| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.714 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1339 | | totalAiIsmAdverbs | 2 | | 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) | |
| 55.19% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1339 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "treacherous" | | 1 | "weight" | | 2 | "footsteps" | | 3 | "echoing" | | 4 | "standard" | | 5 | "electric" | | 6 | "measured" | | 7 | "depths" |
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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 | 144 | | matches | (empty) | |
| 93.25% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 144 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1325 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1290 | | uniqueNames | 25 | | maxNameDensity | 1.47 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Chapter | 1 | | Seven | 1 | | Quinn | 19 | | London | 2 | | Raven | 2 | | Nest | 2 | | Morris | 3 | | Lane | 1 | | Vietnamese | 1 | | Wardour | 1 | | Street | 2 | | Shaftesbury | 1 | | Avenue | 1 | | Great | 1 | | Pulteney | 1 | | Glock | 1 | | Veil | 1 | | Market | 2 | | English | 1 | | Victorian | 2 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Spanish-touched | 1 | | Tomás | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Herrera" | | 6 | "Tomás" |
| | places | | 0 | "London" | | 1 | "Lane" | | 2 | "Wardour" | | 3 | "Street" | | 4 | "Shaftesbury" | | 5 | "Avenue" | | 6 | "Pulteney" | | 7 | "Market" | | 8 | "English" |
| | globalScore | 0.764 | | windowScore | 0.667 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 5 | | matches | | 0 | "smelled like spilled lager and regret, and" | | 1 | "descent that seemed to go far deeper than any London street should allow" | | 2 | "smelled like copper and wet stone and some" | | 3 | "looked like smoke" | | 4 | "Things that seemed to shift when she wasn't looking directly at them" |
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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 | 1325 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 147 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 28.8 | | std | 19.71 | | cv | 0.684 | | sampleLengths | | 0 | 3 | | 1 | 13 | | 2 | 67 | | 3 | 60 | | 4 | 48 | | 5 | 38 | | 6 | 11 | | 7 | 3 | | 8 | 41 | | 9 | 41 | | 10 | 11 | | 11 | 53 | | 12 | 29 | | 13 | 8 | | 14 | 66 | | 15 | 22 | | 16 | 2 | | 17 | 26 | | 18 | 60 | | 19 | 14 | | 20 | 43 | | 21 | 32 | | 22 | 28 | | 23 | 25 | | 24 | 5 | | 25 | 51 | | 26 | 26 | | 27 | 38 | | 28 | 44 | | 29 | 5 | | 30 | 53 | | 31 | 16 | | 32 | 55 | | 33 | 36 | | 34 | 43 | | 35 | 11 | | 36 | 35 | | 37 | 7 | | 38 | 61 | | 39 | 6 | | 40 | 30 | | 41 | 6 | | 42 | 6 | | 43 | 28 | | 44 | 15 | | 45 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 144 | | matches | | 0 | "was trained" | | 1 | "been used" |
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| 51.85% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 225 | | matches | | 0 | "wasn't running" | | 1 | "was waiting" | | 2 | "was waiting" | | 3 | "was coming" | | 4 | "wasn't looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 147 | | ratio | 0.054 | | matches | | 0 | "Soho at midnight smelled like spilled lager and regret, and the pavement was a mirror of reflected light—treacherous." | | 1 | "Quinn knew it—tiny Vietnamese restaurant on the corner, usually packed." | | 2 | "The staircase descended at an angle that suggested purpose—too deliberate, too well-maintained despite the industrial decay surrounding it." | | 3 | "The air changed immediately—grew colder, thinner somehow, like breathing through cloth." | | 4 | "The brick walls gave way to something else—stone that looked older, cut with precision that predated the Victorian era above." | | 5 | "This was colder, bluer—emanating from torches mounted on the walls at intervals, burning with flames that cast shadows in the wrong directions." | | 6 | "They all wore tokens around their necks—small objects carved from bone, hanging on strings or chains." | | 7 | "A figure stepped into her path—a man with warm brown eyes and a Saint Christopher medallion catching blue light." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 827 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.030229746070133012 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006045949214026602 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 9.01 | | std | 6.47 | | cv | 0.717 | | sampleLengths | | 0 | 16 | | 1 | 20 | | 2 | 18 | | 3 | 6 | | 4 | 23 | | 5 | 13 | | 6 | 13 | | 7 | 26 | | 8 | 2 | | 9 | 6 | | 10 | 3 | | 11 | 21 | | 12 | 24 | | 13 | 8 | | 14 | 2 | | 15 | 10 | | 16 | 6 | | 17 | 12 | | 18 | 11 | | 19 | 3 | | 20 | 7 | | 21 | 10 | | 22 | 8 | | 23 | 9 | | 24 | 7 | | 25 | 6 | | 26 | 2 | | 27 | 15 | | 28 | 6 | | 29 | 12 | | 30 | 1 | | 31 | 10 | | 32 | 11 | | 33 | 18 | | 34 | 2 | | 35 | 2 | | 36 | 20 | | 37 | 14 | | 38 | 2 | | 39 | 2 | | 40 | 5 | | 41 | 6 | | 42 | 3 | | 43 | 5 | | 44 | 3 | | 45 | 4 | | 46 | 7 | | 47 | 20 | | 48 | 6 | | 49 | 8 |
