| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 738 | | 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) | |
| 72.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 738 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "flicker" | | 1 | "unsettled" | | 2 | "mosaic" |
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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 | 43 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 43 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 44 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 738 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 735 | | uniqueNames | 17 | | maxNameDensity | 0.82 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Quinn | 6 | | Tomás | 4 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Bateman | 1 | | Street | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Euston | 1 | | Camden | 1 | | Tube | 1 | | Morris | 1 | | Walthamstow | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" |
| | places | | 0 | "Bateman" | | 1 | "Street" | | 2 | "Tottenham" | | 3 | "Court" | | 4 | "Road" | | 5 | "Euston" | | 6 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 35 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 64.50% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.355 | | wordCount | 738 | | matches | | 0 | "not panic, but a grim calculation, as though he were measuring the distance" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 44 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 46.13 | | std | 27.93 | | cv | 0.606 | | sampleLengths | | 0 | 59 | | 1 | 57 | | 2 | 3 | | 3 | 71 | | 4 | 86 | | 5 | 29 | | 6 | 64 | | 7 | 6 | | 8 | 64 | | 9 | 28 | | 10 | 41 | | 11 | 98 | | 12 | 18 | | 13 | 30 | | 14 | 69 | | 15 | 15 |
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| 88.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 43 | | matches | | 0 | "been painted" | | 1 | "been written" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 110 | | matches | | 0 | "was already running" | | 1 | "were measuring" | | 2 | "was going" | | 3 | "was going" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 44 | | ratio | 0 | | matches | (empty) | |
| 94.69% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 738 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.04607046070460705 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009485094850948509 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 44 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 44 | | mean | 16.77 | | std | 11.9 | | cv | 0.71 | | sampleLengths | | 0 | 19 | | 1 | 40 | | 2 | 41 | | 3 | 5 | | 4 | 11 | | 5 | 3 | | 6 | 7 | | 7 | 19 | | 8 | 6 | | 9 | 39 | | 10 | 5 | | 11 | 29 | | 12 | 13 | | 13 | 39 | | 14 | 12 | | 15 | 17 | | 16 | 3 | | 17 | 26 | | 18 | 35 | | 19 | 6 | | 20 | 29 | | 21 | 8 | | 22 | 10 | | 23 | 17 | | 24 | 5 | | 25 | 23 | | 26 | 10 | | 27 | 4 | | 28 | 27 | | 29 | 12 | | 30 | 22 | | 31 | 9 | | 32 | 1 | | 33 | 2 | | 34 | 28 | | 35 | 6 | | 36 | 18 | | 37 | 5 | | 38 | 13 | | 39 | 9 | | 40 | 21 | | 41 | 30 | | 42 | 39 | | 43 | 15 |
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| 81.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5 | | totalSentences | 44 | | uniqueOpeners | 22 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 41 | | matches | | 0 | "Then, faintly, the sound of" |
| | ratio | 0.024 | |
| 63.90% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 41 | | matches | | 0 | "She knew him from the" | | 1 | "He did not look back." | | 2 | "He turned into Bateman Street" | | 3 | "Her breath came hard and" | | 4 | "He vaulted a low railing," | | 5 | "Her watch, the worn leather" | | 6 | "Her radio had gone silent" | | 7 | "She pushed harder." | | 8 | "He turned off the main" | | 9 | "She stopped at the edge" | | 10 | "Her partner had walked into" | | 11 | "It sat in an evidence" | | 12 | "Her phone showed no signal." | | 13 | "She tried the emergency line" | | 14 | "He had come down here" | | 15 | "She looked at the stairwell," |
| | ratio | 0.39 | |
| 8.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 41 | | matches | | 0 | "The green neon above the" | | 1 | "Harlow Quinn had her warrant" | | 2 | "She knew him from the" | | 3 | "He did not look back." | | 4 | "People who looked back were" | | 5 | "He turned into Bateman Street" | | 6 | "Quinn cursed under her breath" | | 7 | "Her breath came hard and" | | 8 | "Soho fell away behind them." | | 9 | "Tomás ran like a man" | | 10 | "He vaulted a low railing," | | 11 | "Her watch, the worn leather" | | 12 | "Her radio had gone silent" | | 13 | "She pushed harder." | | 14 | "The chase bent north, through" | | 15 | "Tomás glanced over his shoulder" | | 16 | "That was what unsettled her" | | 17 | "He turned off the main" | | 18 | "Quinn slowed, her hand going" | | 19 | "The fence sagged where someone" |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 41 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 1 | | matches | | 0 | "Tomás glanced over his shoulder once, and she saw his face clearly in the sodium light: not panic, but a grim calculation, as though he were measuring the dista…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |