| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 5 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" | | 1 | "He glanced around [around]" | | 2 | "He leaned back [back]" | | 3 | "he finally said [finally]" | | 4 | "He knocked back [back]" |
| | dialogueSentences | 60 | | tagDensity | 0.367 | | leniency | 0.733 | | rawRatio | 0.227 | | effectiveRatio | 0.167 | |
| 90.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1506 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "softly" | | 1 | "truly" | | 2 | "utterly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 40.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1506 | | totalAiIsms | 18 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "carried the weight" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "shattered" | | 1 | "glinting" | | 2 | "lilt" | | 3 | "carried the weight" | | 4 | "tracing" | | 5 | "footsteps" | | 6 | "flicker" | | 7 | "silence" | | 8 | "charged" | | 9 | "familiar" | | 10 | "pulse" | | 11 | "predator" | | 12 | "stark" | | 13 | "scanning" | | 14 | "crystal" | | 15 | "efficient" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "let out a breath" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "let out a breath" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 112 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 112 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 148 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 6 | | totalWords | 1499 | | ratio | 0.004 | | matches | | 0 | "Alright?" | | 1 | "Fine." | | 2 | "I changed my mind." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 1114 | | uniqueNames | 8 | | maxNameDensity | 0.63 | | worstName | "Evan" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 6 | | Raven | 1 | | Nest | 1 | | Cardiff | 1 | | Silas | 6 | | Evan | 7 | | Cheung | 1 | | Yu-Fei | 2 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Silas" | | 4 | "Evan" | | 5 | "Cheung" | | 6 | "Yu-Fei" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 85.06% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 2 | | matches | | 0 | "not quite reaching his eyes" | | 1 | "as if confirming a long-held suspicion" |
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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 | 1499 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 148 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 23.79 | | std | 19.45 | | cv | 0.817 | | sampleLengths | | 0 | 9 | | 1 | 74 | | 2 | 24 | | 3 | 7 | | 4 | 40 | | 5 | 75 | | 6 | 1 | | 7 | 21 | | 8 | 18 | | 9 | 43 | | 10 | 47 | | 11 | 10 | | 12 | 15 | | 13 | 4 | | 14 | 21 | | 15 | 36 | | 16 | 7 | | 17 | 37 | | 18 | 2 | | 19 | 38 | | 20 | 2 | | 21 | 13 | | 22 | 55 | | 23 | 45 | | 24 | 2 | | 25 | 78 | | 26 | 44 | | 27 | 12 | | 28 | 30 | | 29 | 16 | | 30 | 5 | | 31 | 16 | | 32 | 7 | | 33 | 38 | | 34 | 11 | | 35 | 54 | | 36 | 6 | | 37 | 24 | | 38 | 37 | | 39 | 50 | | 40 | 15 | | 41 | 63 | | 42 | 6 | | 43 | 13 | | 44 | 29 | | 45 | 7 | | 46 | 24 | | 47 | 11 | | 48 | 29 | | 49 | 6 |
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| 99.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 112 | | matches | | 0 | "was gone" | | 1 | "was cropped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 174 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 148 | | ratio | 0.014 | | matches | | 0 | "His scent enveloped her—leather, whisky, that faint, familiar hint of his old soap." | | 1 | "She watched the conflict play out on his face—anger, defence, and finally, a weary acceptance." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1121 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.03746654772524532 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01159678858162355 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 148 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 148 | | mean | 10.13 | | std | 7.45 | | cv | 0.736 | | sampleLengths | | 0 | 9 | | 1 | 3 | | 2 | 23 | | 3 | 19 | | 4 | 22 | | 5 | 6 | | 6 | 1 | | 7 | 24 | | 8 | 7 | | 9 | 22 | | 10 | 9 | | 11 | 9 | | 12 | 12 | | 13 | 6 | | 14 | 25 | | 15 | 12 | | 16 | 9 | | 17 | 7 | | 18 | 2 | | 19 | 2 | | 20 | 1 | | 21 | 16 | | 22 | 5 | | 23 | 7 | | 24 | 11 | | 25 | 25 | | 26 | 18 | | 27 | 12 | | 28 | 25 | | 29 | 10 | | 30 | 5 | | 31 | 5 | | 32 | 12 | | 33 | 3 | | 34 | 4 | | 35 | 7 | | 36 | 14 | | 37 | 4 | | 38 | 23 | | 39 | 9 | | 40 | 7 | | 41 | 7 | | 42 | 28 | | 43 | 2 | | 44 | 2 | | 45 | 27 | | 46 | 11 | | 47 | 2 | | 48 | 6 | | 49 | 7 |
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| 52.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3783783783783784 | | totalSentences | 148 | | uniqueOpeners | 56 | |
| 64.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 103 | | matches | | 0 | "Too much for two drinks." | | 1 | "Then he was moving, joining" |
| | ratio | 0.019 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 58 | | totalSentences | 103 | | matches | | 0 | "She didn’t flinch." | | 1 | "She just stared at the" | | 2 | "She was mopping up the" | | 3 | "She froze, the wet rag" | | 4 | "She knew that voice before" | | 5 | "His hair, once a floppy" | | 6 | "He slid onto the stool" | | 7 | "She tossed the soggy rag" | | 8 | "He said it without malice," | | 9 | "He lingered just a second" | | 10 | "She didn’t touch her drink." | | 11 | "He knocked his whisky back" | | 12 | "He corrected her, his gaze" | | 13 | "She remembered the dress." | | 14 | "She also remembered the argument" | | 15 | "He gestured vaguely east" | | 16 | "He finally looked at her" | | 17 | "He swirled the ice in" | | 18 | "She could give him the" | | 19 | "*I changed my mind.* But" |
| | ratio | 0.563 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 103 | | matches | | 0 | "The glass shattered two inches" | | 1 | "She didn’t flinch." | | 2 | "She just stared at the" | | 3 | "The drunk at the end" | | 4 | "Silas, polishing a tumbler with" | | 5 | "She was mopping up the" | | 6 | "The voice was older, sandpapered" | | 7 | "She froze, the wet rag" | | 8 | "She knew that voice before" | | 9 | "Evan leaned against the bar," | | 10 | "The boy she’d known was" | | 11 | "The softness in his cheeks" | | 12 | "His hair, once a floppy" | | 13 | "Knuckles scarred, the nails clean" | | 14 | "He slid onto the stool" | | 15 | "Rory said, her voice flat" | | 16 | "She tossed the soggy rag" | | 17 | "He said it without malice," | | 18 | "Silas placed the drinks without" | | 19 | "He lingered just a second" |
| | ratio | 0.942 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "Two men in dark, expensive coats entered, their eyes scanning the room before settling on a corner table." | | 1 | "Evan’s posture shifted infinitesimally, his shoulders squaring, his gaze tracking them without seeming to." | | 2 | "Rory was already moving, the glass of whisky forgotten, the ghost of Evan’s apology evaporating in the face of a new, immediate terror." |
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| 11.36% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 5 | | matches | | 0 | "Rory said, her voice flat" | | 1 | "He corrected, his gaze steady" | | 2 | "his eyes tracing, the set of her shoulders" | | 3 | "He leaned back, a smirk playing on his lips" | | 4 | "he asked, his tone neutral" |
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| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 4 | | fancyTags | | 0 | "He corrected (correct)" | | 1 | "she repeated (repeat)" | | 2 | "he whispered (whisper)" | | 3 | "Rory murmured (murmur)" |
| | dialogueSentences | 60 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.4 | | effectiveRatio | 0.133 | |