| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "She glanced back [back]" |
| | dialogueSentences | 28 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.083 | | effectiveRatio | 0.071 | |
| 96.56% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1455 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 7.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1455 | | totalAiIsms | 27 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | |
| | highlights | | 0 | "echoing" | | 1 | "gloom" | | 2 | "macabre" | | 3 | "shattered" | | 4 | "chill" | | 5 | "traced" | | 6 | "weight" | | 7 | "etched" | | 8 | "quivered" | | 9 | "uncanny" | | 10 | "shimmered" | | 11 | "warmth" | | 12 | "throbbed" | | 13 | "pulse" | | 14 | "raced" | | 15 | "flickered" | | 16 | "otherworldly" | | 17 | "quickened" | | 18 | "vibrated" | | 19 | "cascading" | | 20 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 78 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1425 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1047 | | uniqueNames | 16 | | maxNameDensity | 1.05 | | worstName | "Patel" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Patel" | | discoveredNames | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Northern | 1 | | Line | 1 | | Patel | 11 | | Bovril | 1 | | Brylcreem | 1 | | Morris | 3 | | Harlow | 9 | | Veil | 1 | | Market | 1 | | Earth | 1 | | Eva | 2 | | Kowalski | 1 | | Displacement | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Line" | | 3 | "Patel" | | 4 | "Morris" | | 5 | "Harlow" | | 6 | "Earth" | | 7 | "Eva" | | 8 | "Kowalski" |
| | places | (empty) | | globalScore | 0.975 | | windowScore | 0.833 | |
| 66.67% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 2 | | matches | | 0 | "quite match arterial spray—too thick, too slow to congeal" | | 1 | "seemed elongated slitted" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 4 | | per1kWords | 2.807 | | wordCount | 1425 | | matches | | 0 | "not by a blade but by something jagged, unnatural" | | 1 | "not north, but toward the cracked wall behind the body" | | 2 | "not random vandalism, but deliberate: interlocking symbols, angular" | | 3 | "not empty, but alive with writhing shapes" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 94 | | matches | (empty) | |
| 94.72% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 52.78 | | std | 25.41 | | cv | 0.482 | | sampleLengths | | 0 | 79 | | 1 | 71 | | 2 | 51 | | 3 | 44 | | 4 | 60 | | 5 | 83 | | 6 | 26 | | 7 | 43 | | 8 | 88 | | 9 | 101 | | 10 | 41 | | 11 | 93 | | 12 | 34 | | 13 | 79 | | 14 | 22 | | 15 | 61 | | 16 | 44 | | 17 | 32 | | 18 | 51 | | 19 | 88 | | 20 | 27 | | 21 | 51 | | 22 | 27 | | 23 | 50 | | 24 | 15 | | 25 | 62 | | 26 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 187 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 2 | | flaggedSentences | 11 | | totalSentences | 94 | | ratio | 0.117 | | matches | | 0 | "Military precision guided her steps—heel to toe, deliberate—past the first cluster of forensics techs snapping photos of shattered vials and singed fabric scraps." | | 1 | "The edges wept a dark, viscous fluid that didn't quite match arterial spray—too thick, too slow to congeal." | | 2 | "Verdigris patina clung to the edges, and the needle quivered—not north, but toward the cracked wall behind the body, where shadows pooled unnaturally deep." | | 3 | "Her partner, Morris, had chased leads like this three years back—unexplained deaths laced with the uncanny—until he vanished into thin air during a raid she still couldn't piece together." | | 4 | "Her torch revealed faint scratches along the seams—not random vandalism, but deliberate: interlocking symbols, angular and looping, pulsing faintly as if alive." | | 5 | "Her mind raced through the fragments: the market's schedule, moving every full moon to evade raids; the bone tokens required for entry, carved from who-knew-what; whispers from informants about rifts bleeding into the mundane world." | | 6 | "The Veil Market wasn't just a black market—it was a nexus, where the supernatural brushed Earth's underbelly." | | 7 | "A jolt shot up her arm—static, or something more." | | 8 | "She recalled Eva Kowalski's offhand warnings during their last briefing—Eva, that freckled researcher with her curly red hair and endless satchel of tomes, who'd cornered her at the museum with tales of rifts destabilizing." | | 9 | "Morris's autopsy had read the same—organs shifted, as if yanked by invisible strings." | | 10 | "A fissure split the centre, wide enough to glimpse swirling darkness beyond—not empty, but alive with writhing shapes that whispered in no language she knew." