| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 719 | | totalAiIsmAdverbs | 2 | | 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) | |
| 51.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 719 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "scanning" | | 2 | "echoed" | | 3 | "footsteps" | | 4 | "flicked" | | 5 | "tension" | | 6 | "could feel" |
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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 | 63 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | (empty) | |
| 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 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 712 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 87.01% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 635 | | uniqueNames | 14 | | maxNameDensity | 1.26 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 8 | | Raven | 1 | | Nest | 1 | | Met | 1 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 | | Tomás | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Met" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Tomás" |
| | places | | | globalScore | 0.87 | | windowScore | 1 | |
| 33.72% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed endless before her boots hit solid ground" | | 1 | "something like this—something she couldn’t e" |
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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 | 712 | | 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 | 29 | | mean | 24.55 | | std | 16.85 | | cv | 0.686 | | sampleLengths | | 0 | 63 | | 1 | 49 | | 2 | 29 | | 3 | 42 | | 4 | 36 | | 5 | 3 | | 6 | 41 | | 7 | 44 | | 8 | 10 | | 9 | 51 | | 10 | 31 | | 11 | 9 | | 12 | 48 | | 13 | 5 | | 14 | 30 | | 15 | 8 | | 16 | 8 | | 17 | 7 | | 18 | 3 | | 19 | 25 | | 20 | 22 | | 21 | 23 | | 22 | 21 | | 23 | 23 | | 24 | 3 | | 25 | 38 | | 26 | 18 | | 27 | 15 | | 28 | 7 |
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| 99.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 63 | | matches | | |
| 79.88% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 111 | | matches | | 0 | "was still running" | | 1 | "were breathing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 72 | | ratio | 0.097 | | matches | | 0 | "She kept her eyes locked on the figure darting between the shadows—a lanky silhouette in a dark hoodie, moving with the desperation of a man who knew the game was up." | | 1 | "No doors, no side streets—just a dead end." | | 2 | "The Met didn’t train for this—chasing a suspect into the bowels of the city, where the rules above ground didn’t apply." | | 3 | "Then she saw it—the entrance to The Veil Market." | | 4 | "A figure stepped into her path—a man with olive skin and a scar running the length of his forearm." | | 5 | "But the way he stood, the tension in his shoulders—he wasn’t just a bystander." | | 6 | "She’d lost Morris to something like this—something she couldn’t explain." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 644 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.027950310559006212 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007763975155279503 | |
| 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 | 9.89 | | std | 6.26 | | cv | 0.633 | | sampleLengths | | 0 | 15 | | 1 | 17 | | 2 | 31 | | 3 | 13 | | 4 | 15 | | 5 | 3 | | 6 | 18 | | 7 | 10 | | 8 | 4 | | 9 | 15 | | 10 | 15 | | 11 | 27 | | 12 | 7 | | 13 | 8 | | 14 | 12 | | 15 | 9 | | 16 | 3 | | 17 | 7 | | 18 | 8 | | 19 | 11 | | 20 | 15 | | 21 | 7 | | 22 | 14 | | 23 | 2 | | 24 | 21 | | 25 | 6 | | 26 | 4 | | 27 | 7 | | 28 | 10 | | 29 | 10 | | 30 | 13 | | 31 | 11 | | 32 | 1 | | 33 | 10 | | 34 | 3 | | 35 | 17 | | 36 | 9 | | 37 | 15 | | 38 | 17 | | 39 | 16 | | 40 | 4 | | 41 | 1 | | 42 | 19 | | 43 | 2 | | 44 | 9 | | 45 | 8 | | 46 | 4 | | 47 | 4 | | 48 | 3 | | 49 | 4 |
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| 47.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3194444444444444 | | totalSentences | 72 | | uniqueOpeners | 23 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 59 | | matches | | 0 | "Then he vanished." | | 1 | "Then she saw it: a" | | 2 | "Then she saw it—the entrance" |
| | ratio | 0.051 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 59 | | matches | | 0 | "She kept her eyes locked" | | 1 | "He ducked left, weaving through" | | 2 | "She cut through the throng," | | 3 | "He bolted right, towards a" | | 4 | "He was fast, but she" | | 5 | "She slowed, scanning the empty" | | 6 | "He was still running." | | 7 | "She gripped the edge, lowering" | | 8 | "She moved, her bearing all" | | 9 | "She took the left fork," | | 10 | "Her suspect was here." | | 11 | "His Saint Christopher medallion glinted" | | 12 | "His voice was calm, but" | | 13 | "She could push past him." | | 14 | "She’d lost Morris to something" | | 15 | "He stepped aside." | | 16 | "She could feel it." |
| | ratio | 0.288 | |
| 19.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 59 | | matches | | 0 | "The rain hammered the pavement," | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "She kept her eyes locked" | | 3 | "He ducked left, weaving through" | | 4 | "The green neon sign flickered" | | 5 | "Quinn didn’t slow." | | 6 | "She cut through the throng," | | 7 | "The suspect glanced back, his" | | 8 | "Fear widened his eyes." | | 9 | "He bolted right, towards a" | | 10 | "Quinn followed, her leather watch" | | 11 | "The alley spat them out" | | 12 | "He was fast, but she" | | 13 | "Years of chasing ghosts had" | | 14 | "The suspect skidded around a" | | 15 | "Quinn took the turn tighter," | | 16 | "She slowed, scanning the empty" | | 17 | "A rusted fire escape sagged" | | 18 | "Quinn crouched, fingers brushing the" | | 19 | "The scent of damp earth" |
| | ratio | 0.881 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "She kept her eyes locked on the figure darting between the shadows—a lanky silhouette in a dark hoodie, moving with the desperation of a man who knew the game w…" | | 1 | "The market stretched before her, a labyrinth of stalls selling things that defied logic." | | 2 | "Jars of glowing liquid, weapons that hummed with energy, eyes that followed her from the dark." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |