| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.294 | | leniency | 0.588 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1288 | | 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) | |
| 49.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1288 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "footsteps" | | 2 | "glint" | | 3 | "silence" | | 4 | "familiar" | | 5 | "flickered" | | 6 | "dancing" | | 7 | "gloom" | | 8 | "chill" | | 9 | "predator" |
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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 | 89 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 89 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1282 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 1054 | | uniqueNames | 11 | | maxNameDensity | 0.95 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 10 | | Herrera | 1 | | Underground | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Veil | 2 | | Market | 2 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Underground" | | 2 | "Camden" | | 3 | "High" | | 4 | "Street" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like crushed glass" |
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| 43.99% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.56 | | wordCount | 1282 | | matches | | 0 | "no sound but" | | 1 | "not toward her, but toward the wooden stalls in the back" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 101 | | matches | | 0 | "knew that much" | | 1 | "faces that looked" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 35.61 | | std | 22.78 | | cv | 0.64 | | sampleLengths | | 0 | 68 | | 1 | 14 | | 2 | 4 | | 3 | 70 | | 4 | 53 | | 5 | 4 | | 6 | 30 | | 7 | 40 | | 8 | 51 | | 9 | 56 | | 10 | 34 | | 11 | 23 | | 12 | 5 | | 13 | 45 | | 14 | 64 | | 15 | 11 | | 16 | 7 | | 17 | 52 | | 18 | 24 | | 19 | 19 | | 20 | 47 | | 21 | 2 | | 22 | 16 | | 23 | 18 | | 24 | 43 | | 25 | 77 | | 26 | 46 | | 27 | 36 | | 28 | 38 | | 29 | 57 | | 30 | 42 | | 31 | 26 | | 32 | 5 | | 33 | 25 | | 34 | 36 | | 35 | 94 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 89 | | matches | | |
| 87.64% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 178 | | matches | | 0 | "was waking" | | 1 | "were watching" | | 2 | "was sharpening" |
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| 1.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 2 | | flaggedSentences | 5 | | totalSentences | 101 | | ratio | 0.05 | | matches | | 0 | "Rain didn’t just fall; it hammered against the pavement of Soho, blurring the neon signs into smeary streaks of pink and green." | | 1 | "And the smell hit her first—a rotting mix of ozone, stale urine, and something metallic, like copper coins left in a damp pocket." | | 2 | "Tables were crowded with bottles of bubbling liquid, pouches of dried herbs that smelled of lavender and necrosis, and strange artifacts—clocks with hands that spun backward, pocket watches that played no sound but a low humming." | | 3 | "Women in veiled cloaks haggled over stolen birth certificates; masked figures traded stashes of something that looked like crushed glass." | | 4 | "He wore a long coat, collar turned up against the damp chill, and a medallion around his neck—a Saint Christopher, bright against the grimy fabric." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1060 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 25 | | adverbRatio | 0.02358490566037736 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0037735849056603774 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 12.69 | | std | 8.88 | | cv | 0.7 | | sampleLengths | | 0 | 22 | | 1 | 22 | | 2 | 4 | | 3 | 2 | | 4 | 18 | | 5 | 14 | | 6 | 4 | | 7 | 19 | | 8 | 14 | | 9 | 3 | | 10 | 21 | | 11 | 13 | | 12 | 21 | | 13 | 5 | | 14 | 1 | | 15 | 1 | | 16 | 25 | | 17 | 4 | | 18 | 4 | | 19 | 14 | | 20 | 12 | | 21 | 20 | | 22 | 10 | | 23 | 10 | | 24 | 7 | | 25 | 7 | | 26 | 10 | | 27 | 5 | | 28 | 2 | | 29 | 14 | | 30 | 6 | | 31 | 12 | | 32 | 2 | | 33 | 12 | | 34 | 7 | | 35 | 23 | | 36 | 6 | | 37 | 13 | | 38 | 15 | | 39 | 9 | | 40 | 11 | | 41 | 3 | | 42 | 5 | | 43 | 25 | | 44 | 17 | | 45 | 3 | | 46 | 8 | | 47 | 36 | | 48 | 20 | | 49 | 5 |
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| 55.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.4158415841584158 | | totalSentences | 101 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 84 | | matches | | 0 | "Just a hatch in the" | | 1 | "More backup meant more red" | | 2 | "Then he moved, not toward" |
| | ratio | 0.036 | |
| 91.43% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 84 | | matches | | 0 | "She checked the time." | | 1 | "He turned the corner two" | | 2 | "She’d spent fifteen years in" | | 3 | "She vaulted the low brick" | | 4 | "She caught sight of him" | | 5 | "She pushed off the dumpster," | | 6 | "Her breath came in short," | | 7 | "She peered around the corner." | | 8 | "It sat open, swallowing the" | | 9 | "It was an access point" | | 10 | "She holstered her weapon and" | | 11 | "She was alone." | | 12 | "It was a patchwork of" | | 13 | "He wore a long coat," | | 14 | "He smiled, revealing teeth too" | | 15 | "She didn't recognize him, but" | | 16 | "He pushed off the pole," | | 17 | "He took a step closer," | | 18 | "He pointed a gloved finger" | | 19 | "She could pull her weapon" |
| | ratio | 0.321 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 84 | | matches | | 0 | "Rain didn’t just fall; it" | | 1 | "Harlow Quinn adjusted the grip" | | 2 | "She checked the time." | | 3 | "A shout from the night" | | 4 | "He turned the corner two" | | 5 | "Quinn didn't slip." | | 6 | "She’d spent fifteen years in" | | 7 | "She vaulted the low brick" | | 8 | "She caught sight of him" | | 9 | "The air here was different." | | 10 | "The rain vanished almost instantly" | | 11 | "The footsteps didn't stop." | | 12 | "A fraction of a second" | | 13 | "A heavy-duty bolt cutter flew" | | 14 | "Quinn dove behind a rusted" | | 15 | "The sound of retreating steps" | | 16 | "She pushed off the dumpster," | | 17 | "Her breath came in short," | | 18 | "The adrenaline spiked, washing away" | | 19 | "She peered around the corner." |
| | ratio | 0.833 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 84 | | matches | | 0 | "To go up now was" | | 1 | "To go forward was to" |
| | ratio | 0.024 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 1 | | matches | | 0 | "Tables were crowded with bottles of bubbling liquid, pouches of dried herbs that smelled of lavender and necrosis, and strange artifacts—clocks with hands that …" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn said, her voice steady despite the noise of the bazaar" |
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| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn corrected (correct)" |
| | dialogueSentences | 17 | | tagDensity | 0.118 | | leniency | 0.235 | | rawRatio | 0.5 | | effectiveRatio | 0.118 | |