| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.21% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1790 | | 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) | |
| 83.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1790 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "vibrated" | | 1 | "sense of" | | 2 | "velvet" | | 3 | "resolved" | | 4 | "flickered" | | 5 | "footsteps" |
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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 | 170 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 170 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 197 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1790 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 44.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1614 | | uniqueNames | 10 | | maxNameDensity | 2.11 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Herrera | 24 | | Quinn | 34 | | Saint | 1 | | Christopher | 1 | | Camden | 1 | | Tube | 1 | | Sergeant | 1 | | Iqbal | 3 | | Morris | 1 | | Soho | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Sergeant" | | 5 | "Iqbal" | | 6 | "Morris" |
| | places | | | globalScore | 0.447 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 122 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1790 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 197 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 82 | | mean | 21.83 | | std | 18.93 | | cv | 0.867 | | sampleLengths | | 0 | 42 | | 1 | 3 | | 2 | 35 | | 3 | 2 | | 4 | 45 | | 5 | 2 | | 6 | 45 | | 7 | 36 | | 8 | 83 | | 9 | 12 | | 10 | 43 | | 11 | 5 | | 12 | 51 | | 13 | 43 | | 14 | 9 | | 15 | 24 | | 16 | 43 | | 17 | 6 | | 18 | 52 | | 19 | 6 | | 20 | 32 | | 21 | 6 | | 22 | 6 | | 23 | 10 | | 24 | 9 | | 25 | 3 | | 26 | 27 | | 27 | 7 | | 28 | 1 | | 29 | 19 | | 30 | 13 | | 31 | 8 | | 32 | 57 | | 33 | 32 | | 34 | 3 | | 35 | 45 | | 36 | 14 | | 37 | 6 | | 38 | 1 | | 39 | 11 | | 40 | 13 | | 41 | 39 | | 42 | 13 | | 43 | 24 | | 44 | 4 | | 45 | 3 | | 46 | 20 | | 47 | 7 | | 48 | 54 | | 49 | 39 |
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| 99.07% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 170 | | matches | | 0 | "been closed" | | 1 | "were hidden" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 288 | | matches | | 0 | "was hurrying" | | 1 | "was running" | | 2 | "was still coming" | | 3 | "was getting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 197 | | ratio | 0.01 | | matches | | 0 | "The stairs beyond the boards had been closed for decades; she had checked the maps after losing him here the first time." | | 1 | "Somebody had strung electrical cable along the ceiling; bare bulbs hung over traders’ tables." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1056 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.029356060606060608 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003787878787878788 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 197 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 197 | | mean | 9.09 | | std | 5.53 | | cv | 0.609 | | sampleLengths | | 0 | 22 | | 1 | 20 | | 2 | 3 | | 3 | 5 | | 4 | 19 | | 5 | 11 | | 6 | 2 | | 7 | 3 | | 8 | 18 | | 9 | 20 | | 10 | 4 | | 11 | 2 | | 12 | 9 | | 13 | 23 | | 14 | 6 | | 15 | 7 | | 16 | 7 | | 17 | 2 | | 18 | 12 | | 19 | 4 | | 20 | 11 | | 21 | 12 | | 22 | 16 | | 23 | 7 | | 24 | 34 | | 25 | 14 | | 26 | 5 | | 27 | 7 | | 28 | 9 | | 29 | 8 | | 30 | 13 | | 31 | 13 | | 32 | 5 | | 33 | 24 | | 34 | 13 | | 35 | 8 | | 36 | 6 | | 37 | 7 | | 38 | 24 | | 39 | 12 | | 40 | 9 | | 41 | 7 | | 42 | 14 | | 43 | 3 | | 44 | 5 | | 45 | 10 | | 46 | 11 | | 47 | 11 | | 48 | 6 | | 49 | 6 |
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| 54.99% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.34517766497461927 | | totalSentences | 197 | | uniqueOpeners | 68 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 161 | | matches | | 0 | "Then he ran." | | 1 | "Instead she crouched and reached" | | 2 | "Perhaps three now." | | 3 | "Instead she looked down the" | | 4 | "Then the shutter buckled outward." |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 161 | | matches | | 0 | "He saw Quinn across the" | | 1 | "He glanced back." | | 2 | "He had warm brown eyes" | | 3 | "She had watched him for" | | 4 | "It spun away, and she" | | 5 | "She had called in her" | | 6 | "She let it ring." | | 7 | "She had watched him go." | | 8 | "Her watch showed eleven forty-eight." | | 9 | "He had taken the same" | | 10 | "She knew where this street" | | 11 | "He was running into a" | | 12 | "He was younger than her" | | 13 | "He saw that she was" | | 14 | "He pulled out something small" | | 15 | "It slipped from his fingers," | | 16 | "Her fingertips found a disc" | | 17 | "She pocketed it and ran" | | 18 | "She caught a scrape from" | | 19 | "She slipped through and found" |
| | ratio | 0.298 | |
| 53.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 131 | | totalSentences | 161 | | matches | | 0 | "Tomás Herrera came out of" | | 1 | "He saw Quinn across the" | | 2 | "Quinn stepped off the kerb." | | 3 | "A bus shouldered past, throwing" | | 4 | "He glanced back." | | 5 | "He had warm brown eyes" | | 6 | "She had watched him for" | | 7 | "Tonight he was hurrying." | | 8 | "The rain made the pavement" | | 9 | "Herrera cut between two men" | | 10 | "Quinn clipped one with her" | | 11 | "It spun away, and she" | | 12 | "She had called in her" | | 13 | "She let it ring." | | 14 | "The chemist’s owner had reported" | | 15 | "Herrera had lost his paramedic" | | 16 | "That had been enough to" | | 17 | "The photograph on his phone," | | 18 | "The man had got up" | | 19 | "She had watched him go." |
| | ratio | 0.814 | |
| 62.11% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 161 | | matches | | 0 | "By the time someone else" | | 1 | "By the time she reached" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 2 | | matches | | 0 | "He had warm brown eyes and a Saint Christopher medallion that flashed at his throat as he turned." | | 1 | "Grimy white tiles reflected a light that had no business being there: amber, blue, a thin violent green." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.176 | | leniency | 0.353 | | rawRatio | 0 | | effectiveRatio | 0 | |