| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.61% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 928 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 928 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "echoes" | | 1 | "surreal" | | 2 | "dancing" | | 3 | "warmth" | | 4 | "scanning" | | 5 | "familiar" | | 6 | "glint" | | 7 | "intricate" | | 8 | "perfect" | | 9 | "uncanny" | | 10 | "glinting" | | 11 | "etched" | | 12 | "dance" | | 13 | "intensity" | | 14 | "furrowed" | | 15 | "determined" | | 16 | "unravel" | | 17 | "unraveling" | | 18 | "racing" |
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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 | 57 | | matches | | |
| 17.54% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 57 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "tended to" | | 2 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 922 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.31% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 745 | | uniqueNames | 4 | | maxNameDensity | 1.07 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 8 | | Miller | 7 | | Morris | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Morris" |
| | places | (empty) | | globalScore | 0.963 | | windowScore | 1 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite harsh but definitely lacking in warmth" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 3.254 | | wordCount | 922 | | matches | | 0 | "not quite harsh but definitely lacking in warmth" | | 1 | "not just online but" | | 2 | "No one touches it but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 31.79 | | std | 17.84 | | cv | 0.561 | | sampleLengths | | 0 | 68 | | 1 | 1 | | 2 | 45 | | 3 | 38 | | 4 | 39 | | 5 | 58 | | 6 | 27 | | 7 | 34 | | 8 | 37 | | 9 | 19 | | 10 | 9 | | 11 | 2 | | 12 | 30 | | 13 | 14 | | 14 | 28 | | 15 | 62 | | 16 | 35 | | 17 | 33 | | 18 | 23 | | 19 | 39 | | 20 | 32 | | 21 | 40 | | 22 | 25 | | 23 | 5 | | 24 | 21 | | 25 | 46 | | 26 | 6 | | 27 | 41 | | 28 | 65 |
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| 86.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 57 | | matches | | 0 | "was forgotten" | | 1 | "been dragged" | | 2 | "was determined" |
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| 99.75% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 133 | | matches | | 0 | "was already striding" | | 1 | "was racing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 68 | | ratio | 0.059 | | matches | | 0 | "The smell of damp earth and something else—ozone, perhaps—filled the air." | | 1 | "As she approached, Miller handed her a cup of what had to be tea—sweet and disturbed, exactly how he knew she liked it despite her never having asked." | | 2 | "They were intricate—unlike the crude gang tags she saw downtown." | | 3 | "Her eyes landed on something glinting near the edge of the platform—partially obscured by debris but clearly something metallic." |
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| 97.82% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 753 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.04249667994687915 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.0199203187250996 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 13.56 | | std | 8.45 | | cv | 0.623 | | sampleLengths | | 0 | 13 | | 1 | 17 | | 2 | 11 | | 3 | 14 | | 4 | 4 | | 5 | 9 | | 6 | 1 | | 7 | 8 | | 8 | 18 | | 9 | 19 | | 10 | 15 | | 11 | 16 | | 12 | 7 | | 13 | 28 | | 14 | 11 | | 15 | 10 | | 16 | 21 | | 17 | 9 | | 18 | 18 | | 19 | 21 | | 20 | 6 | | 21 | 11 | | 22 | 18 | | 23 | 5 | | 24 | 37 | | 25 | 4 | | 26 | 12 | | 27 | 3 | | 28 | 9 | | 29 | 2 | | 30 | 18 | | 31 | 12 | | 32 | 9 | | 33 | 5 | | 34 | 28 | | 35 | 14 | | 36 | 10 | | 37 | 11 | | 38 | 5 | | 39 | 22 | | 40 | 8 | | 41 | 27 | | 42 | 17 | | 43 | 12 | | 44 | 4 | | 45 | 23 | | 46 | 11 | | 47 | 28 | | 48 | 19 | | 49 | 13 |
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| 90.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5588235294117647 | | totalSentences | 68 | | uniqueOpeners | 38 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 55 | | matches | | 0 | "At least, that's what she" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 55 | | matches | | 0 | "She glanced at her watch," | | 1 | "It was how she always" | | 2 | "She accepted it, nodding her" | | 3 | "Their bodies were pale, almost" | | 4 | "She crouched beside one of" | | 5 | "She snapped photo after photo," | | 6 | "They were intricate—unlike the crude" | | 7 | "They reminded her of something..." | | 8 | "she murmured, circling the bodies" | | 9 | "She pulled out her phone," | | 10 | "Her eyes landed on something" | | 11 | "She caught her colleague's eye," | | 12 | "She turned back to the" |
| | ratio | 0.236 | |
| 41.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 55 | | matches | | 0 | "The station platform stretched endlessly" | | 1 | "Detective Harlow Quinn stepped carefully" | | 2 | "The smell of damp earth" | | 3 | "She glanced at her watch," | | 4 | "This case had pulled her" | | 5 | "The voice came from deeper" | | 6 | "DS Miller waved profusely, his" | | 7 | "The artificial lights set up" | | 8 | "Quinn called back, her voice" | | 9 | "It was how she always" | | 10 | "She accepted it, nodding her" | | 11 | "Their bodies were pale, almost" | | 12 | "This wasn't some pedestrian subway" | | 13 | "This platform was forgotten, part" | | 14 | "Miller explained, following her gaze" | | 15 | "Quinn sipped her tea, letting" | | 16 | "She crouched beside one of" | | 17 | "Miller asked, a hopeful glint" | | 18 | "Quinn ignored the bait." | | 19 | "A constable nearby adjusted the" |
| | ratio | 0.836 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 1 | | matches | | 0 | "This platform was forgotten, part of the labyrinthine abandoned system that swallowed people whole if they dared trespass." |
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| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 2 | | matches | | 0 | "She crouched, her eyes scanning everything around it" | | 1 | "Quinn replied, her voice dropping low" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn called back (call back)" | | 1 | "Miller explained (explain)" | | 2 | "she murmured (murmur)" |
| | dialogueSentences | 20 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.5 | | effectiveRatio | 0.3 | |