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1064 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.03101503759398496 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.010338345864661654 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 15.16 | | std | 9.68 | | cv | 0.639 | | sampleLengths | | 0 | 25 | | 1 | 27 | | 2 | 27 | | 3 | 18 | | 4 | 23 | | 5 | 26 | | 6 | 2 | | 7 | 2 | | 8 | 20 | | 9 | 18 | | 10 | 13 | | 11 | 11 | | 12 | 12 | | 13 | 18 | | 14 | 3 | | 15 | 36 | | 16 | 24 | | 17 | 13 | | 18 | 1 | | 19 | 17 | | 20 | 8 | | 21 | 20 | | 22 | 24 | | 23 | 15 | | 24 | 11 | | 25 | 5 | | 26 | 38 | | 27 | 15 | | 28 | 25 | | 29 | 19 | | 30 | 29 | | 31 | 9 | | 32 | 18 | | 33 | 14 | | 34 | 10 | | 35 | 20 | | 36 | 30 | | 37 | 6 | | 38 | 35 | | 39 | 3 | | 40 | 1 | | 41 | 2 | | 42 | 24 | | 43 | 16 | | 44 | 25 | | 45 | 22 | | 46 | 7 | | 47 | 14 | | 48 | 13 | | 49 | 5 |
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| 85.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5212765957446809 | | totalSentences | 94 | | uniqueOpeners | 49 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 80.87% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 69 | | matches | | 0 | "Her torch beam sliced through" | | 1 | "She straightened, her worn leather" | | 2 | "He clutched a clipboard like" | | 3 | "She crouched beside it, gloved" | | 4 | "She tilted her head, the" | | 5 | "Her gaze drifted to the" | | 6 | "she murmured, snapping a photo" | | 7 | "She paced the platform's edge," | | 8 | "Her partner, Morris, had chased" | | 9 | "She knelt again, this time" | | 10 | "She traced a gash on" | | 11 | "Her jaw tightened." | | 12 | "She pulled a evidence bag" | | 13 | "She nodded toward it, stepping" | | 14 | "Her torch revealed faint scratches" | | 15 | "She pressed a palm to" | | 16 | "He hesitated, then barked orders." | | 17 | "Her mind raced through the" | | 18 | "She straightened, voice low" | | 19 | "She glanced back at the" |
| | ratio | 0.348 | |
| 10.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 69 | | matches | | 0 | "Harlow Quinn shoved the rusted" | | 1 | "Her torch beam sliced through" | | 2 | "She straightened, her worn leather" | | 3 | "Military precision guided her steps—heel" | | 4 | "The victim lay just beyond," | | 5 | "DS Patel emerged from the" | | 6 | "He clutched a clipboard like" | | 7 | "Harlow ignored the jab, her" | | 8 | "She crouched beside it, gloved" | | 9 | "The edges wept a dark," | | 10 | "Patel shifted his weight, glancing" | | 11 | "She tilted her head, the" | | 12 | "That word always slithered into" | | 13 | "Her gaze drifted to the" | | 14 | "Verdigris patina clung to the" | | 15 | "she murmured, snapping a photo" | | 16 | "Patel snorted, rubbing his chin." | | 17 | "Harlow rose, her height giving" | | 18 | "She paced the platform's edge," | | 19 | "The air hummed faintly, a" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 29.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 7 | | matches | | 0 | "Her torch beam sliced through the gloom, catching the sprawl of chalk outlines and scattered evidence markers that turned the abandoned Tube station into a maca…" | | 1 | "The edges wept a dark, viscous fluid that didn't quite match arterial spray—too thick, too slow to congeal." | | 2 | "Protruding from his rigid fingers: a small brass compass, its casing etched with faint sigils that gleamed under her light." | | 3 | "Her torch revealed faint scratches along the seams—not random vandalism, but deliberate: interlocking symbols, angular and looping, pulsing faintly as if alive." | | 4 | "Her mind raced through the fragments: the market's schedule, moving every full moon to evade raids; the bone tokens required for entry, carved from who-knew-wha…" | | 5 | "She recalled Eva Kowalski's offhand warnings during their last briefing—Eva, that freckled researcher with her curly red hair and endless satchel of tomes, who'…" | | 6 | "A fissure split the centre, wide enough to glimpse swirling darkness beyond—not empty, but alive with writhing shapes that whispered in no language she knew." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 2 | | matches | | 0 | "DS Patel emerged, his face slick with sweat despite the chill" | | 1 | "She straightened, voice low" |
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| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Patel muttered (mutter)" |
| | dialogueSentences | 28 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 1 | | effectiveRatio | 0.143 | |