| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1491 | | 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) | |
| 53.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1491 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "beacon" | | 1 | "gleaming" | | 2 | "facade" | | 3 | "gloom" | | 4 | "mechanical" | | 5 | "echoed" | | 6 | "scanned" | | 7 | "chaotic" | | 8 | "weight" | | 9 | "velvet" | | 10 | "absolutely" | | 11 | "familiar" | | 12 | "comforting" |
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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 | 101 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 101 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1491 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 1491 | | uniqueNames | 22 | | maxNameDensity | 0.94 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 3 | | Harlow | 14 | | Quinn | 2 | | Metropolitan | 2 | | Police | 2 | | Tomás | 2 | | Herrera | 12 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Seville-born | 1 | | London | 2 | | High | 1 | | Street | 1 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Morris | 3 | | Hendon | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Raven" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Market" | | 9 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Metropolitan" | | 2 | "Soho" | | 3 | "Seville-born" | | 4 | "London" | | 5 | "High" | | 6 | "Street" | | 7 | "Veil" | | 8 | "Hendon" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed entirely disproportionate to a simple foot pursuit" |
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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 | 1491 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 101 | | matches | | |
| 0.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 124.25 | | std | 13.96 | | cv | 0.112 | | sampleLengths | | 0 | 107 | | 1 | 155 | | 2 | 137 | | 3 | 124 | | 4 | 128 | | 5 | 122 | | 6 | 121 | | 7 | 136 | | 8 | 129 | | 9 | 113 | | 10 | 100 | | 11 | 119 |
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| 91.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 101 | | matches | | 0 | "was replaced" | | 1 | "been stripped" | | 2 | "were illuminated" | | 3 | "was hidden" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 11 | | totalVerbs | 236 | | matches | | 0 | "was taking" | | 1 | "was fleeing" | | 2 | "was playing" | | 3 | "was jamming" | | 4 | "was standing" | | 5 | "was weaving" | | 6 | "was not coming" | | 7 | "was stepping" | | 8 | "was holding" | | 9 | "was investigating" | | 10 | "was buying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 101 | | ratio | 0 | | matches | (empty) | |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1504 | | adjectiveStacks | 3 | | stackExamples | | 0 | "narrow, trash-strewn alleyway" | | 1 | "small, lead-lined lockbox." | | 2 | "sulfurous, orchid-scented air" |
| | adverbCount | 32 | | adverbRatio | 0.02127659574468085 | | lyAdverbCount | 19 | | lyAdverbRatio | 0.012632978723404254 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 14.76 | | std | 7.94 | | cv | 0.538 | | sampleLengths | | 0 | 24 | | 1 | 15 | | 2 | 22 | | 3 | 4 | | 4 | 28 | | 5 | 14 | | 6 | 5 | | 7 | 29 | | 8 | 30 | | 9 | 17 | | 10 | 19 | | 11 | 9 | | 12 | 9 | | 13 | 23 | | 14 | 14 | | 15 | 5 | | 16 | 25 | | 17 | 34 | | 18 | 6 | | 19 | 14 | | 20 | 18 | | 21 | 12 | | 22 | 16 | | 23 | 4 | | 24 | 2 | | 25 | 1 | | 26 | 19 | | 27 | 4 | | 28 | 25 | | 29 | 11 | | 30 | 15 | | 31 | 26 | | 32 | 8 | | 33 | 16 | | 34 | 4 | | 35 | 7 | | 36 | 13 | | 37 | 13 | | 38 | 12 | | 39 | 20 | | 40 | 10 | | 41 | 14 | | 42 | 4 | | 43 | 9 | | 44 | 6 | | 45 | 16 | | 46 | 5 | | 47 | 10 | | 48 | 24 | | 49 | 13 |
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| 46.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3564356435643564 | | totalSentences | 101 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 99 | | matches | | 0 | "Instead, he reached into his" | | 1 | "Just a smooth, shallow indentation" | | 2 | "More importantly, the people he" | | 3 | "Then, without looking back at" |
