| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 57 | | tagDensity | 0.281 | | leniency | 0.561 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1136 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 55.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1136 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "familiar" | | 1 | "measured" | | 2 | "effortless" | | 3 | "flickered" | | 4 | "silence" | | 5 | "crystal" | | 6 | "weight" | | 7 | "potential" | | 8 | "intensity" |
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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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 123 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1135 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 804 | | uniqueNames | 23 | | maxNameDensity | 0.87 | | worstName | "Eva" | | maxWindowNameDensity | 2 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Knightsbridge | 1 | | Like | 1 | | Cardiff | 1 | | Difficult | 1 | | Silas | 6 | | Tom | 1 | | Waits | 1 | | Eva | 7 | | Brendan | 1 | | Carter | 1 | | London | 1 | | Evan | 1 | | Dead | 1 | | Soho | 1 | | Penarth | 1 | | Made | 1 | | March | 1 | | Carrington | 1 | | Law | 1 | | Chambers | 1 | | Temple | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Difficult" | | 3 | "Silas" | | 4 | "Tom" | | 5 | "Waits" | | 6 | "Eva" | | 7 | "Brendan" | | 8 | "Carter" | | 9 | "Evan" | | 10 | "Dead" | | 11 | "Carrington" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Soho" | | 3 | "Penarth" | | 4 | "Law" | | 5 | "Temple" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite meeting my eye" |
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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 | 1135 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 123 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 16.94 | | std | 15.06 | | cv | 0.889 | | sampleLengths | | 0 | 40 | | 1 | 66 | | 2 | 11 | | 3 | 13 | | 4 | 1 | | 5 | 12 | | 6 | 24 | | 7 | 40 | | 8 | 4 | | 9 | 6 | | 10 | 28 | | 11 | 13 | | 12 | 16 | | 13 | 3 | | 14 | 4 | | 15 | 26 | | 16 | 18 | | 17 | 7 | | 18 | 6 | | 19 | 11 | | 20 | 2 | | 21 | 22 | | 22 | 45 | | 23 | 2 | | 24 | 28 | | 25 | 15 | | 26 | 5 | | 27 | 7 | | 28 | 7 | | 29 | 26 | | 30 | 25 | | 31 | 4 | | 32 | 7 | | 33 | 7 | | 34 | 2 | | 35 | 10 | | 36 | 18 | | 37 | 4 | | 38 | 41 | | 39 | 44 | | 40 | 17 | | 41 | 5 | | 42 | 31 | | 43 | 5 | | 44 | 5 | | 45 | 7 | | 46 | 10 | | 47 | 4 | | 48 | 7 | | 49 | 26 |
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| 92.58% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 83 | | matches | | 0 | "were gone" | | 1 | "being summoned" | | 2 | "was gone" |
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| 50.75% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 134 | | matches | | 0 | "was polishing" | | 1 | "was reading" | | 2 | "was still moving" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 123 | | ratio | 0.008 | | matches | | 0 | "Silas looked up, and something flickered across his face—the briefest tightening around the eyes." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 809 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.030902348578491966 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006180469715698393 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 123 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 123 | | mean | 9.23 | | std | 8.05 | | cv | 0.872 | | sampleLengths | | 0 | 24 | | 1 | 4 | | 2 | 7 | | 3 | 5 | | 4 | 19 | | 5 | 4 | | 6 | 18 | | 7 | 25 | | 8 | 11 | | 9 | 4 | | 10 | 3 | | 11 | 6 | | 12 | 1 | | 13 | 2 | | 14 | 10 | | 15 | 13 | | 16 | 7 | | 17 | 4 | | 18 | 40 | | 19 | 4 | | 20 | 6 | | 21 | 4 | | 22 | 24 | | 23 | 9 | | 24 | 4 | | 25 | 6 | | 26 | 10 | | 27 | 3 | | 28 | 4 | | 29 | 7 | | 30 | 7 | | 31 | 3 | | 32 | 9 | | 33 | 10 | | 34 | 8 | | 35 | 7 | | 36 | 6 | | 37 | 3 | | 38 | 8 | | 39 | 2 | | 40 | 13 | | 41 | 9 | | 42 | 1 | | 43 | 44 | | 44 | 2 | | 45 | 24 | | 46 | 4 | | 47 | 15 | | 48 | 5 | | 49 | 7 |
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| 78.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4959349593495935 | | totalSentences | 123 | | uniqueOpeners | 61 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 73 | | matches | | 0 | "Then that laugh again, the" | | 1 | "Just the blood rushing in" | | 2 | "Then she was gone, the" |
| | ratio | 0.041 | |
| 66.58% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 73 | | matches | | 0 | "She'd cut her hair." | | 1 | "I could have left." | | 2 | "My name left her lips" | | 3 | "Her eyes did the inventory" | | 4 | "She gestured to the stool" | | 5 | "I slid onto the cracked" | | 6 | "Her father's drink." | | 7 | "she said, not quite meeting" | | 8 | "I took a longer pull" | | 9 | "She swirled her whisky, watched" | | 10 | "She glanced toward the bar," | | 11 | "She said it to her" | | 12 | "Her jaw tightened" | | 13 | "She took a sip of" | | 14 | "She set her glass down" | | 15 | "She reached into her blazer" | | 16 | "She slid it across the" | | 17 | "It was us." | | 18 | "I'm laughing at something off-camera," | | 19 | "She said it flatly, like" |
| | ratio | 0.384 | |
| 76.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 73 | | matches | | 0 | "The brass door handle of" | | 1 | "A measured thing, practised like" | | 2 | "Eva stood at the far" | | 3 | "She'd cut her hair." | | 4 | "The long copper waves I" | | 5 | "A grey blazer hung from" | | 6 | "Silas caught my eye from" | | 7 | "I could have left." | | 8 | "The smile came first, automatic," | | 9 | "My name left her lips" | | 10 | "Her eyes did the inventory" | | 11 | "A beat of silence." | | 12 | "She gestured to the stool" | | 13 | "I slid onto the cracked" | | 14 | "Silas materialised without being summoned," | | 15 | "Eva's glass held something amber" | | 16 | "Her father's drink." | | 17 | "That thought pressed against something" | | 18 | "she said, not quite meeting" | | 19 | "The gin arrived." |
| | ratio | 0.767 | |
| 68.49% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 73 | | matches | | 0 | "Now, the night was still" |
| | ratio | 0.014 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 2 | | matches | | 0 | "A grey blazer hung from her shoulders with the kind of effortless expense that screamed Knightsbridge, not our old shared wardrobe of charity shop finds." | | 1 | "I pocketed the card and followed him toward the bookshelf that wasn't a bookshelf, my father's death settling somewhere behind my ribs like a stone dropped into…" |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, not quite meeting my eye" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 57 | | tagDensity | 0.123 | | leniency | 0.246 | | rawRatio | 0 | | effectiveRatio | 0 | |