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| 72.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.4965986394557823 | | totalSentences | 147 | | uniqueOpeners | 73 | |
| 80.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 125 | | matches | | 0 | "Just a rectangular mouth swallowing" | | 1 | "Somewhere in the dark below," | | 2 | "Away from her." |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 125 | | matches | | 0 | "She was three steps behind," | | 1 | "Her feet knew the rhythm" | | 2 | "He'd been leaving The Raven's" | | 3 | "She'd been at her desk" | | 4 | "She gained ground, her breathing" | | 5 | "Her voice cracked the rain-soaked" | | 6 | "He didn't slow." | | 7 | "Her hand stretched out, fingers" | | 8 | "He twisted, slipping like water" | | 9 | "She pulled her torch from" | | 10 | "She was trained for this." | | 11 | "She stared at the stairs," | | 12 | "She'd read those files." | | 13 | "He wasn't running anymore." | | 14 | "He was waiting." | | 15 | "It felt *aware*." | | 16 | "She touched the warrant in" | | 17 | "It was useless here." | | 18 | "Her radio crackled." | | 19 | "She didn't answer." |
| | ratio | 0.216 | |
| 84.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 125 | | matches | | 0 | "# Chapter Seven The suspect" | | 1 | "She was three steps behind," | | 2 | "Soho at midnight smelled like" | | 3 | "Her feet knew the rhythm" | | 4 | "The man ahead wore a" | | 5 | "He'd been leaving The Raven's" | | 6 | "Quinn's thighs burned." | | 7 | "She'd been at her desk" | | 8 | "The suspect cut left onto" | | 9 | "Quinn knew it—tiny Vietnamese restaurant" | | 10 | "The kitchen light was dark" | | 11 | "She gained ground, her breathing" | | 12 | "Her voice cracked the rain-soaked" | | 13 | "He didn't slow." | | 14 | "Her hand stretched out, fingers" | | 15 | "He twisted, slipping like water" | | 16 | "A man walking a bulldog" | | 17 | "The dog barked, confused by" | | 18 | "The suspect veered toward Shaftesbury" | | 19 | "Quinn skidded to a halt" |
| | ratio | 0.752 | |
| 40.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 125 | | matches | | 0 | "Before she could process his" |
| | ratio | 0.008 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 12 | | matches | | 0 | "About people who seemed to exist in the gaps between normal police work and something stranger, something Morris had died chasing three years ago." | | 1 | "Just a rectangular mouth swallowing the darkness below, and the sound of the suspect's footsteps echoing upward like drowned voices." | | 2 | "An underground market that relocated every full moon, dealing in things that didn't appear on any official registry." | | 3 | "The written obsession of a dead detective who'd spent too much time chasing shadows." | | 4 | "The torch beam caught graffiti that didn't make sense: symbols and words in languages she couldn't identify." | | 5 | "The brick walls gave way to something else—stone that looked older, cut with precision that predated the Victorian era above." | | 6 | "The connection was there, buried in files and dismissed by bureaucrats who understood crime but not this." | | 7 | "Actual tunnel, with Victorian pipe work running along the ceiling and industrial light fixtures that hadn't been used in decades." | | 8 | "The tunnel curved slightly, descending in a gentle slope that went deeper, always deeper." | | 9 | "This was colder, bluer—emanating from torches mounted on the walls at intervals, burning with flames that cast shadows in the wrong directions." | | 10 | "Merchants in dark clothes stood behind tables laden with objects that reflected the strange blue light in ways objects shouldn't." | | 11 | "All the weight of an entire underground market's attention, focused on the uniform woman standing at the platform's edge with a torch and a warrant that meant n…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |