| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1395 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "reluctantly" | | count | 1 |
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| | 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) | |
| 53.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1395 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "stark" | | 1 | "predator" | | 2 | "gloom" | | 3 | "measured" | | 4 | "echoing" | | 5 | "flickered" | | 6 | "chill" | | 7 | "warmth" | | 8 | "chaotic" | | 9 | "velvet" | | 10 | "facade" | | 11 | "footsteps" | | 12 | "echoed" |
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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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 106 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 19 | | totalWords | 1395 | | ratio | 0.014 | | matches | | 0 | "to" | | 1 | "Of course it is." | | 2 | "Do not enter an unknown, confined space alone. Call for backup. Secure the perimeter." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1232 | | uniqueNames | 7 | | maxNameDensity | 0.89 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Herrera | 11 | | Saint | 1 | | Christopher | 1 | | Morris | 2 | | Tube | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.717 | | wordCount | 1395 | | matches | | 0 | "not towards the street, but towards a heavy metal door half-hidden behind a stack of rot" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 45 | | std | 24.79 | | cv | 0.551 | | sampleLengths | | 0 | 79 | | 1 | 39 | | 2 | 77 | | 3 | 63 | | 4 | 43 | | 5 | 55 | | 6 | 42 | | 7 | 40 | | 8 | 68 | | 9 | 4 | | 10 | 52 | | 11 | 89 | | 12 | 76 | | 13 | 71 | | 14 | 47 | | 15 | 5 | | 16 | 24 | | 17 | 14 | | 18 | 5 | | 19 | 61 | | 20 | 38 | | 21 | 26 | | 22 | 18 | | 23 | 26 | | 24 | 30 | | 25 | 45 | | 26 | 34 | | 27 | 37 | | 28 | 41 | | 29 | 40 | | 30 | 106 |
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| 95.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 106 | | matches | | 0 | "was tiled" | | 1 | "were crammed" | | 2 | "was boxed" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 187 | | matches | | 0 | "wasn't just running" | | 1 | "was running" | | 2 | "was heading" | | 3 | "was hawking" | | 4 | "was weighing" | | 5 | "was getting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 114 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1099 | | adjectiveStacks | 1 | | stackExamples | | 0 | "metallic, washed over Quinn." |
| | adverbCount | 20 | | adverbRatio | 0.018198362147406732 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007279344858962694 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 12.24 | | std | 7.89 | | cv | 0.645 | | sampleLengths | | 0 | 16 | | 1 | 26 | | 2 | 24 | | 3 | 13 | | 4 | 21 | | 5 | 12 | | 6 | 6 | | 7 | 19 | | 8 | 6 | | 9 | 5 | | 10 | 4 | | 11 | 20 | | 12 | 8 | | 13 | 15 | | 14 | 4 | | 15 | 29 | | 16 | 7 | | 17 | 19 | | 18 | 4 | | 19 | 11 | | 20 | 13 | | 21 | 13 | | 22 | 5 | | 23 | 1 | | 24 | 20 | | 25 | 16 | | 26 | 19 | | 27 | 4 | | 28 | 10 | | 29 | 18 | | 30 | 6 | | 31 | 4 | | 32 | 17 | | 33 | 3 | | 34 | 20 | | 35 | 4 | | 36 | 17 | | 37 | 4 | | 38 | 25 | | 39 | 14 | | 40 | 4 | | 41 | 4 | | 42 | 19 | | 43 | 18 | | 44 | 15 | | 45 | 4 | | 46 | 22 | | 47 | 6 | | 48 | 4 | | 49 | 13 |
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| 35.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.30701754385964913 | | totalSentences | 114 | | uniqueOpeners | 35 | |
| 33.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 100 | | matches | | 0 | "*Of course it is.* Her" |
| | ratio | 0.01 | |
| 80.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 100 | | matches | | 0 | "Her quarry, a man in" | | 1 | "He glanced back, his warm" | | 2 | "He wasn't just running from" | | 3 | "He was running *to* something." | | 4 | "His Saint Christopher medallion caught" | | 5 | "He saw her gaining, and" | | 6 | "He cut left, into a" | | 7 | "Her breath came in controlled," | | 8 | "Her hand rested on the" | | 9 | "He went down hard, a" | | 10 | "She was ten yards away." | | 11 | "He scrambled up, not towards" | | 12 | "He yanked it open, the" | | 13 | "She reached the door." | | 14 | "It led to a stairwell," | | 15 | "He was heading down." | | 16 | "He hadn't come out." | | 17 | "She took the stairs." | | 18 | "She caught sight of Herrera" | | 19 | "She surged towards it, her" |
| | ratio | 0.35 | |
| 15.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 100 | | matches | | 0 | "The rain hammered the pavement," | | 1 | "Her quarry, a man in" | | 2 | "Detective Harlow Quinn was a" | | 3 | "Every muscle in her body" | | 4 | "The man, Herrera, was a" | | 5 | "The file on him was" | | 6 | "Tonight, he was her only" | | 7 | "He glanced back, his warm" | | 8 | "He wasn't just running from" | | 9 | "He was running *to* something." | | 10 | "His Saint Christopher medallion caught" | | 11 | "He saw her gaining, and" | | 12 | "He cut left, into a" | | 13 | "Quinn followed without hesitation." | | 14 | "The alley was a black" | | 15 | "Her breath came in controlled," | | 16 | "Her hand rested on the" | | 17 | "This was her collar." | | 18 | "He went down hard, a" | | 19 | "Quinn closed the distance, her" |
| | ratio | 0.89 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 6 | | matches | | 0 | "The alley was a black throat that swallowed the city's noise, replacing it with the drumming of rain on metal fire escapes and the gurgle of a clogged drain." | | 1 | "A single, bare bulb cast a sickly yellow light on the concrete walls, which were slick with condensation." | | 2 | "The air grew heavier with each step, the city's damp chill replaced by a subterranean warmth that felt wrong." | | 3 | "A man with a face full of piercings and ears that tapered to a point stared at her, his gaze lingering on the warrant card held tight in her fist, a challenge i…" | | 4 | "She surged towards it, her shoulder brushing against a man who hissed at her, his breath smelling of ozone and sulphur." | | 5 | "And as the figure behind Herrera stepped into the dim light, a grin spreading across a face that was more rock than flesh, Quinn knew her night had just gone fr…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn said, her voice steady despite the frantic thudding in her chest" | | 1 | "Quinn pressed, the urgency rising in her voice" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "Quinn pressed (press)" |
| | dialogueSentences | 12 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.667 | | effectiveRatio | 0.333 | |