| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 552 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 27.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 552 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "weight" | | 2 | "pulse" | | 3 | "footsteps" | | 4 | "gloom" | | 5 | "electric" | | 6 | "racing" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "air was thick with" | | count | 2 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 38 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 38 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 38 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 543 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 57.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 543 | | uniqueNames | 12 | | maxNameDensity | 1.84 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 10 | | Tomás | 1 | | Herrera | 5 | | Soho | 1 | | Tube | 1 | | Saint | 1 | | Christopher | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Market" |
| | places | | | globalScore | 0.579 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 543 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 38 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 38.79 | | std | 22.06 | | cv | 0.569 | | sampleLengths | | 0 | 65 | | 1 | 7 | | 2 | 51 | | 3 | 55 | | 4 | 52 | | 5 | 64 | | 6 | 49 | | 7 | 7 | | 8 | 41 | | 9 | 50 | | 10 | 47 | | 11 | 48 | | 12 | 5 | | 13 | 2 |
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| 96.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 38 | | matches | | |
| 24.56% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 76 | | matches | | 0 | "was gaining" | | 1 | "was already moving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 38 | | ratio | 0.158 | | matches | | 0 | "Detective Harlow Quinn’s breath came in shallow bursts, her leather watch—worn and timeworn—ticking against the damp fabric of her coat." | | 1 | "Quinn had followed him through the usual routes—back alleys, doorways, the flickering glow of streetlamps—but he’d slipped away like smoke." | | 2 | "The air was thick with the scent of rust and something else—something sour, like old blood." | | 3 | "She could hear him—his quick, sure footsteps, the occasional rustle of his coat." | | 4 | "The station’s exit—a narrow door set into the wall, barely visible in the gloom—was ajar." | | 5 | "The station groaned beneath her as she forced it open, the air outside sharp with the scent of damp stone and something else—something electric, like the hum of a circuit just about to spark." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 553 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.0162748643761302 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0018083182640144665 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 38 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 38 | | mean | 14.29 | | std | 8.23 | | cv | 0.576 | | sampleLengths | | 0 | 25 | | 1 | 20 | | 2 | 20 | | 3 | 7 | | 4 | 29 | | 5 | 20 | | 6 | 2 | | 7 | 27 | | 8 | 15 | | 9 | 4 | | 10 | 9 | | 11 | 13 | | 12 | 19 | | 13 | 3 | | 14 | 17 | | 15 | 16 | | 16 | 27 | | 17 | 21 | | 18 | 18 | | 19 | 15 | | 20 | 13 | | 21 | 3 | | 22 | 7 | | 23 | 2 | | 24 | 13 | | 25 | 15 | | 26 | 11 | | 27 | 3 | | 28 | 13 | | 29 | 34 | | 30 | 12 | | 31 | 17 | | 32 | 18 | | 33 | 24 | | 34 | 8 | | 35 | 16 | | 36 | 5 | | 37 | 2 |
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| 39.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.2894736842105263 | | totalSentences | 38 | | uniqueOpeners | 11 | |
| 95.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 35 | | matches | | | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 35 | | matches | | 0 | "She’d been chasing Tomás Herrera" | | 1 | "She had no choice." | | 2 | "She lunged, her coat flaring" | | 3 | "She could hear him—his quick," | | 4 | "She was gaining." | | 5 | "She lunged, her fingers closing" | | 6 | "She had no idea what" |
| | ratio | 0.2 | |
| 17.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 31 | | totalSentences | 35 | | matches | | 0 | "The neon glow of the" | | 1 | "Detective Harlow Quinn’s breath came" | | 2 | "The wind howled through the" | | 3 | "She’d been chasing Tomás Herrera" | | 4 | "The man had vanished into" | | 5 | "Quinn had followed him through" | | 6 | "Quinn moved with military precision," | | 7 | "She had no choice." | | 8 | "The rain hammered against her" | | 9 | "Herrera turned, his warm brown" | | 10 | "Quinn didn’t hesitate." | | 11 | "She lunged, her coat flaring" | | 12 | "The air was thick with" | | 13 | "The station had been sealed" | | 14 | "Quinn’s pulse hammered in her" | | 15 | "Herrera was ahead of her," | | 16 | "Quinn’s boots splashed through puddles," | | 17 | "She could hear him—his quick," | | 18 | "She was gaining." | | 19 | "A low hum filled the" |
| | ratio | 0.886 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 35 | | matches | | | ratio | 0.029 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 2 | | matches | | 0 | "Quinn’s pulse hammered in her throat, her fingers digging into the cold metal of the platform railing as she pushed forward." | | 1 | "Herrera was ahead of her, moving with the quiet efficiency of a man who knew the station’s secrets." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |