| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn straightened again [again]" |
| | dialogueSentences | 75 | | tagDensity | 0.213 | | leniency | 0.427 | | rawRatio | 0.063 | | effectiveRatio | 0.027 | |
| 82.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1756 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | | | 3 | |
| | highlights | | 0 | "softly" | | 1 | "deliberately" | | 2 | "very" | | 3 | "really" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 80.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1756 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "database" | | 1 | "silk" | | 2 | "etched" | | 3 | "crystal" | | 4 | "mechanical" | | 5 | "weight" | | 6 | "flickered" |
| |
| 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 | 115 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 115 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 174 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1760 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 65.17% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 1061 | | uniqueNames | 24 | | maxNameDensity | 1.7 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 18 | | Camden | 2 | | Town | 1 | | Amir | 1 | | Patel | 10 | | Morris | 3 | | Thames | 2 | | Victorian-era | 1 | | Veil | 2 | | Market | 1 | | Northern | 2 | | Metropolitan | 1 | | Police | 1 | | Hampstead | 1 | | Latin | 1 | | Victorian | 2 | | Keep | 1 | | Compass | 1 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Eva | 2 | | Cordelia | 2 | | Ashworth | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Amir" | | 2 | "Patel" | | 3 | "Morris" | | 4 | "Victorian-era" | | 5 | "Police" | | 6 | "Compass" | | 7 | "Kowalski" | | 8 | "Museum" | | 9 | "Eva" | | 10 | "Cordelia" | | 11 | "Ashworth" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Thames" | | 3 | "Hampstead" | | 4 | "Latin" | | 5 | "British" |
| | globalScore | 0.652 | | windowScore | 0.667 | |
| 78.57% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like he'd aged twenty years overni" | | 1 | "felt like admitting the supernatural ca" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1760 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 174 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 86 | | mean | 20.47 | | std | 19.59 | | cv | 0.957 | | sampleLengths | | 0 | 39 | | 1 | 18 | | 2 | 42 | | 3 | 50 | | 4 | 46 | | 5 | 3 | | 6 | 12 | | 7 | 23 | | 8 | 70 | | 9 | 1 | | 10 | 9 | | 11 | 31 | | 12 | 31 | | 13 | 6 | | 14 | 30 | | 15 | 50 | | 16 | 3 | | 17 | 8 | | 18 | 5 | | 19 | 26 | | 20 | 75 | | 21 | 16 | | 22 | 19 | | 23 | 3 | | 24 | 58 | | 25 | 5 | | 26 | 6 | | 27 | 39 | | 28 | 19 | | 29 | 5 | | 30 | 5 | | 31 | 11 | | 32 | 47 | | 33 | 4 | | 34 | 12 | | 35 | 5 | | 36 | 7 | | 37 | 10 | | 38 | 43 | | 39 | 9 | | 40 | 1 | | 41 | 28 | | 42 | 9 | | 43 | 56 | | 44 | 52 | | 45 | 9 | | 46 | 39 | | 47 | 2 | | 48 | 17 | | 49 | 10 |
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| 86.96% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 115 | | matches | | 0 | "been arranged" | | 1 | "been sealed" | | 2 | "was discovered" | | 3 | "been reported" | | 4 | "was blocked" | | 5 | "were aimed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 77.18% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 174 | | ratio | 0.023 | | matches | | 0 | "Tables still held their wares — crystal grids, jars of dried herbs with handwritten labels in Latin, leather-bound volumes whose spines bore no titles." | | 1 | "The tunnel mouth was blocked by a steel barrier that bore official-looking warning signs — structural instability, asbestos contamination, Keep Out." | | 2 | "The compass needles in the photographs she'd taken confirmed what she suspected — supernatural energy lay behind that steel." | | 3 | "Three years hadn't softened the edges of that memory — the phone call, the Thames embankment, the way Morris looked like he'd aged twenty years overnight." