| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said softly [softly]" |
| | dialogueSentences | 60 | | tagDensity | 0.283 | | leniency | 0.567 | | rawRatio | 0.059 | | effectiveRatio | 0.033 | |
| 83.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1520 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "quickly" | | 1 | "utterly" | | 2 | "very" | | 3 | "slowly" | | 4 | "softly" |
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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) | |
| 67.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1520 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "scanning" | | 1 | "stomach" | | 2 | "etched" | | 3 | "intensity" | | 4 | "chill" | | 5 | "traced" | | 6 | "silence" | | 7 | "standard" | | 8 | "flicker" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 116 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 116 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 159 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 11 | | totalWords | 1508 | | ratio | 0.007 | | matches | | 0 | "click" | | 1 | "File" | | 2 | "what" | | 3 | "Don’t give them a thing. Not a flicker." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 1012 | | uniqueNames | 12 | | maxNameDensity | 0.59 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Rory | 3 | | Flynn | 1 | | Cardiff | 1 | | Belfast | 2 | | Guinness | 1 | | Scotch | 1 | | Silas | 6 | | London | 1 | | Soho | 1 | | Connor | 3 |
| | persons | | 0 | "Rory" | | 1 | "Flynn" | | 2 | "Guinness" | | 3 | "Silas" | | 4 | "Connor" |
| | places | | 0 | "Raven" | | 1 | "Cardiff" | | 2 | "Belfast" | | 3 | "London" | | 4 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a brand against her thigh" |
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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 | 1508 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 159 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 22.18 | | std | 17.01 | | cv | 0.767 | | sampleLengths | | 0 | 69 | | 1 | 7 | | 2 | 65 | | 3 | 59 | | 4 | 2 | | 5 | 56 | | 6 | 29 | | 7 | 2 | | 8 | 26 | | 9 | 22 | | 10 | 7 | | 11 | 42 | | 12 | 4 | | 13 | 13 | | 14 | 6 | | 15 | 33 | | 16 | 4 | | 17 | 6 | | 18 | 22 | | 19 | 20 | | 20 | 32 | | 21 | 2 | | 22 | 28 | | 23 | 6 | | 24 | 11 | | 25 | 35 | | 26 | 8 | | 27 | 9 | | 28 | 35 | | 29 | 20 | | 30 | 36 | | 31 | 3 | | 32 | 16 | | 33 | 24 | | 34 | 10 | | 35 | 29 | | 36 | 2 | | 37 | 5 | | 38 | 3 | | 39 | 44 | | 40 | 10 | | 41 | 30 | | 42 | 3 | | 43 | 56 | | 44 | 25 | | 45 | 7 | | 46 | 37 | | 47 | 18 | | 48 | 41 | | 49 | 6 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 116 | | matches | | 0 | "been softened" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 162 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 159 | | ratio | 0.013 | | matches | | 0 | "He clocked the exits first—the main door, the fire exit by the toilets, the staff-only door behind her." | | 1 | "The low chatter from the corner, the clink of glass, the hum of the cooler—all of it receded into a dull, throbbing silence." |
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| 93.90% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1022 | | adjectiveStacks | 1 | | stackExamples | | 0 | "simple, cream-coloured business" |
| | adverbCount | 41 | | adverbRatio | 0.040117416829745595 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.012720156555772993 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 159 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 159 | | mean | 9.48 | | std | 6.88 | | cv | 0.726 | | sampleLengths | | 0 | 15 | | 1 | 22 | | 2 | 16 | | 3 | 16 | | 4 | 3 | | 5 | 4 | | 6 | 3 | | 7 | 19 | | 8 | 16 | | 9 | 18 | | 10 | 3 | | 11 | 6 | | 12 | 8 | | 13 | 4 | | 14 | 17 | | 15 | 20 | | 16 | 6 | | 17 | 4 | | 18 | 2 | | 19 | 8 | | 20 | 19 | | 21 | 18 | | 22 | 6 | | 23 | 2 | | 24 | 3 | | 25 | 7 | | 26 | 14 | | 27 | 8 | | 28 | 2 | | 29 | 8 | | 30 | 18 | | 31 | 12 | | 32 | 3 | | 33 | 1 | | 34 | 6 | | 35 | 7 | | 36 | 3 | | 37 | 20 | | 38 | 19 | | 39 | 3 | | 40 | 1 | | 41 | 6 | | 42 | 7 | | 43 | 6 | | 44 | 2 | | 45 | 22 | | 46 | 5 | | 47 | 4 | | 48 | 4 | | 49 | 6 |
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| 50.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3584905660377358 | | totalSentences | 159 | | uniqueOpeners | 57 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 104 | | matches | | 0 | "Then the patrons." | | 1 | "Finally, his gaze settled on" | | 2 | "Just a phone number, engraved." | | 3 | "Then he straightened, the moment" |
| | ratio | 0.038 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 58 | | totalSentences | 104 | | matches | | 0 | "He moved all wrong." | | 1 | "He clocked the exits first—the" | | 2 | "She knew that face." | | 3 | "He’d been in her law" | | 4 | "They’d shared coffees and conspiratorial" | | 5 | "He’d vanished after their second" | | 6 | "He saw the recognition in" | | 7 | "He approached the bar, his" | | 8 | "His voice was deeper, rougher" | | 9 | "She kept her tone neutral," | | 10 | "They were steady." | | 11 | "He didn’t sit." | | 12 | "He stood, his coat still" | | 13 | "She reached for the mid-shelf" | | 14 | "His hand, resting on the" | | 15 | "She poured two fingers into" | | 16 | "She set it before him." | | 17 | "He didn’t touch it." | | 18 | "He finally picked up the" | | 19 | "He took a sip, his" |
| | ratio | 0.558 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 104 | | matches | | 0 | "The scent of stale beer" | | 1 | "Rory wiped down the counter" | | 2 | "Silas had asked her to" | | 3 | "A man entered." | | 4 | "He moved all wrong." | | 5 | "Regulars slid in like they" | | 6 | "This one paused on the" | | 7 | "He clocked the exits first—the" | | 8 | "A cold, hard knot formed" | | 9 | "She knew that face." | | 10 | "The lean, hungry build of" | | 11 | "A flat, watchful grey." | | 12 | "He’d been in her law" | | 13 | "The quiet one from Belfast" | | 14 | "They’d shared coffees and conspiratorial" | | 15 | "He’d vanished after their second" | | 16 | "He saw the recognition in" | | 17 | "A ghost of a smile" | | 18 | "He approached the bar, his" | | 19 | "His voice was deeper, rougher" |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 104 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 4 | | matches | | 0 | "Regulars slid in like they were part of the furniture, with a weary familiarity that matched the dim light." | | 1 | "The quiet one from Belfast who quoted obscure political theorists and could dismantle an argument with terrifying, surgical precision." | | 2 | "A Belfast accent that had been softened by university was now pronounced again, each syllable a deliberate choice." | | 3 | "His gaze drifted past her shoulder, towards the bookshelf that hid Silas’s private room." |
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| 66.18% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 2 | | matches | | 0 | "She kept, her hands busy beneath the counter" | | 1 | "she said, her voice strangely calm" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 60 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0.125 | | effectiveRatio | 0.033 | |