| 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 | |
| 85.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1336 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "slightly" | | 1 | "deliberately" | | 2 | "completely" |
| |
| 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) | |
| 47.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1336 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | | 2 | | word | "practiced ease" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | |
| | highlights | | 0 | "jaw clenched" | | 1 | "loomed" | | 2 | "practiced ease" | | 3 | "methodical" | | 4 | "footsteps" | | 5 | "echoing" | | 6 | "silence" | | 7 | "whisper" | | 8 | "structure" | | 9 | "vibrated" | | 10 | "weight" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 139 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 139 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1331 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1328 | | uniqueNames | 21 | | maxNameDensity | 0.9 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | High | 1 | | Street | 1 | | Harlow | 2 | | Quinn | 12 | | London | 1 | | Metropolitan | 3 | | Police | 2 | | Raven | 1 | | Nest | 1 | | Turkish | 1 | | Town | 1 | | Station | 1 | | Victorian | 1 | | Underground | 4 | | James | 2 | | Morris | 5 | | Academy | 1 | | Veil | 1 | | Market | 2 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Raven" | | 4 | "James" | | 5 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "London" | | 4 | "Town" | | 5 | "Station" | | 6 | "Underground" | | 7 | "Veil" | | 8 | "Market" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like a thousand conversations happ" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.751 | | wordCount | 1331 | | matches | | |
| 94.72% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 139 | | matches | | 0 | "screamed that this" | | 1 | "left that way" | | 2 | "knew that much" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 38.03 | | std | 19.87 | | cv | 0.522 | | sampleLengths | | 0 | 55 | | 1 | 33 | | 2 | 76 | | 3 | 52 | | 4 | 50 | | 5 | 43 | | 6 | 40 | | 7 | 45 | | 8 | 46 | | 9 | 39 | | 10 | 35 | | 11 | 10 | | 12 | 54 | | 13 | 76 | | 14 | 26 | | 15 | 36 | | 16 | 26 | | 17 | 40 | | 18 | 61 | | 19 | 4 | | 20 | 52 | | 21 | 8 | | 22 | 45 | | 23 | 8 | | 24 | 64 | | 25 | 43 | | 26 | 26 | | 27 | 68 | | 28 | 10 | | 29 | 26 | | 30 | 40 | | 31 | 15 | | 32 | 57 | | 33 | 15 | | 34 | 7 |
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| 97.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 139 | | matches | | 0 | "were sold" | | 1 | "been sealed" | | 2 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 221 | | matches | | 0 | "was heading" | | 1 | "was purchasing" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 139 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1333 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.02175543885971493 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.007501875468867217 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 9.58 | | std | 6.87 | | cv | 0.717 | | sampleLengths | | 0 | 16 | | 1 | 18 | | 2 | 21 | | 3 | 7 | | 4 | 16 | | 5 | 4 | | 6 | 3 | | 7 | 3 | | 8 | 14 | | 9 | 16 | | 10 | 26 | | 11 | 4 | | 12 | 3 | | 13 | 13 | | 14 | 15 | | 15 | 9 | | 16 | 9 | | 17 | 19 | | 18 | 4 | | 19 | 24 | | 20 | 3 | | 21 | 10 | | 22 | 9 | | 23 | 16 | | 24 | 12 | | 25 | 5 | | 26 | 5 | | 27 | 5 | | 28 | 6 | | 29 | 16 | | 30 | 18 | | 31 | 8 | | 32 | 2 | | 33 | 1 | | 34 | 27 | | 35 | 4 | | 36 | 3 | | 37 | 19 | | 38 | 5 | | 39 | 22 | | 40 | 6 | | 41 | 14 | | 42 | 11 | | 43 | 2 | | 44 | 3 | | 45 | 3 | | 46 | 35 | | 47 | 10 | | 48 | 6 | | 49 | 2 |
| |
| 64.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.45323741007194246 | | totalSentences | 139 | | uniqueOpeners | 63 | |
| 26.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 124 | | matches | | 0 | "Instead of heading toward the" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 124 | | matches | | 0 | "She'd been tailing him for" | | 1 | "He wore an expensive black" | | 2 | "He cut left down an" | | 3 | "Their eyes met for a" | | 4 | "She was faster at distance" | | 5 | "He vaulted over a low" | | 6 | "She'd dislocated it twice before." | | 7 | "She was nearly on him" | | 8 | "He knew this route." | | 9 | "He'd planned this." | | 10 | "She followed him through the" | | 11 | "He wouldn't wait." | | 12 | "He'd keep running." | | 13 | "She'd studied police reports about" | | 14 | "They'd been investigating a trafficking" | | 15 | "He went into the tunnels" | | 16 | "He never came out." | | 17 | "She'd been wearing the same" | | 18 | "She could hear his footsteps" | | 19 | "She could call it in." |
| | ratio | 0.21 | |
| 68.87% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 124 | | matches | | 0 | "The suspect's trainers splashed through" | | 1 | "Detective Harlow Quinn pushed herself" | | 2 | "The rain hammered down in" | | 3 | "She'd been tailing him for" | | 4 | "That's when she knew." | | 5 | "The suspect was maybe thirty," | | 6 | "He wore an expensive black" | | 7 | "Quinn's instincts, honed over eighteen" | | 8 | "The Raven's Nest connections." | | 9 | "The missing persons." | | 10 | "The whispers about an underground" | | 11 | "He cut left down an" | | 12 | "Steam from the kitchen's ventilation" | | 13 | "Quinn's breathing came hard and" | | 14 | "The alley narrowed, forcing them" | | 15 | "The suspect glanced back." | | 16 | "Their eyes met for a" | | 17 | "Quinn's jaw clenched." | | 18 | "She was faster at distance" | | 19 | "He vaulted over a low" |
| | ratio | 0.782 | |
| 40.32% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 124 | | matches | | | ratio | 0.008 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 13 | | matches | | 0 | "The suspect's trainers splashed through a puddle that reflected the sodium-orange glow of Camden High Street." | | 1 | "Twenty-three minutes of surveillance that had turned sharp the moment he bolted from the internet café." | | 2 | "He wore an expensive black peacoat that didn't match the nervous energy radiating from his shoulders." | | 3 | "He vaulted over a low brick wall that marked the edge of a small residential courtyard." | | 4 | "She was nearly on him when he descended the station stairs, taking them two at a time, his hand sliding along the metal rail with practiced ease." | | 5 | "The abandoned sections of the Underground that had been sealed off decades ago, left to rot beneath the living city." | | 6 | "The structural reports that warned of collapsed sections and unsecured shafts." | | 7 | "Something that lived in the margins of every case file that crossed her desk." | | 8 | "She could hear his footsteps echoing in the tunnel, growing fainter." | | 9 | "She could station officers above ground, organize a proper search, bring in the specialized units that knew these tunnels better than she did." | | 10 | "An underground market that sold impossible things, that appeared and disappeared like it operated on a different geography entirely." | | 11 | "Something that made her trained detective's brain flag as wrong." | | 12 | "The noise of transactions in a place that shouldn't exist, trading in goods that defied the forensic analysis she'd spent her career perfecting." |
| |
| 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 | |