| | ratio | 0.04 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 99 | | matches | | 0 | "She kept her breathing steady," | | 1 | "She had tracked him all" | | 2 | "She knew the place by" | | 3 | "She caught a glimpse of" | | 4 | "His warm brown eyes met" | | 5 | "He was a former paramedic," | | 6 | "He was not a hardened" | | 7 | "He took a sharp left" | | 8 | "She checked the distance." | | 9 | "He sprinted toward a rusted" | | 10 | "She reached for the radio" | | 11 | "She called out her status," | | 12 | "He did not fumble with" | | 13 | "He pressed the bone token" | | 14 | "It did not budge." | | 15 | "She stepped back, chest heaving," | | 16 | "She refused to lose him." | | 17 | "It was a tight squeeze," | | 18 | "She holstered her radio, turned" | | 19 | "She drew her flashlight and" |
| | ratio | 0.333 | |
| 61.01% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 99 | | matches | | 0 | "The rain did not fall" | | 1 | "Detective Harlow Quinn pumped her" | | 2 | "Harlow did not slow." | | 3 | "She kept her breathing steady," | | 4 | "The man was Tomás Herrera." | | 5 | "She had tracked him all" | | 6 | "She knew the place by" | | 7 | "Herrera had been meeting with" | | 8 | "Herrera was twenty-nine and quick," | | 9 | "She caught a glimpse of" | | 10 | "A silver Saint Christopher medallion" | | 11 | "Herrera glanced over his shoulder." | | 12 | "His warm brown eyes met" | | 13 | "He was a former paramedic," | | 14 | "He was not a hardened" | | 15 | "He took a sharp left" | | 16 | "Harlow adjusted her trajectory, her" | | 17 | "Water dripped from her closely" | | 18 | "She checked the distance." | | 19 | "The alley opened into a" |
| | ratio | 0.798 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 99 | | matches | | 0 | "Now, blocks away, the chase" | | 1 | "Even in the gloom, Harlow" | | 2 | "To the right of the" | | 3 | "If she followed him deeper" | | 4 | "To climb out of the" |
| | ratio | 0.051 | |
| 3.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 72 | | technicalSentenceCount | 14 | | matches | | 0 | "The rain did not fall so much as it attacked, driving in cold, horizontal sheets that turned the Camden pavements into slick black mirrors." | | 1 | "Ahead of her, the suspect vaulted over a spilled rubbish bin, his boots skidding before he caught his balance and kept running." | | 2 | "Eighteen years in the Metropolitan Police had wired her for endurance, stripping away the panic that made lesser officers gasp and falter in the heat of a pursu…" | | 3 | "His warm brown eyes met hers for a fraction of a second, wide with a fear that seemed entirely disproportionate to a simple foot pursuit." | | 4 | "He was a former paramedic, a Seville-born transplant who had moved to London to work for the NHS before losing his medical license for administering unauthorize…" | | 5 | "He took a sharp left down a narrow, trash-strewn alleyway that smelled of rotting cabbage and damp brick." | | 6 | "She called out her status, reporting that the suspect was fleeing on foot into a subterranean access point near Camden High Street and requesting immediate back…" | | 7 | "The iconic roundel signs had been stripped from the walls, leaving only rusted brackets and peeling advertisements for shows that had closed decades ago." | | 8 | "Canvas stalls were illuminated by strange, smokeless lanterns that cast a sickly violet glow." | | 9 | "She had heard the whispers in the squad room, the off-the-record jokes about an underground bazaar that sold enchanted goods and banned alchemical substances." | | 10 | "Harlow knew it was something else, something that defied the neat, logical boundaries of police work." | | 11 | "The full moon was high above the city, which meant the market would move again tomorrow night, vanishing to a new location and taking her best lead with it." | | 12 | "Harlow stood up, her posture straightening into that familiar military bearing." | | 13 | "Then, without looking back at the fissure that led to the rain and the safety of the streets above, Detective Harlow Quinn stepped out from behind the kiosk and…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |