| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1193 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "lightly" | | 2 | "cautiously" |
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| 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) | |
| 20.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1193 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "fractured" | | 1 | "flicker" | | 2 | "cacophony" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "chaotic" | | 6 | "familiar" | | 7 | "gleaming" | | 8 | "gloom" | | 9 | "perfect" | | 10 | "charged" | | 11 | "electric" | | 12 | "resonated" | | 13 | "pulsed" | | 14 | "echoing" | | 15 | "weight" |
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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 | 1 | | narrationSentences | 110 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1187 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 1183 | | uniqueNames | 7 | | maxNameDensity | 0.93 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 11 | | Herrera | 11 | | Morris | 3 | | Camden | 1 | | Tube | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a piece of carved bone, milky" | | 1 | "sound that seemed to belong to another world" |
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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 | 1187 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 55.45% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 59.35 | | std | 20.42 | | cv | 0.344 | | sampleLengths | | 0 | 55 | | 1 | 28 | | 2 | 63 | | 3 | 88 | | 4 | 61 | | 5 | 83 | | 6 | 77 | | 7 | 80 | | 8 | 65 | | 9 | 19 | | 10 | 66 | | 11 | 41 | | 12 | 13 | | 13 | 64 | | 14 | 79 | | 15 | 56 | | 16 | 73 | | 17 | 43 | | 18 | 62 | | 19 | 71 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 83.72% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 172 | | matches | | 0 | "was flagging" | | 1 | "was stumbling" | | 2 | "wasn't trying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 110 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1189 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint, sickly green light pulsed" |
| | adverbCount | 25 | | adverbRatio | 0.021026072329688814 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004205214465937763 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 10.79 | | std | 5.54 | | cv | 0.513 | | sampleLengths | | 0 | 6 | | 1 | 18 | | 2 | 11 | | 3 | 18 | | 4 | 2 | | 5 | 18 | | 6 | 4 | | 7 | 4 | | 8 | 2 | | 9 | 15 | | 10 | 4 | | 11 | 17 | | 12 | 14 | | 13 | 13 | | 14 | 16 | | 15 | 17 | | 16 | 11 | | 17 | 21 | | 18 | 10 | | 19 | 13 | | 20 | 22 | | 21 | 11 | | 22 | 2 | | 23 | 7 | | 24 | 19 | | 25 | 13 | | 26 | 10 | | 27 | 9 | | 28 | 18 | | 29 | 11 | | 30 | 3 | | 31 | 10 | | 32 | 4 | | 33 | 5 | | 34 | 12 | | 35 | 11 | | 36 | 16 | | 37 | 3 | | 38 | 1 | | 39 | 8 | | 40 | 5 | | 41 | 21 | | 42 | 21 | | 43 | 14 | | 44 | 12 | | 45 | 18 | | 46 | 15 | | 47 | 8 | | 48 | 16 | | 49 | 14 |
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| 40.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.2909090909090909 | | totalSentences | 110 | | uniqueOpeners | 32 | |
| 62.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 106 | | matches | | 0 | "Away from the cars and" | | 1 | "Then, a low hum resonated" |
| | ratio | 0.019 | |
| 46.42% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 106 | | matches | | 0 | "He glanced back, just a" | | 1 | "She vaulted a low barrier" | | 2 | "She ignored it, her focus" | | 3 | "He was fast, weaving through" | | 4 | "He dodged a stumbling couple," | | 5 | "She saw the scar on" | | 6 | "Her mind catalogued the detail," | | 7 | "She shoved past a man" | | 8 | "Her world had shrunk to" | | 9 | "It was Morris’s watch." | | 10 | "She never took it off." | | 11 | "He led her north, through" | | 12 | "He was flagging." | | 13 | "She could run all night." | | 14 | "Her body was a machine" | | 15 | "He ducked left, into the" | | 16 | "He clawed his way to" | | 17 | "She took the embankment in" | | 18 | "She crested the top and" | | 19 | "Her radio crackled on her" |
| | ratio | 0.434 | |
| 11.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 106 | | matches | | 0 | "The rat-grey sky wept over" | | 1 | "Rain slicked the pavement into" | | 2 | "Detective Harlow Quinn’s coat, heavy" | | 3 | "He glanced back, just a" | | 4 | "The recognition was instant." | | 5 | "A spark of panic." | | 6 | "Quinn pushed off the grimy" | | 7 | "The chase was on." | | 8 | "She vaulted a low barrier" | | 9 | "A black cab swerved, its" | | 10 | "She ignored it, her focus" | | 11 | "He was fast, weaving through" | | 12 | "He dodged a stumbling couple," | | 13 | "Quinn followed without hesitation, her" | | 14 | "The alley was a tight" | | 15 | "Herrera's footsteps echoed off the" | | 16 | "Quinn matched his rhythm, her" | | 17 | "She saw the scar on" | | 18 | "Her mind catalogued the detail," | | 19 | "The kind of people who" |
| | ratio | 0.896 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 106 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 3 | | matches | | 0 | "He was fast, weaving through the late-night revellers with a fluid grace that spoke of desperation." | | 1 | "The kind of people who left behind crime scenes that twisted in her gut and reminded her of Morris." | | 2 | "He veered off the main line, leaping down onto a service path that ran alongside a disused tunnel mouth." |
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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 | |