| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1182 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "carefully" | | 1 | "cautiously" | | 2 | "completely" |
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| 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) | |
| 45.01% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1182 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "perfect" | | 1 | "quickened" | | 2 | "scanning" | | 3 | "loomed" | | 4 | "footsteps" | | 5 | "echoing" | | 6 | "echo" | | 7 | "electric" | | 8 | "dancing" | | 9 | "echoed" |
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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 | 1 | | narrationSentences | 72 | | matches | | |
| 3.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 5 | | narrationSentences | 72 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "seemed to" | | 2 | "happened to" |
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| 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 | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1173 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.28% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1160 | | uniqueNames | 20 | | maxNameDensity | 1.03 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 12 | | Tomás | 2 | | Herrera | 8 | | Saint | 1 | | Christopher | 1 | | Camden | 3 | | High | 1 | | Street | 1 | | North | 1 | | Morris | 5 | | Market | 1 | | Victorian | 2 | | Control | 1 | | Crawford | 2 | | Latin | 1 | | English | 1 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Market" | | 8 | "Crawford" | | 9 | "Latin" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "North" | | 5 | "Victorian" | | 6 | "Control" |
| | globalScore | 0.983 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like distant voices" |
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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 | 1173 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 60.58% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 53.32 | | std | 19.3 | | cv | 0.362 | | sampleLengths | | 0 | 66 | | 1 | 62 | | 2 | 80 | | 3 | 67 | | 4 | 67 | | 5 | 83 | | 6 | 69 | | 7 | 53 | | 8 | 34 | | 9 | 64 | | 10 | 80 | | 11 | 12 | | 12 | 50 | | 13 | 46 | | 14 | 48 | | 15 | 64 | | 16 | 50 | | 17 | 34 | | 18 | 43 | | 19 | 54 | | 20 | 37 | | 21 | 10 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 72 | | matches | | 0 | "was connected" | | 1 | "was trapped" |
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| 66.67% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 200 | | matches | | 0 | "was chasing" | | 1 | "was speaking" | | 2 | "was coming" | | 3 | "was coming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 72 | | ratio | 0.111 | | matches | | 0 | "Hospital-grade morphine, surgical equipment, rare blood types—the kind of inventory that didn't just walk out of NHS facilities without inside help." | | 1 | "Then she saw it—a flash of movement in the shadows near a boarded-up entrance that shouldn't have existed." | | 2 | "A musty scent drifted up from the opening—old earth and something else she couldn't identify." | | 3 | "This wasn't just a suspect fleeing through an abandoned tunnel—this was something bigger." | | 4 | "Not just earth now—something organic and metallic, like copper pennies left in rich soil." | | 5 | "Not electric light—something older, more primitive." | | 6 | "Footsteps echoed from the tunnel entrance—multiple sets, moving with purpose." | | 7 | "Quinn checked her watch one last time—23:52—and made her choice." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1176 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.023809523809523808 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.012755102040816327 | |
| 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 | 16.29 | | std | 7.79 | | cv | 0.478 | | sampleLengths | | 0 | 24 | | 1 | 22 | | 2 | 20 | | 3 | 15 | | 4 | 25 | | 5 | 22 | | 6 | 25 | | 7 | 21 | | 8 | 21 | | 9 | 1 | | 10 | 12 | | 11 | 15 | | 12 | 25 | | 13 | 10 | | 14 | 17 | | 15 | 15 | | 16 | 7 | | 17 | 14 | | 18 | 15 | | 19 | 16 | | 20 | 18 | | 21 | 25 | | 22 | 15 | | 23 | 7 | | 24 | 18 | | 25 | 8 | | 26 | 13 | | 27 | 20 | | 28 | 15 | | 29 | 13 | | 30 | 4 | | 31 | 2 | | 32 | 16 | | 33 | 16 | | 34 | 15 | | 35 | 13 | | 36 | 8 | | 37 | 13 | | 38 | 18 | | 39 | 15 | | 40 | 5 | | 41 | 26 | | 42 | 41 | | 43 | 39 | | 44 | 12 | | 45 | 24 | | 46 | 12 | | 47 | 14 | | 48 | 16 | | 49 | 20 |
