| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.233 | | leniency | 0.467 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1733 | | 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) | |
| 85.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1733 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "footsteps" | | 1 | "silence" | | 2 | "velvet" | | 3 | "flicked" | | 4 | "measured" |
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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 | 171 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 171 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 194 | | 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 | 1733 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 1574 | | uniqueNames | 13 | | maxNameDensity | 2.16 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Harlow | 1 | | Quinn | 34 | | Herrera | 18 | | Soho | 1 | | Morris | 3 | | Tube | 1 | | Saint | 1 | | Christopher | 1 | | Underground | 1 | | Control | 1 | | One | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Control" | | 7 | "One" |
| | places | | | globalScore | 0.42 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 124 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a man who had seen what stood" |
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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 | 1733 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 194 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 83 | | mean | 20.88 | | std | 17 | | cv | 0.814 | | sampleLengths | | 0 | 32 | | 1 | 24 | | 2 | 3 | | 3 | 28 | | 4 | 7 | | 5 | 44 | | 6 | 18 | | 7 | 2 | | 8 | 39 | | 9 | 47 | | 10 | 8 | | 11 | 5 | | 12 | 21 | | 13 | 44 | | 14 | 4 | | 15 | 14 | | 16 | 38 | | 17 | 2 | | 18 | 6 | | 19 | 54 | | 20 | 24 | | 21 | 22 | | 22 | 13 | | 23 | 22 | | 24 | 12 | | 25 | 7 | | 26 | 42 | | 27 | 12 | | 28 | 13 | | 29 | 39 | | 30 | 1 | | 31 | 3 | | 32 | 36 | | 33 | 2 | | 34 | 49 | | 35 | 13 | | 36 | 14 | | 37 | 2 | | 38 | 38 | | 39 | 2 | | 40 | 15 | | 41 | 12 | | 42 | 26 | | 43 | 10 | | 44 | 5 | | 45 | 11 | | 46 | 3 | | 47 | 19 | | 48 | 28 | | 49 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 171 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 259 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 194 | | ratio | 0.005 | | matches | | 0 | "Herrera clipped its mirror; the rider shouted and lunged for it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1580 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.01139240506329114 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0018987341772151898 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 194 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 194 | | mean | 8.93 | | std | 5.23 | | cv | 0.586 | | sampleLengths | | 0 | 14 | | 1 | 18 | | 2 | 24 | | 3 | 3 | | 4 | 3 | | 5 | 7 | | 6 | 18 | | 7 | 7 | | 8 | 20 | | 9 | 3 | | 10 | 15 | | 11 | 6 | | 12 | 7 | | 13 | 11 | | 14 | 2 | | 15 | 11 | | 16 | 15 | | 17 | 13 | | 18 | 22 | | 19 | 9 | | 20 | 16 | | 21 | 8 | | 22 | 5 | | 23 | 10 | | 24 | 11 | | 25 | 25 | | 26 | 8 | | 27 | 11 | | 28 | 4 | | 29 | 8 | | 30 | 6 | | 31 | 5 | | 32 | 15 | | 33 | 12 | | 34 | 6 | | 35 | 2 | | 36 | 6 | | 37 | 7 | | 38 | 23 | | 39 | 15 | | 40 | 9 | | 41 | 12 | | 42 | 12 | | 43 | 9 | | 44 | 1 | | 45 | 1 | | 46 | 6 | | 47 | 5 | | 48 | 3 | | 49 | 10 |
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| 54.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3402061855670103 | | totalSentences | 194 | | uniqueOpeners | 66 | |
| 41.41% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 161 | | matches | | 0 | "Then a bus cut across" | | 1 | "Only the rattle of distant" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 161 | | matches | | 0 | "Its green neon sign broke" | | 1 | "He glanced back." | | 2 | "She stepped off the kerb" | | 3 | "Her shoes struck wet stone" | | 4 | "He had recognised the photograph" | | 5 | "he had asked" | | 6 | "He slipped through a crowd" | | 7 | "Its metal spoke scraped her" | | 8 | "His left sleeve rode up" | | 9 | "He did not look like" | | 10 | "He looked like a man" | | 11 | "She turned back." | | 12 | "She descended the first flight" | | 13 | "Its plastic cord swung against" | | 14 | "Her watch struck the rail" | | 15 | "She reached a landing and" | | 16 | "She caught the handle, pulled" | | 17 | "Her radio gave a single" | | 18 | "She pocketed the radio and" | | 19 | "His Saint Christopher medallion lay" |
| | ratio | 0.236 | |
| 62.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 128 | | totalSentences | 161 | | matches | | 0 | "Its green neon sign broke" | | 1 | "Detective Harlow Quinn came after" | | 2 | "He glanced back." | | 3 | "Blood marked the cuff of" | | 4 | "She stepped off the kerb" | | 5 | "A horn blared." | | 6 | "Herrera had reached the far" | | 7 | "Quinn followed the gap he" | | 8 | "Her shoes struck wet stone" | | 9 | "A woman hauling a suitcase" | | 10 | "Quinn caught a last glimpse" | | 11 | "He had recognised the photograph" | | 12 | "Quinn had seen it in" | | 13 | "he had asked" | | 14 | "Herrera had looked past her" | | 15 | "A delivery rider leaned beside" | | 16 | "Herrera clipped its mirror; the" | | 17 | "The rider pointed, still wrestling" | | 18 | "Herrera reached a busier road." | | 19 | "He slipped through a crowd" |
| | ratio | 0.795 | |
| 62.11% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 161 | | matches | | 0 | "By the time Quinn turned," | | 1 | "Now she took the corner" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 1 | | matches | | 0 | "Now she took the corner into the narrow street and spotted him at its far end, boots splashing through water that streamed along the gutter." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 30 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.2 | | effectiveRatio | 0.067 | |