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1067 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.023430178069353328 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005623242736644799 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 174 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 174 | | mean | 10.11 | | std | 8.88 | | cv | 0.878 | | sampleLengths | | 0 | 5 | | 1 | 21 | | 2 | 12 | | 3 | 1 | | 4 | 6 | | 5 | 9 | | 6 | 3 | | 7 | 21 | | 8 | 15 | | 9 | 6 | | 10 | 2 | | 11 | 22 | | 12 | 5 | | 13 | 2 | | 14 | 19 | | 15 | 14 | | 16 | 22 | | 17 | 10 | | 18 | 3 | | 19 | 7 | | 20 | 5 | | 21 | 19 | | 22 | 3 | | 23 | 1 | | 24 | 7 | | 25 | 27 | | 26 | 12 | | 27 | 5 | | 28 | 19 | | 29 | 1 | | 30 | 9 | | 31 | 15 | | 32 | 10 | | 33 | 6 | | 34 | 3 | | 35 | 28 | | 36 | 6 | | 37 | 24 | | 38 | 6 | | 39 | 9 | | 40 | 17 | | 41 | 9 | | 42 | 2 | | 43 | 13 | | 44 | 3 | | 45 | 3 | | 46 | 5 | | 47 | 2 | | 48 | 3 | | 49 | 26 |
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| 74.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4827586206896552 | | totalSentences | 174 | | uniqueOpeners | 84 | |
| 35.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 94 | | matches | | 0 | "Then she photographed the rest" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 94 | | matches | | 0 | "She pulled her collar tighter" | | 1 | "He flipped a page" | | 2 | "She'd seen one before, three" | | 3 | "She wore a heavy coat" | | 4 | "Her knees cracked." | | 5 | "She swept her torch across" | | 6 | "She photographed the compasses, the" | | 7 | "She turned back to the" | | 8 | "She pointed to the chain" | | 9 | "She leaned in, careful not" | | 10 | "She turned to the sealed" | | 11 | "She walked toward the barrier." | | 12 | "He'd been her partner for" | | 13 | "He closed his notepad" | | 14 | "His body bore no marks" | | 15 | "She pulled out her phone" | | 16 | "He'd turned away, pretending to" | | 17 | "She stared at the compasses" | | 18 | "She stepped back from the" | | 19 | "She crouched beside Cordelia Ashworth" |
| | ratio | 0.213 | |
| 66.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 94 | | matches | | 0 | "The body had been arranged." | | 1 | "That was the first thing" | | 2 | "She pulled her collar tighter" | | 3 | "The underground air tasted of" | | 4 | "DS Amir Patel materialised from" | | 5 | "He flipped a page" | | 6 | "The woman lay on her" | | 7 | "Quinn recognised the shape immediately." | | 8 | "She'd seen one before, three" | | 9 | "The dead woman's eyes were" | | 10 | "She wore a heavy coat" | | 11 | "Patel consulted his phone" | | 12 | "Her knees cracked." | | 13 | "She swept her torch across" | | 14 | "The Veil Market operated out" | | 15 | "Quinn stepped closer and lifted" | | 16 | "Hand-stitched into the silk lining" | | 17 | "Patel's voice carried that careful" | | 18 | "Quinn studied the compasses in" | | 19 | "Both were identical brass instruments" |
| | ratio | 0.787 | |
| 53.19% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 94 | | matches | | 0 | "Either way, Quinn intended to" |
| | ratio | 0.011 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 6 | | matches | | 0 | "She wore a heavy coat over Victorian-era mourning dress, complete with jet beadwork that caught the forensic lights in tiny dark stars." | | 1 | "A clockwork bird perched on one stall, its brass feathers ruffling in a draft that carried the faint scent of ozone." | | 2 | "The mourning dress, appropriate for a Victorian funeral but absurd for a twenty-first-century woman who'd been reported alive five days ago." | | 3 | "The tunnel mouth was blocked by a steel barrier that bore official-looking warning signs — structural instability, asbestos contamination, Keep Out." | | 4 | "An occult specialist recommended by an informant who dealt in information rather than contraband." | | 5 | "Above them, the last Northern line train of the evening rumbled past, its passengers oblivious to the market, the body, and the thing waiting behind a steel bar…" |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "DS Amir Patel materialised, his notepad already open" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 75 | | tagDensity | 0.04 | | leniency | 0.08 | | rawRatio | 0.333 | | effectiveRatio | 0.027 | |