| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow mused aloud [aloud]" |
| | dialogueSentences | 28 | | tagDensity | 0.393 | | leniency | 0.786 | | rawRatio | 0.091 | | effectiveRatio | 0.071 | |
| 85.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1051 | | totalAiIsmAdverbs | 3 | | 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) | |
| 14.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1051 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "dancing" | | 3 | "scanned" | | 4 | "gloom" | | 5 | "etched" | | 6 | "apprehension" | | 7 | "racing" | | 8 | "unspoken" | | 9 | "glint" | | 10 | "resolve" | | 11 | "delve" | | 12 | "whisper" | | 13 | "weight" | | 14 | "determined" | | 15 | "unravel" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 69 | | matches | | 0 | "t in determination" | | 1 | "t in determination" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 69 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1047 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 856 | | uniqueNames | 10 | | maxNameDensity | 3.15 | | worstName | "Harlow" | | maxWindowNameDensity | 6 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 27 | | Quinn | 2 | | Tube | 1 | | Camden | 1 | | Veil | 4 | | Market | 6 | | Kowalski | 1 | | Eva | 16 | | Compass | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 1 | | matches | | 0 | "shadows that seemed to whisper secrets, and Harlow felt the weight of the unknown pressing down on her" |
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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 | 1047 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 29.91 | | std | 19.8 | | cv | 0.662 | | sampleLengths | | 0 | 88 | | 1 | 69 | | 2 | 52 | | 3 | 18 | | 4 | 11 | | 5 | 28 | | 6 | 59 | | 7 | 7 | | 8 | 13 | | 9 | 43 | | 10 | 7 | | 11 | 20 | | 12 | 50 | | 13 | 28 | | 14 | 14 | | 15 | 21 | | 16 | 20 | | 17 | 39 | | 18 | 18 | | 19 | 18 | | 20 | 40 | | 21 | 38 | | 22 | 13 | | 23 | 10 | | 24 | 24 | | 25 | 8 | | 26 | 14 | | 27 | 23 | | 28 | 32 | | 29 | 19 | | 30 | 16 | | 31 | 52 | | 32 | 51 | | 33 | 64 | | 34 | 20 |
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| 69.67% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 69 | | matches | | 0 | "was supposed" | | 1 | "was, called" | | 2 | "been displayed" | | 3 | "was etched" | | 4 | "was tailored" | | 5 | "was determined" | | 6 | "was determined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 156 | | matches | | |
| 76.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 86 | | ratio | 0.023 | | matches | | 0 | "The air was thick with the scent of damp earth and something else—something metallic and unsettling." | | 1 | "The Market's shifting nature, the man's presence, the compass—it all hinted at something deeper." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 859 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.01979045401629802 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.011641443538998836 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 12.17 | | std | 5.31 | | cv | 0.436 | | sampleLengths | | 0 | 24 | | 1 | 16 | | 2 | 18 | | 3 | 17 | | 4 | 13 | | 5 | 16 | | 6 | 8 | | 7 | 24 | | 8 | 21 | | 9 | 15 | | 10 | 16 | | 11 | 15 | | 12 | 6 | | 13 | 11 | | 14 | 7 | | 15 | 8 | | 16 | 3 | | 17 | 7 | | 18 | 21 | | 19 | 8 | | 20 | 11 | | 21 | 16 | | 22 | 13 | | 23 | 11 | | 24 | 7 | | 25 | 7 | | 26 | 6 | | 27 | 7 | | 28 | 17 | | 29 | 19 | | 30 | 7 | | 31 | 10 | | 32 | 10 | | 33 | 10 | | 34 | 17 | | 35 | 12 | | 36 | 11 | | 37 | 11 | | 38 | 17 | | 39 | 5 | | 40 | 9 | | 41 | 12 | | 42 | 9 | | 43 | 5 | | 44 | 15 | | 45 | 10 | | 46 | 14 | | 47 | 15 | | 48 | 9 | | 49 | 9 |
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| 50.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.313953488372093 | | totalSentences | 86 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 69 | | matches | | 0 | "She paused, her sharp jaw" | | 1 | "It was just past midnight," | | 2 | "Her gaze settled on a" | | 3 | "She adjusted her round glasses," | | 4 | "Her freckled face was etched" | | 5 | "His face was pale, eyes" | | 6 | "She stood, her gaze sweeping" | | 7 | "she asked, turning to Eva" | | 8 | "She moved closer to the" | | 9 | "She glanced at Eva, who" | | 10 | "She turned her attention back" | | 11 | "His tie was askew, and" | | 12 | "She needed more information, more" | | 13 | "She needed to understand the" | | 14 | "Her sharp jaw set in" | | 15 | "It was a world she" |
| | ratio | 0.232 | |
| 3.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 69 | | matches | | 0 | "Detective Harlow Quinn stepped into" | | 1 | "The air was thick with" | | 2 | "She paused, her sharp jaw" | | 3 | "It was just past midnight," | | 4 | "The flickering light from the" | | 5 | "Harlow's eyes, keen and observant," | | 6 | "The usual stalls of the" | | 7 | "Her gaze settled on a" | | 8 | "Eva Kowalski stood among them," | | 9 | "She adjusted her round glasses," | | 10 | "Her freckled face was etched" | | 11 | "Harlow approached, her presence commanding" | | 12 | "Eva greeted, her voice a" | | 13 | "Harlow replied, her tone clipped" | | 14 | "Eva gestured to the scene" | | 15 | "Harlow's eyes narrowed as she" | | 16 | "The body lay sprawled near" | | 17 | "A man, mid-thirties, dressed in" | | 18 | "His face was pale, eyes" | | 19 | "Harlow crouched beside him, noting" |
| | ratio | 0.913 | |
| 72.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 69 | | matches | | 0 | "Yet, here she was, called" |
| | ratio | 0.014 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "She turned her attention back to the body, searching for clues that might reveal the truth." | | 1 | "His tie was askew, and his shoes bore scuff marks, as if he'd been running." | | 2 | "It was a world she had only glimpsed, a world that had taken her partner, DS Morris, three years ago." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 7 | | matches | | 0 | "Eva greeted, her voice a mix of relief and apprehension" | | 1 | "Harlow replied, her tone clipped" | | 2 | "Eva suggested, her voice uncertain" | | 3 | "Harlow murmured, her mind already racing" | | 4 | "Harlow said, her voice firm" | | 5 | "Harlow replied, her voice thoughtful" | | 6 | "Harlow asked, her tone decisive" |
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| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Eva suggested (suggest)" | | 1 | "Harlow murmured (murmur)" |
| | dialogueSentences | 28 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.25 | | effectiveRatio | 0.143 | |