| 53.66% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 3 | | adverbTags | | 0 | "Tomás answered honestly [honestly]" | | 1 | "Tomás explained patiently [patiently]" | | 2 | "Tomás said gently [gently]" |
| | dialogueSentences | 41 | | tagDensity | 0.439 | | leniency | 0.878 | | rawRatio | 0.167 | | effectiveRatio | 0.146 | |
| 75.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2200 | | totalAiIsmAdverbs | 11 | | found | | | highlights | | 0 | "slightly" | | 1 | "softly" | | 2 | "very" | | 3 | "precisely" | | 4 | "completely" | | 5 | "slowly" | | 6 | "truly" | | 7 | "gently" |
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| 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) | |
| 61.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2200 | | totalAiIsms | 17 | | found | | 0 | | word | "down her spine" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "down her spine" | | 1 | "chaotic" | | 2 | "pulsed" | | 3 | "quickened" | | 4 | "absolutely" | | 5 | "flicked" | | 6 | "silence" | | 7 | "racing" | | 8 | "potential" | | 9 | "footsteps" | | 10 | "intensity" | | 11 | "tension" | | 12 | "calculating" | | 13 | "glinting" | | 14 | "silk" | | 15 | "single tear" |
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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 | 136 | | matches | (empty) | |
| 79.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 136 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 157 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2211 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 1744 | | uniqueNames | 22 | | maxNameDensity | 1.09 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 19 | | Tuesday | 3 | | Blackwood | 1 | | Clique | 2 | | Raven | 2 | | Nest | 2 | | Trinity-cloaked | 1 | | Sergeant | 1 | | Wilson | 1 | | Morris | 8 | | Herrera | 1 | | Saint | 2 | | Christopher | 2 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Tube | 1 | | Old | 1 | | Tomás | 16 | | Spanish | 1 | | Ermittled | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Blackwood" | | 3 | "Clique" | | 4 | "Raven" | | 5 | "Sergeant" | | 6 | "Wilson" | | 7 | "Morris" | | 8 | "Herrera" | | 9 | "Saint" | | 10 | "Christopher" | | 11 | "Camden" | | 12 | "Old" | | 13 | "Tomás" |
| | places | | | globalScore | 0.955 | | windowScore | 0.667 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 114 | | glossingSentenceCount | 3 | | matches | | 0 | "humming that seemed to vibrate through the pavement" | | 1 | "felt like static electricity prickled a" | | 2 | "Not quite the scent of decay, not precisely stale air" |
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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 | 2211 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 157 | | matches | | 0 | "knew – that her" | | 1 | "suspected – that something" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 42.52 | | std | 21.93 | | cv | 0.516 | | sampleLengths | | 0 | 90 | | 1 | 29 | | 2 | 38 | | 3 | 31 | | 4 | 74 | | 5 | 100 | | 6 | 73 | | 7 | 72 | | 8 | 66 | | 9 | 59 | | 10 | 78 | | 11 | 56 | | 12 | 35 | | 13 | 45 | | 14 | 67 | | 15 | 59 | | 16 | 66 | | 17 | 41 | | 18 | 34 | | 19 | 42 | | 20 | 73 | | 21 | 5 | | 22 | 22 | | 23 | 21 | | 24 | 53 | | 25 | 15 | | 26 | 28 | | 27 | 18 | | 28 | 24 | | 29 | 45 | | 30 | 35 | | 31 | 11 | | 32 | 34 | | 33 | 51 | | 34 | 18 | | 35 | 30 | | 36 | 64 | | 37 | 32 | | 38 | 9 | | 39 | 42 | | 40 | 28 | | 41 | 43 | | 42 | 52 | | 43 | 13 | | 44 | 44 | | 45 | 56 | | 46 | 16 | | 47 | 62 | | 48 | 19 | | 49 | 38 |
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| 92.36% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 136 | | matches | | 0 | "been ten" | | 1 | "been taken" | | 2 | "was lined" | | 3 | "being seen" | | 4 | "been built" |
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| 60.63% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 287 | | matches | | 0 | "were feeling" | | 1 | "wasn't just chasing" | | 2 | "was chasing" | | 3 | "was happening" | | 4 | "was getting" | | 5 | "was standing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 157 | | ratio | 0.051 | | matches | | 0 | "As she approached the station, a faint sound cut through the drumming rain – a low humming that seemed to vibrate through the pavement." | | 1 | "The jedemarket lay beyond this entrance, and she knew – absolutely knew – that her suspect had gone this way." | | 2 | "As she reached the bottom, she realized the air had changed – thick with something she couldn't identify." | | 3 | "This was it – the decision that would determine whether Quinn lived to see sunrise or became another name in the files of people who disappeared in the veins of this city." | | 4 | "Her eyes glanced at her watch – precisely three minutes past two." | | 5 | "The police would laugh her out of office if she mentioned what she suspected – that something supernatural was happening in this city." | | 6 | "Quinn studied him – the olive skin slick with rain, the dark curly hair plastered to his forehead, the scar, the Saint Christopher medallion, and those warm brown eyes that held no deceit that she could detect." | | 7 | "The people she'd been investigating weren't just criminals – they were something else entirely." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1743 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 67 | | adverbRatio | 0.03843947217441193 | | lyAdverbCount | 31 | | lyAdverbRatio | 0.01778542742398164 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 157 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 157 | | mean | 14.08 | | std | 7.88 | | cv | 0.56 | | sampleLengths | | 0 | 22 | | 1 | 27 | | 2 | 12 | | 3 | 9 | | 4 | 15 | | 5 | 5 | | 6 | 29 | | 7 | 15 | | 8 | 23 | | 9 | 17 | | 10 | 14 | | 11 | 13 | | 12 | 8 | | 13 | 28 | | 14 | 11 | | 15 | 14 | | 16 | 15 | | 17 | 18 | | 18 | 13 | | 19 | 7 | | 20 | 14 | | 21 | 33 | | 22 | 13 | | 23 | 13 | | 24 | 17 | | 25 | 16 | | 26 | 14 | | 27 | 17 | | 28 | 15 | | 29 | 11 | | 30 | 11 | | 31 | 18 | | 32 | 24 | | 33 | 16 | | 34 | 21 | | 35 | 5 | | 36 | 16 | | 37 | 17 | | 38 | 14 | | 39 | 12 | | 40 | 9 | | 41 | 10 | | 42 | 18 | | 43 | 20 | | 44 | 16 | | 45 | 5 | | 46 | 9 | | 47 | 16 | | 48 | 15 | | 49 | 8 |
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| 53.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.34394904458598724 | | totalSentences | 157 | | uniqueOpeners | 54 | |
| 50.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 131 | | matches | | 0 | "Only she had known about" | | 1 | "Only the drips of water" |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 131 | | matches | | 0 | "she muttered into her collar" | | 1 | "she said, her focus locked" | | 2 | "She'd been chasing shadows since" | | 3 | "He'd been ten blocks behind" | | 4 | "She was chasing a ghost" | | 5 | "He quickened his pace, ignoring" | | 6 | "His generous olive skin, already" | | 7 | "She felt rather than saw" | | 8 | "Her breath came in controlled" | | 9 | "She'd passed this station a" | | 10 | "Her worn leather watch glowed" | | 11 | "She paused beneath the station" | | 12 | "He'd been inside once, passed" | | 13 | "She flicked it on, the" | | 14 | "Her fingers tightened around the" | | 15 | "She hesitated only for a" | | 16 | "His breath came steady despite" | | 17 | "Her eyes glanced at her" | | 18 | "She put her ear to" | | 19 | "She reached for the handle." |
| | ratio | 0.237 | |
| 85.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 131 | | matches | | 0 | "Detective Harlow Quinn didn't notice" | | 1 | "The sting of cold water" | | 2 | "Every Tuesday for the last" | | 3 | "Every Tuesday, Blackwood's Clique gathered" | | 4 | "Tonight would not be different." | | 5 | "she muttered into her collar" | | 6 | "she said, her focus locked" | | 7 | "She85 slammed the car door" | | 8 | "The rain hammered down, turning" | | 9 | "Water drummed a frantic rhythm" | | 10 | "She'd been chasing shadows since" | | 11 | "Tomás Herrera wiped the rain" | | 12 | "The Saint Christopher medallion around" | | 13 | "He'd been ten blocks behind" | | 14 | "She was chasing a ghost" | | 15 | "The distinctive green neon sign" | | 16 | "The back entrance to the" | | 17 | "He quickened his pace, ignoring" | | 18 | "His generous olive skin, already" | | 19 | "The bone token he'd kept" |
| | ratio | 0.748 | |
| 76.34% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 131 | | matches | | 0 | "If Quinn didn't know what" | | 1 | "Now someone was standing in" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 79 | | technicalSentenceCount | 4 | | matches | | 0 | "The sting of cold water against her skin did little to clear the fog of determination that had settled over her mind for the past three years." | | 1 | "Something that felt like static electricity prickled across her skin." | | 2 | "And now, someone who appeared to know intimate details about her late partner had cornered her in an abandoned station on a rain-soaked night." | | 3 | "Quinn studied him – the olive skin slick with rain, the dark curly hair plastered to his forehead, the scar, the Saint Christopher medallion, and those warm bro…" |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 3 | | matches | | 0 | "she asked, her expression inscrutable despite the tension radiating from her frame" | | 1 | "Tomás repeated, his gaze steady" | | 2 | "Quinn asked, her voice low" |
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| 3.66% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 6 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Tomás continued (continue)" | | 2 | "Tomás repeated (repeat)" | | 3 | "she demanded (demand)" | | 4 | "Tomás explained (explain)" | | 5 | "Tomás explained patiently (explain)" |
| | dialogueSentences | 41 | | tagDensity | 0.415 | | leniency | 0.829 | | rawRatio | 0.353 | | effectiveRatio | 0.293 | |