| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 1 | | adverbTags | | 0 | "His Spanish accent carried clearly [clearly]" |
| | dialogueSentences | 12 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.5 | | effectiveRatio | 0.167 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 971 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 53.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 971 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "treacherous" | | 1 | "familiar" | | 2 | "weight" | | 3 | "tracing" | | 4 | "echo" | | 5 | "pulse" | | 6 | "echoed" | | 7 | "depths" |
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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 | 103 | | matches | (empty) | |
| 59.64% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "seemed to" | | 2 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 971 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 87.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 886 | | uniqueNames | 18 | | maxNameDensity | 1.24 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 11 | | Neal | 1 | | Yard | 1 | | Spanish | 1 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 3 | | Shorts | 1 | | Gardens | 1 | | Covent | 1 | | Garden | 1 | | Tommy | 9 | | Tomás | 1 | | Long | 1 | | Acre | 1 | | Morris | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Neal" | | 2 | "Yard" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Herrera" | | 6 | "Tommy" | | 7 | "Tomás" | | 8 | "Morris" |
| | places | | 0 | "Raven" | | 1 | "Shorts" | | 2 | "Gardens" | | 3 | "Covent" | | 4 | "Garden" |
| | globalScore | 0.879 | | windowScore | 1 | |
| 36.36% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 3 | | matches | | 0 | "melodies that seemed to echo longer than they should in the narrow space between buildings" | | 1 | "quite people" | | 2 | "melodies that seemed to twist through her mind like smoke" |
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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 | 971 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 113 | | matches | | 0 | "screamed that whatever" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 21.11 | | std | 15.62 | | cv | 0.74 | | sampleLengths | | 0 | 13 | | 1 | 4 | | 2 | 41 | | 3 | 11 | | 4 | 14 | | 5 | 13 | | 6 | 48 | | 7 | 6 | | 8 | 3 | | 9 | 23 | | 10 | 8 | | 11 | 4 | | 12 | 41 | | 13 | 5 | | 14 | 11 | | 15 | 34 | | 16 | 12 | | 17 | 17 | | 18 | 37 | | 19 | 62 | | 20 | 38 | | 21 | 36 | | 22 | 10 | | 23 | 29 | | 24 | 27 | | 25 | 3 | | 26 | 34 | | 27 | 18 | | 28 | 8 | | 29 | 4 | | 30 | 37 | | 31 | 16 | | 32 | 13 | | 33 | 67 | | 34 | 12 | | 35 | 30 | | 36 | 6 | | 37 | 7 | | 38 | 14 | | 39 | 21 | | 40 | 27 | | 41 | 34 | | 42 | 33 | | 43 | 3 | | 44 | 12 | | 45 | 25 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 103 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 152 | | matches | | 0 | "was already moving" | | 1 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 113 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 889 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.02924634420697413 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006749156355455568 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 8.59 | | std | 5.52 | | cv | 0.642 | | sampleLengths | | 0 | 13 | | 1 | 4 | | 2 | 18 | | 3 | 14 | | 4 | 9 | | 5 | 11 | | 6 | 10 | | 7 | 2 | | 8 | 2 | | 9 | 10 | | 10 | 3 | | 11 | 8 | | 12 | 18 | | 13 | 13 | | 14 | 9 | | 15 | 6 | | 16 | 3 | | 17 | 23 | | 18 | 5 | | 19 | 3 | | 20 | 4 | | 21 | 5 | | 22 | 15 | | 23 | 5 | | 24 | 2 | | 25 | 14 | | 26 | 5 | | 27 | 11 | | 28 | 5 | | 29 | 8 | | 30 | 10 | | 31 | 11 | | 32 | 12 | | 33 | 5 | | 34 | 2 | | 35 | 10 | | 36 | 11 | | 37 | 7 | | 38 | 9 | | 39 | 10 | | 40 | 5 | | 41 | 15 | | 42 | 13 | | 43 | 6 | | 44 | 2 | | 45 | 6 | | 46 | 15 | | 47 | 9 | | 48 | 10 | | 49 | 8 |
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| 79.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.49557522123893805 | | totalSentences | 113 | | uniqueOpeners | 56 | |
| 37.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 90 | | matches | | 0 | "Then he stepped through the" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 90 | | matches | | 0 | "His Spanish accent carried clearly" | | 1 | "She pressed her knee harder" | | 2 | "His muscles tensed beneath her." | | 3 | "She rolled sideways, hand reaching" | | 4 | "He vaulted over a recycling" | | 5 | "She pushed herself up, ignoring" | | 6 | "Her worn leather watch showed" | | 7 | "She'd chased suspects through these" | | 8 | "She rounded the corner onto" | | 9 | "She wiped it away and" | | 10 | "His hand moved in a" | | 11 | "Her mobile buzzed." | | 12 | "She looked up." | | 13 | "She'd thought it was stress" | | 14 | "Her radio crackled back to" | | 15 | "She could call for backup." | | 16 | "She could smell incense now," | | 17 | "He was waiting for her." |
| | ratio | 0.2 | |
| 82.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 90 | | matches | | 0 | "The knife clattered across wet" | | 1 | "The man beneath her writhed," | | 2 | "Blood from his split lip" | | 3 | "The paramedic's warm brown eyes" | | 4 | "His Spanish accent carried clearly" | | 5 | "She pressed her knee harder" | | 6 | "The Saint Christopher medallion around" | | 7 | "His muscles tensed beneath her." | | 8 | "Quinn reached for her cuffs." | | 9 | "The metal was cold against" | | 10 | "This one felt different though." | | 11 | "Tommy Herrera didn't make mistakes," | | 12 | "The realisation hit her as" | | 13 | "Pain exploded through her chest." | | 14 | "She rolled sideways, hand reaching" | | 15 | "Tommy was already moving, fluid" | | 16 | "He vaulted over a recycling" | | 17 | "The radio crackled with static." | | 18 | "The old buildings around Covent" | | 19 | "She pushed herself up, ignoring" |
| | ratio | 0.756 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 4 | | matches | | 0 | "Quinn followed, her boots splashing through puddles that reflected the orange glow of street lamps." | | 1 | "Music drifted out, haunting melodies that seemed to echo longer than they should in the narrow space between buildings." | | 2 | "DS Morris had been investigating missing persons reports, following leads that took him to places that shouldn't exist." | | 3 | "Live instruments playing melodies that seemed to twist through her mind like smoke." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |