | | sampleLengths | | 0 | 4 | | 1 | 20 | | 2 | 19 | | 3 | 24 | | 4 | 6 | | 5 | 11 | | 6 | 14 | | 7 | 27 | | 8 | 5 | | 9 | 24 | | 10 | 1 | | 11 | 12 | | 12 | 9 | | 13 | 18 | | 14 | 9 | | 15 | 8 | | 16 | 18 | | 17 | 12 | | 18 | 4 | | 19 | 14 | | 20 | 9 | | 21 | 25 | | 22 | 10 | | 23 | 5 | | 24 | 26 | | 25 | 17 | | 26 | 17 | | 27 | 23 | | 28 | 7 | | 29 | 17 | | 30 | 4 | | 31 | 3 | | 32 | 19 | | 33 | 17 | | 34 | 16 | | 35 | 24 | | 36 | 3 | | 37 | 6 | | 38 | 9 | | 39 | 4 | | 40 | 14 | | 41 | 16 | | 42 | 1 | | 43 | 1 | | 44 | 1 | | 45 | 16 | | 46 | 4 | | 47 | 2 | | 48 | 4 | | 49 | 10 |
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| 45.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3217391304347826 | | totalSentences | 115 | | uniqueOpeners | 37 | |
| 31.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 107 | | matches | | 0 | "Just a small, circular indentation" |
| | ratio | 0.009 | |
| 44.30% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 107 | | matches | | 0 | "His desperate glance back, face" | | 1 | "He dodged a black cab," | | 2 | "Her lungs burned, a familiar" | | 3 | "He was fast, but panicked." | | 4 | "He took a sharp right" | | 5 | "She followed, her hand instinctively" | | 6 | "It was a suffocating, tight" | | 7 | "He scrambled over a chain-link" | | 8 | "He was leading her away" | | 9 | "She vaulted the fence with" | | 10 | "Her boots hit the concrete" | | 11 | "He led her into the" | | 12 | "He was tiring, his movements" | | 13 | "He risked another look back," | | 14 | "He went down hard." | | 15 | "She saw him push himself" | | 16 | "It looked like a shuttered," | | 17 | "He fumbled with something in" | | 18 | "He was gone." | | 19 | "She ran her fingers over" |
| | ratio | 0.439 | |
| 2.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 107 | | matches | | 0 | "The gap was closing." | | 1 | "His desperate glance back, face" | | 2 | "Rain slicked the pavement of" | | 3 | "He dodged a black cab," | | 4 | "Detective Harlow Quinn didn’t break" | | 5 | "Her lungs burned, a familiar" | | 6 | "The worn leather of her" | | 7 | "He was fast, but panicked." | | 8 | "He took a sharp right" | | 9 | "She followed, her hand instinctively" | | 10 | "The alley stank of stale" | | 11 | "It was a suffocating, tight" | | 12 | "He scrambled over a chain-link" | | 13 | "The metal rattled and groaned" | | 14 | "He was leading her away" | | 15 | "She vaulted the fence with" | | 16 | "Her boots hit the concrete" | | 17 | "The suspect was a good" | | 18 | "Quinn pushed harder, her breath" | | 19 | "This wasn't just another runner." |
| | ratio | 0.916 | |
| 46.73% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 107 | | matches | | 0 | "By the time Quinn reached" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 1 | | matches | | 0 | "He dodged a black cab, the driver’s horn blaring a furious, drawn-out note that was lost in the hiss of tyres on wet asphalt." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |