| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 655 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 38.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 655 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "scanning" | | 2 | "pulse" | | 3 | "glinting" | | 4 | "flicked" | | 5 | "silence" | | 6 | "pulsed" | | 7 | "pounding" |
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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 | 62 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 62 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 644 | | ratio | 0.005 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.76% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 581 | | uniqueNames | 11 | | maxNameDensity | 1.2 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Quinn | 7 | | Raven | 1 | | Tomás | 3 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Market" | | 7 | "Morris" |
| | places | | | globalScore | 0.898 | | windowScore | 1 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like human teeth blocked her path" |
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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 | 644 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 23 | | std | 14.36 | | cv | 0.624 | | sampleLengths | | 0 | 53 | | 1 | 20 | | 2 | 43 | | 3 | 49 | | 4 | 23 | | 5 | 18 | | 6 | 13 | | 7 | 39 | | 8 | 3 | | 9 | 8 | | 10 | 5 | | 11 | 32 | | 12 | 17 | | 13 | 38 | | 14 | 40 | | 15 | 33 | | 16 | 5 | | 17 | 13 | | 18 | 3 | | 19 | 11 | | 20 | 19 | | 21 | 42 | | 22 | 34 | | 23 | 22 | | 24 | 18 | | 25 | 16 | | 26 | 15 | | 27 | 12 |
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| 99.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 62 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 107 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 72 | | ratio | 0.111 | | matches | | 0 | "The suspect—lean, fast, and now ducking into a narrow alley—had the look of someone who knew these backstreets better than she did." | | 1 | "*The Raven’s Nest.* She’d heard whispers about this place—off-duty cops, shady deals, things that didn’t add up." | | 2 | "A man behind the counter—tall, gaunt, with a face like weathered stone—watched her with cold eyes." | | 3 | "A figure stepped out—Tomás Herrera, his Saint Christopher medallion glinting under the low light." | | 4 | "The air grew thick, humid, laced with the scent of damp earth and something sharper—metal, maybe, or ozone." | | 5 | "Stalls stretched in every direction, their wares glowing under dim, eerie light—jars of swirling liquid, bundles of herbs that pulsed faintly, bones carved with strange symbols." | | 6 | "She spun, ready to strike—" | | 7 | "Figures emerged from the shadows—tall, gaunt, their movements too smooth, too precise." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 592 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 18 | | adverbRatio | 0.030405405405405407 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.005067567567567568 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 8.94 | | std | 5.27 | | cv | 0.589 | | sampleLengths | | 0 | 15 | | 1 | 16 | | 2 | 22 | | 3 | 3 | | 4 | 15 | | 5 | 2 | | 6 | 22 | | 7 | 17 | | 8 | 4 | | 9 | 10 | | 10 | 21 | | 11 | 13 | | 12 | 5 | | 13 | 16 | | 14 | 7 | | 15 | 6 | | 16 | 9 | | 17 | 3 | | 18 | 4 | | 19 | 9 | | 20 | 8 | | 21 | 14 | | 22 | 13 | | 23 | 4 | | 24 | 3 | | 25 | 3 | | 26 | 5 | | 27 | 5 | | 28 | 4 | | 29 | 7 | | 30 | 10 | | 31 | 11 | | 32 | 3 | | 33 | 14 | | 34 | 18 | | 35 | 12 | | 36 | 8 | | 37 | 11 | | 38 | 26 | | 39 | 3 | | 40 | 17 | | 41 | 10 | | 42 | 6 | | 43 | 5 | | 44 | 6 | | 45 | 7 | | 46 | 3 | | 47 | 4 | | 48 | 7 | | 49 | 8 |
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| 42.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3055555555555556 | | totalSentences | 72 | | uniqueOpeners | 22 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 61 | | matches | | 0 | "Then he turned and melted" | | 1 | "Then the tunnel opened into" |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 61 | | matches | | 0 | "Her radio crackled." | | 1 | "She ignored him, scanning the" | | 2 | "Her pulse kicked up." | | 3 | "She moved toward it, fingers" | | 4 | "His warm brown eyes flicked" | | 5 | "He didn’t budge." | | 6 | "She slipped past him, descending" | | 7 | "She emerged into a cavernous" | | 8 | "Her suspect stood near a" | | 9 | "She spun, ready to strike—" | | 10 | "He shook his head." | | 11 | "She vaulted over the counter," | | 12 | "Their eyes gleamed in the" | | 13 | "She’d seen things like this" | | 14 | "Her hand went to her" |
| | ratio | 0.246 | |
| 9.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 61 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn’s boots splashed through" | | 2 | "The suspect—lean, fast, and now" | | 3 | "Her radio crackled." | | 4 | "Dispatch’s voice cut through the" | | 5 | "The alley twisted, then spilled" | | 6 | "*The Raven’s Nest.* She’d heard" | | 7 | "The suspect vanished inside." | | 8 | "Quinn slowed, her hand brushing" | | 9 | "The door groaned as she" | | 10 | "The bar was half-empty, a" | | 11 | "A man behind the counter—tall," | | 12 | "She ignored him, scanning the" | | 13 | "A bookshelf against the far" | | 14 | "The hidden room." | | 15 | "Her pulse kicked up." | | 16 | "She moved toward it, fingers" | | 17 | "The bookshelf swung open before" | | 18 | "A figure stepped out—Tomás Herrera," | | 19 | "His warm brown eyes flicked" |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "The suspect—lean, fast, and now ducking into a narrow alley—had the look of someone who knew these backstreets better than she did." | | 1 | "Stalls stretched in every direction, their wares glowing under dim, eerie light—jars of swirling liquid, bundles of herbs that pulsed faintly, bones carved with…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |