| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 980 | | 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) | |
| 33.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 980 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "pulse" | | 1 | "tension" | | 2 | "flickered" | | 3 | "glinting" | | 4 | "stomach" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "wavered" | | 8 | "velvet" | | 9 | "flicker" | | 10 | "silence" |
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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 | 80 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | 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 | 16 | | markdownWords | 27 | | totalWords | 971 | | ratio | 0.028 | | matches | | 0 | "Tomás Herrera" | | 1 | "The Raven’s Nest" | | 2 | "Morris." | | 3 | "Raven’s Nest" | | 4 | "Got you." | | 5 | "Camden Town" | | 6 | "No." | | 7 | "wrong" | | 8 | "token." | | 9 | "Damn it." | | 10 | "The Veil Market." | | 11 | "Drugs." | | 12 | "rich." | | 13 | "detective" | | 14 | "dangerous" | | 15 | "god, his eyes" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.76% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 889 | | uniqueNames | 9 | | maxNameDensity | 1.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 10 | | Raven | 2 | | Dean | 1 | | Street | 1 | | Herrera | 9 | | Tube | 1 | | Veil | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" |
| | places | | | globalScore | 0.938 | | windowScore | 1 | |
| 55.66% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 2 | | matches | | 0 | "quite match his movements" | | 1 | "something like regret flicker across his fac" |
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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 | 971 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 27.74 | | std | 26.45 | | cv | 0.953 | | sampleLengths | | 0 | 116 | | 1 | 1 | | 2 | 15 | | 3 | 82 | | 4 | 18 | | 5 | 53 | | 6 | 5 | | 7 | 56 | | 8 | 60 | | 9 | 2 | | 10 | 46 | | 11 | 9 | | 12 | 8 | | 13 | 2 | | 14 | 70 | | 15 | 3 | | 16 | 38 | | 17 | 8 | | 18 | 38 | | 19 | 58 | | 20 | 40 | | 21 | 9 | | 22 | 14 | | 23 | 6 | | 24 | 42 | | 25 | 18 | | 26 | 30 | | 27 | 12 | | 28 | 12 | | 29 | 26 | | 30 | 15 | | 31 | 7 | | 32 | 5 | | 33 | 37 | | 34 | 10 |
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| 92.11% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 80 | | matches | | 0 | "were rusted" | | 1 | "been closed" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 155 | | matches | | 0 | "was standing" | | 1 | "was going" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 89 | | ratio | 0.124 | | matches | | 0 | "The suspect—*Tomás Herrera*—had bolted the second he’d spotted her outside *The Raven’s Nest*, his dark curls plastered to his forehead as he’d ducked into the alley like a rat into a sewer." | | 1 | "Eighteen years on the force had honed her instincts, and she could read the city like a map—knew which alleys dead-ended, which fire escapes were rusted through, which shadows could hide a man." | | 2 | "The sign above—*Camden Town*—was half-peeled, the letters flaking like old skin." | | 3 | "The platform was a graveyard of forgotten things—peeling posters, rusted turnstiles, the skeletal remains of a long-dead ticket booth." | | 4 | "Bone-white, carved with a symbol she didn’t recognize—a crescent moon cradling a serpent." | | 5 | "Stalls lined the walls, their wares illuminated by flickering lanterns—jars of swirling liquid, bundles of dried herbs, weapons that hummed with an unnatural energy." | | 6 | "She’d heard whispers of it—rumors, half-truths, the kind of stories that got you laughed out of the station if you repeated them." | | 7 | "The market’s patrons were a motley bunch—pale figures with too many teeth, a woman with eyes like polished obsidian, a man whose shadow didn’t quite match his movements." | | 8 | "The vendor—a gaunt man with fingers too long, too many joints—leaned in close as Herrera slid something across the counter." | | 9 | "Then, from the shadows behind the stall, a voice—smooth, amused, *dangerous*—spoke." | | 10 | "He wore a long coat, the fabric shifting like smoke, and his eyes—*god, his eyes*—were the color of a storm at midnight." