| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1870 | | 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) | |
| 83.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1870 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "shattered" | | 1 | "warmth" | | 2 | "unsettled" | | 3 | "velvet" | | 4 | "quickened" | | 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 | 0 | | narrationSentences | 185 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 185 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 215 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1870 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 75 | | wordCount | 1672 | | uniqueNames | 16 | | maxNameDensity | 1.79 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 30 | | Tomás | 1 | | Herrera | 28 | | Raven | 2 | | Nest | 2 | | Soho | 1 | | Underground | 1 | | Morris | 1 | | Whitechapel | 1 | | Tube | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Soho" |
| | globalScore | 0.603 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 131 | | 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 | 1870 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 215 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 96 | | mean | 19.48 | | std | 16.87 | | cv | 0.866 | | sampleLengths | | 0 | 45 | | 1 | 39 | | 2 | 13 | | 3 | 2 | | 4 | 8 | | 5 | 70 | | 6 | 37 | | 7 | 6 | | 8 | 37 | | 9 | 6 | | 10 | 65 | | 11 | 9 | | 12 | 13 | | 13 | 5 | | 14 | 20 | | 15 | 70 | | 16 | 12 | | 17 | 5 | | 18 | 3 | | 19 | 5 | | 20 | 4 | | 21 | 33 | | 22 | 38 | | 23 | 33 | | 24 | 42 | | 25 | 8 | | 26 | 22 | | 27 | 3 | | 28 | 22 | | 29 | 3 | | 30 | 1 | | 31 | 30 | | 32 | 60 | | 33 | 31 | | 34 | 44 | | 35 | 7 | | 36 | 10 | | 37 | 4 | | 38 | 4 | | 39 | 18 | | 40 | 3 | | 41 | 40 | | 42 | 10 | | 43 | 59 | | 44 | 17 | | 45 | 12 | | 46 | 43 | | 47 | 9 | | 48 | 32 | | 49 | 35 |
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| 99.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 185 | | matches | | 0 | "been open" | | 1 | "been painted" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 305 | | matches | | 0 | "was taking" | | 1 | "was going" | | 2 | "was lying" | | 3 | "was certainly keeping" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 215 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1674 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.01971326164874552 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0023894862604540022 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 215 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 215 | | mean | 8.7 | | std | 5.28 | | cv | 0.608 | | sampleLengths | | 0 | 11 | | 1 | 19 | | 2 | 15 | | 3 | 13 | | 4 | 18 | | 5 | 5 | | 6 | 3 | | 7 | 4 | | 8 | 9 | | 9 | 2 | | 10 | 8 | | 11 | 18 | | 12 | 13 | | 13 | 18 | | 14 | 6 | | 15 | 15 | | 16 | 12 | | 17 | 6 | | 18 | 19 | | 19 | 6 | | 20 | 8 | | 21 | 13 | | 22 | 9 | | 23 | 7 | | 24 | 6 | | 25 | 9 | | 26 | 11 | | 27 | 22 | | 28 | 16 | | 29 | 7 | | 30 | 6 | | 31 | 3 | | 32 | 13 | | 33 | 5 | | 34 | 4 | | 35 | 16 | | 36 | 6 | | 37 | 15 | | 38 | 27 | | 39 | 8 | | 40 | 2 | | 41 | 3 | | 42 | 9 | | 43 | 12 | | 44 | 5 | | 45 | 3 | | 46 | 5 | | 47 | 4 | | 48 | 6 | | 49 | 14 |
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| 49.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.32558139534883723 | | totalSentences | 215 | | uniqueOpeners | 70 | |
| 39.45% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 169 | | matches | | 0 | "Then he turned, keeping one" | | 1 | "Then the curtain began to" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 169 | | matches | | 0 | "He ran well for a" | | 1 | "He cut between two taxis" | | 2 | "He lost half a step," | | 3 | "He turned into a side" | | 4 | "She had seen him hand" | | 5 | "She had made sure of" | | 6 | "His eyes, though, had been" | | 7 | "He skidded around a pile" | | 8 | "She drove harder, certain she" | | 9 | "Her radio crackled at her" | | 10 | "She had spent six weeks" | | 11 | "She went after him." | | 12 | "She hit the transmit button." | | 13 | "She could not have missed" | | 14 | "He slipped it through the" | | 15 | "Her face was ordinary, broad" | | 16 | "He did not look triumphant." | | 17 | "He looked frightened." | | 18 | "She reached into her coat" | | 19 | "She had found it pressed" |
| | ratio | 0.284 | |
| 54.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 137 | | totalSentences | 169 | | matches | | 0 | "Rain blurred the Camden High" | | 1 | "Detective Harlow Quinn kept Tomás" | | 2 | "He ran well for a" | | 3 | "He cut between two taxis" | | 4 | "Quinn followed, caught the top" | | 5 | "A horn sounded behind her." | | 6 | "Herrera looked back." | | 7 | "That was a mistake." | | 8 | "He lost half a step," | | 9 | "He turned into a side" | | 10 | "An hour ago she had" | | 11 | "She had seen him hand" | | 12 | "The man had opened it," | | 13 | "Herrera had tried to revive" | | 14 | "The man was alive when" | | 15 | "She had made sure of" | | 16 | "His eyes, though, had been" | | 17 | "He skidded around a pile" | | 18 | "Quinn followed into an alley" | | 19 | "She drove harder, certain she" |
| | ratio | 0.811 | |
| 88.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 169 | | matches | | 0 | "Now Herrera was taking it" | | 1 | "By the time she got" | | 2 | "By the time she reached" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 69 | | technicalSentenceCount | 1 | | matches | | 0 | "She had spent six weeks building a case around the Raven’s Nest: unexplained injuries, stolen pharmaceuticals, people who entered the bar and later denied havin…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 2 | | fancyTags | | 0 | "he whispered (whisper)" | | 1 | "he whispered (whisper)" |
| | dialogueSentences | 45 | | tagDensity | 0.267 | | leniency | 0.533 | | rawRatio | 0.167 | | effectiveRatio | 0.089 | |