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| 82.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5138888888888888 | | totalSentences | 72 | | uniqueOpeners | 37 | |
| 47.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 70 | | matches | | 0 | "Then she saw it—a flash" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 70 | | matches | | 0 | "His Saint Christopher medallion caught" | | 1 | "She'd been tracking Herrera for" | | 2 | "His olive skin glistened with" | | 3 | "She quickened her pace, her" | | 4 | "She peered through the gap" | | 5 | "She thought of Morris, found" | | 6 | "She sent the message and" | | 7 | "Her footsteps seemed too loud" | | 8 | "He was speaking animatedly with" | | 9 | "She fumbled for her radio," | | 10 | "Her phone showed no signal" | | 11 | "Her hand moved instinctively to" |
| | ratio | 0.171 | |
| 81.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 70 | | matches | | 0 | "The rain hammered London's streets" | | 1 | "Detective Harlow Quinn pressed herself" | | 2 | "Water dripped steadily from the" | | 3 | "His Saint Christopher medallion caught" | | 4 | "Quinn cursed under her breath" | | 5 | "She'd been tracking Herrera for" | | 6 | "Hospital-grade morphine, surgical equipment, rare" | | 7 | "The fact that Herrera had" | | 8 | "The same wrongness that had" | | 9 | "Quinn rounded the corner and" | | 10 | "His olive skin glistened with" | | 11 | "She quickened her pace, her" | | 12 | "A double-decker bus roared past," | | 13 | "Quinn sprinted to where she'd" | | 14 | "Camden Market's entrance loomed to" | | 15 | "The wooden planks that should" | | 16 | "Quinn approached cautiously, her hand" | | 17 | "That she was chasing a" | | 18 | "She peered through the gap" | | 19 | "Darkness stretched beyond, but she" |
| | ratio | 0.757 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 70 | | matches | | 0 | "To her right, a narrow" | | 1 | "If she called for backup" |
| | ratio | 0.029 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 13 | | matches | | 0 | "The rain hammered London's streets with relentless fury, turning the asphalt into a mirror that reflected the neon bleeding from shop windows and streetlights." | | 1 | "Hospital-grade morphine, surgical equipment, rare blood types—the kind of inventory that didn't just walk out of NHS facilities without inside help." | | 2 | "A double-decker bus roared past, its headlights cutting through the downpour and temporarily blinding her." | | 3 | "Then she saw it—a flash of movement in the shadows near a boarded-up entrance that shouldn't have existed." | | 4 | "The wooden planks that should have sealed the opening hung at odd angles, as if they'd been carefully repositioned to appear intact from a distance." | | 5 | "The sound had an odd quality to it, as if the space beyond was far larger than it should be." | | 6 | "But some instinct deeper than training told her that whatever was down there was connected to Morris's death, to the medical thefts, to all the cases that had s…" | | 7 | "The tunnel beyond was wider than she'd expected, its walls lined with old Victorian brickwork that disappeared into shadows beyond her phone's flashlight beam." | | 8 | "Quinn pressed herself against the tunnel wall and crept forward, grateful for the shadows that concealed her movement." | | 9 | "The tunnel opened into a vast circular chamber that shouldn't have existed beneath Camden." | | 10 | "Gas lamps mounted on wrought iron posts cast dancing shadows across stone walls covered in symbols that hurt her eyes to look at directly." | | 11 | "Something told her that her authority as a Metropolitan Police detective would mean nothing in this place that shouldn't exist." | | 12 | "The footsteps grew closer, and she had seconds to decide: retreat toward whatever was coming down the tunnel, or step into the impossible market ahead and confr…" |
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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 | |