| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 39 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1243 | | 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) | |
| 43.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1243 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "weight" | | 1 | "flickered" | | 2 | "pulsed" | | 3 | "silence" | | 4 | "pulse" | | 5 | "familiar" | | 6 | "predator" | | 7 | "silk" | | 8 | "tension" | | 9 | "flicked" | | 10 | "unspoken" |
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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 | 138 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 138 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 164 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 45 | | markdownWords | 207 | | totalWords | 1233 | | ratio | 0.168 | | matches | | 0 | "Silas Vex" | | 1 | "\"You’re not supposed to be here.\"" | | 2 | "\"The market’s closed to outsiders,\"" | | 3 | "\"Unless you’ve got a bone.\"" | | 4 | "her" | | 5 | "\"Bone token,\"" | | 6 | "\"Name’s Quinn. Detective Quinn. And I’m looking for Silas Vex.\"" | | 7 | "\"Silas doesn’t take kindly to visitors.\"" | | 8 | "\"Then I’ll make sure he does.\"" | | 9 | "\"You’re either very brave or very stupid,\"" | | 10 | "\"Or both.\"" | | 11 | "this" | | 12 | "\"I’m not here to play games,\"" | | 13 | "\"I’m here to end one.\"" | | 14 | "guard" | | 15 | "\"Detective Quinn,\"" | | 16 | "\"I was beginning to think you’d never find me.\"" | | 17 | "\"You’re wanted for three murders. Unofficial, but still.\"" | | 18 | "\"Murders? How quaint. I prefer to call them… *elevations" | | 19 | "\"Easy, Detective.\"" | | 20 | "\"Tomás?\"" | | 21 | "\"What the hell are you doing here?\"" | | 22 | "\"Healing,\"" | | 23 | "\"They’ve been… *unwell" | | 24 | "\"And you, Tomás, are always so *helpful" | | 25 | "\"I’m not here for you, Silas.\"" | | 26 | "\"No?\"" | | 27 | "\"Then why are you here at all?\"" | | 28 | "\"You’re bleeding.\"" | | 29 | "\"Shit,\"" | | 30 | "\"Let’s get you out of here before you lose any more blood.\"" | | 31 | "smiling" | | 32 | "\"Not yet,\"" | | 33 | "\"I’m not leaving until I know what you’re selling.\"" | | 34 | "\"Oh, Detective. You have no idea what you’re asking for.\"" | | 35 | "thunk" | | 36 | "\"Tomás!\"" | | 37 | "\"You’re lucky,\"" | | 38 | "\"That was a *bad" | | 39 | "\"What the hell is happening here?\"" | | 40 | "\"The market,\"" | | 41 | "\"It’s not just a place. It’s a *thing" | | 42 | "\"Enough!\"" | | 43 | "\"We need to move. Now.\"" | | 44 | "\"Tell your friend to watch his back, Detective. The Veil doesn’t forgive.\"" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1029 | | uniqueNames | 16 | | maxNameDensity | 1.26 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Dean | 1 | | Street | 1 | | Tube | 1 | | English | 1 | | Quinn | 13 | | Veil | 1 | | Market | 1 | | London | 1 | | Vex | 9 | | Tomás | 7 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Silas | 4 |
| | persons | | 0 | "Raven" | | 1 | "Quinn" | | 2 | "Vex" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Silas" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "London" |
| | globalScore | 0.868 | | windowScore | 0.667 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 3 | | matches | | 0 | "something like old books and damp parchment" | | 1 | "not quite" | | 2 | "smelled like ozone and something rotten, l" |
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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 | 1233 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 164 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 18.68 | | std | 17.33 | | cv | 0.928 | | sampleLengths | | 0 | 1 | | 1 | 66 | | 2 | 60 | | 3 | 51 | | 4 | 58 | | 5 | 9 | | 6 | 6 | | 7 | 44 | | 8 | 15 | | 9 | 42 | | 10 | 49 | | 11 | 15 | | 12 | 36 | | 13 | 6 | | 14 | 3 | | 15 | 14 | | 16 | 32 | | 17 | 15 | | 18 | 9 | | 19 | 1 | | 20 | 49 | | 21 | 70 | | 22 | 12 | | 23 | 19 | | 24 | 13 | | 25 | 27 | | 26 | 11 | | 27 | 2 | | 28 | 42 | | 29 | 12 | | 30 | 10 | | 31 | 12 | | 32 | 10 | | 33 | 11 | | 34 | 14 | | 35 | 19 | | 36 | 3 | | 37 | 24 | | 38 | 22 | | 39 | 15 | | 40 | 18 | | 41 | 1 | | 42 | 8 | | 43 | 5 | | 44 | 38 | | 45 | 1 | | 46 | 30 | | 47 | 11 | | 48 | 13 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 138 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 178 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 164 | | ratio | 0.067 | | matches | | 0 | "The alleyway stank of wet concrete and something sharper—copper and burnt wiring." | | 1 | "The suspect’s scent clung to the air: cheap cologne, the metallic tang of