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 906 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.024282560706401765 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004415011037527594 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 10.91 | | std | 7.95 | | cv | 0.729 | | sampleLengths | | 0 | 14 | | 1 | 29 | | 2 | 32 | | 3 | 32 | | 4 | 9 | | 5 | 1 | | 6 | 11 | | 7 | 4 | | 8 | 7 | | 9 | 33 | | 10 | 21 | | 11 | 21 | | 12 | 13 | | 13 | 3 | | 14 | 2 | | 15 | 26 | | 16 | 11 | | 17 | 3 | | 18 | 8 | | 19 | 5 | | 20 | 5 | | 21 | 3 | | 22 | 19 | | 23 | 10 | | 24 | 15 | | 25 | 9 | | 26 | 19 | | 27 | 19 | | 28 | 22 | | 29 | 2 | | 30 | 8 | | 31 | 13 | | 32 | 3 | | 33 | 22 | | 34 | 9 | | 35 | 6 | | 36 | 2 | | 37 | 2 | | 38 | 9 | | 39 | 12 | | 40 | 15 | | 41 | 24 | | 42 | 10 | | 43 | 3 | | 44 | 3 | | 45 | 22 | | 46 | 13 | | 47 | 8 | | 48 | 18 | | 49 | 10 |
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| 57.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3707865168539326 | | totalSentences | 89 | | uniqueOpeners | 33 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 72 | | matches | | 0 | "Then, without warning, the tunnel" | | 1 | "Then, slowly, he turned." | | 2 | "Then, from the shadows behind" |
| | ratio | 0.042 | |
| 97.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 72 | | matches | | 0 | "She’d been tailing him for" | | 1 | "She cut left at the" | | 2 | "She rounded the bend just" | | 3 | "Her pulse spiked." | | 4 | "She yanked a flashlight from" | | 5 | "She stepped inside, her boots" | | 6 | "Her flashlight flickered as she" | | 7 | "She crouched, picking it up" | | 8 | "Her stomach twisted." | | 9 | "She’d seen enough in her" | | 10 | "She’d heard whispers of it—rumors," | | 11 | "He moved with purpose, his" | | 12 | "She needed to see where" | | 13 | "She slipped into the crowd," | | 14 | "She’d faced dangerous men before," | | 15 | "She stepped forward, her hand" | | 16 | "His warm brown eyes locked" | | 17 | "he said, his voice low" | | 18 | "she shot back, her finger" | | 19 | "she said, her voice steel" |
| | ratio | 0.306 | |
| 64.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 72 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The suspect—*Tomás Herrera*—had bolted the" | | 3 | "She’d been tailing him for" | | 4 | "Cases like the one that" | | 5 | "The thought was a knife" | | 6 | "Herrera was fast, but she" | | 7 | "She cut left at the" | | 8 | "The neon glow of the" | | 9 | "Quinn grinned, feral." | | 10 | "She rounded the bend just" | | 11 | "The sign above—*Camden Town*—was half-peeled," | | 12 | "Her pulse spiked." | | 13 | "Quinn didn’t hesitate." | | 14 | "She yanked a flashlight from" | | 15 | "The nails groaned, then gave" | | 16 | "The stench of damp concrete" | | 17 | "She stepped inside, her boots" | | 18 | "The platform was a graveyard" | | 19 | "Her flashlight flickered as she" |
| | ratio | 0.792 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 84.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 3 | | matches | | 0 | "She’d been tailing him for weeks, ever since his name had surfaced in connection with the clique, that tight-knit group of misfits who kept popping up in cases …" | | 1 | "Stalls lined the walls, their wares illuminated by flickering lanterns—jars of swirling liquid, bundles of dried herbs, weapons that hummed with an unnatural en…" | | 2 | "She’d heard whispers of it—rumors, half-truths, the kind of stories that got you laughed out of the station if you repeated them." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 3 | | matches | | 0 | "he said, his voice low" | | 1 | "she shot back, her finger hovering over the trigger" | | 2 | "he said, smiling" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0 | | effectiveRatio | 0 | |