adrenaline, and something else—something like old books and damp parchment." | | 2 | "Just a name—*Silas Vex*—and a habit of vanishing after midnight." | | 3 | "The digital display flickered—11:47." | | 4 | "The figure stepped from the shadows—a man in a long, oil-stained coat, his face obscured by a wide-brimmed hat." | | 5 | "She’d heard whispers of it—supernatural dealers, enchanted goods, transactions that left buyers with more questions than answers." | | 6 | "It clattered against something metallic—a table, maybe." | | 7 | "The man—*guard*, she realised now—had already vanished into the crowd of figures that had materialised from the darkness." | | 8 | "She whirled, pistol up—" | | 9 | "The sound of tearing fabric, the wet *thunk* of flesh hitting bone—" | | 10 | "And then—" |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1042 | | adjectiveStacks | 1 | | stackExamples | | 0 | "long, oil-stained coat," |
| | adverbCount | 31 | | adverbRatio | 0.029750479846449136 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0028790786948176585 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 164 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 164 | | mean | 7.5 | | std | 4.68 | | cv | 0.624 | | sampleLengths | | 0 | 12 | | 1 | 16 | | 2 | 4 | | 3 | 2 | | 4 | 23 | | 5 | 9 | | 6 | 11 | | 7 | 12 | | 8 | 4 | | 9 | 5 | | 10 | 11 | | 11 | 5 | | 12 | 2 | | 13 | 10 | | 14 | 14 | | 15 | 15 | | 16 | 4 | | 17 | 16 | | 18 | 2 | | 19 | 15 | | 20 | 16 | | 21 | 3 | | 22 | 4 | | 23 | 4 | | 24 | 16 | | 25 | 9 | | 26 | 6 | | 27 | 12 | | 28 | 19 | | 29 | 4 | | 30 | 2 | | 31 | 7 | | 32 | 10 | | 33 | 5 | | 34 | 3 | | 35 | 3 | | 36 | 17 | | 37 | 4 | | 38 | 4 | | 39 | 11 | | 40 | 3 | | 41 | 12 | | 42 | 2 | | 43 | 6 | | 44 | 10 | | 45 | 6 | | 46 | 10 | | 47 | 4 | | 48 | 3 | | 49 | 2 |
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| 43.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.31097560975609756 | | totalSentences | 164 | | uniqueOpeners | 51 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 123 | | matches | | 0 | "Just a name—*Silas Vex*—and a" | | 1 | "Instead, she stepped forward, boots" | | 2 | "Instead, his gaze flicked to" |
| | ratio | 0.024 | |
| 89.92% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 123 | | matches | | 0 | "She didn’t look back." | | 1 | "She could turn back." | | 2 | "She could call for backup." | | 3 | "Her left wrist ached where" | | 4 | "She adjusted it with a" | | 5 | "She’d lost him at the" | | 6 | "She spun, hand already reaching" | | 7 | "His voice wasn’t English." | | 8 | "he said, tilting his head" | | 9 | "She’d heard whispers of it—supernatural" | | 10 | "She didn’t answer." | | 11 | "She picked it up, the" | | 12 | "she said, voice steady" | | 13 | "She tossed the bone into" | | 14 | "It clattered against something metallic—a" | | 15 | "She didn’t turn." | | 16 | "She knew the sound of" | | 17 | "she said, voice low" | | 18 | "He wasn’t alone." | | 19 | "She didn’t lower her gun." |
| | ratio | 0.325 | |
| 37.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 123 | | matches | | 0 | "The alleyway stank of wet" | | 1 | "Quinn’s boots splashed through a" | | 2 | "She didn’t look back." | | 3 | "The suspect’s scent clung to" | | 4 | "The scent of the Raven’s" | | 5 | "The iron steps groaned under" | | 6 | "She could turn back." | | 7 | "She could call for backup." | | 8 | "Her left wrist ached where" | | 9 | "She adjusted it with a" | | 10 | "The digital display flickered—11:47." | | 11 | "She’d lost him at the" | | 12 | "The staircase opened into a" | | 13 | "The walls pulsed with a" | | 14 | "Quinn’s breath hitched." | | 15 | "This wasn’t the Tube." | | 16 | "The air smelled like ozone" | | 17 | "A voice, low and guttural," | | 18 | "She spun, hand already reaching" | | 19 | "The figure stepped from the" |
| | ratio | 0.846 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 1 | | matches | | 0 | "A man in a suit that was clearly too small for him adjusted his cufflinks, his eyes reflecting the neon glow like a predator’s." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 6 | | matches | | 0 | "she said, voice steady" | | 1 | "she said, voice low" | | 2 | "Vex said, his voice smooth as aged whiskey" | | 3 | "she said, voice firm" | | 4 | "he said, voice tight" | | 5 | "he said, voice low" |
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| 98.72% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 2 | | fancyTags | | 0 | "Vex said (vex say)" | | 1 | "she muttered (mutter)" |
| | dialogueSentences | 39 | | tagDensity | 0.256 | | leniency | 0.513 | | rawRatio | 0.2 | | effectiveRatio | 0.103 | |