| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.525 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1466 | | 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) | |
| 59.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1466 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "echo" | | 1 | "gloom" | | 2 | "gleaming" | | 3 | "etched" | | 4 | "weight" | | 5 | "scanning" | | 6 | "eyebrow" | | 7 | "silence" | | 8 | "chill" | | 9 | "echoed" | | 10 | "pounding" |
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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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1446 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1073 | | uniqueNames | 14 | | maxNameDensity | 2.24 | | worstName | "Quinn" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 24 | | Tube | 1 | | Camden | 1 | | Rorschach | 1 | | Kowalski | 1 | | Eva | 11 | | Choudhury | 6 | | War-era | 1 | | Morris | 1 | | London | 3 | | Shade | 1 | | Veil | 1 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Choudhury" | | 6 | "Morris" |
| | places | | 0 | "Rorschach" | | 1 | "London" | | 2 | "Market" |
| | globalScore | 0.382 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.692 | | wordCount | 1446 | | matches | | 0 | "not out, but deeper underground" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 36.15 | | std | 22.87 | | cv | 0.633 | | sampleLengths | | 0 | 79 | | 1 | 59 | | 2 | 61 | | 3 | 68 | | 4 | 33 | | 5 | 39 | | 6 | 54 | | 7 | 27 | | 8 | 8 | | 9 | 62 | | 10 | 3 | | 11 | 8 | | 12 | 31 | | 13 | 65 | | 14 | 27 | | 15 | 76 | | 16 | 63 | | 17 | 9 | | 18 | 11 | | 19 | 37 | | 20 | 17 | | 21 | 51 | | 22 | 4 | | 23 | 32 | | 24 | 54 | | 25 | 9 | | 26 | 26 | | 27 | 47 | | 28 | 23 | | 29 | 65 | | 30 | 13 | | 31 | 8 | | 32 | 10 | | 33 | 40 | | 34 | 13 | | 35 | 37 | | 36 | 11 | | 37 | 53 | | 38 | 69 | | 39 | 44 |
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| 94.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 95 | | matches | | 0 | "were curled" | | 1 | "been turned" | | 2 | "was pooled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 184 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 9 | | flaggedSentences | 19 | | totalSentences | 113 | | ratio | 0.168 | | matches | | 0 | "Her boots landed with a hollow echo on the stone platform—old rails and ancient shadows folding in close like conspirators." | | 1 | "The abandoned Tube station beneath Camden—condemned, officially sealed, yet buzzing with illicit rumor—was not the typical theatre for a murder scene." | | 2 | "Eva Kowalski—aurora’s childhood friend, but tonight just another witness with too much to say." | | 3 | "The compass’s casing glimmered dull green-blue under the lights; its face, etched with strange sigils, glinted." | | 4 | "His left sleeve, though, retained a faint crescent-shaped patch—as though he often wore a watch, now missing." | | 5 | "At this angle, the wound opened before her—slit cleanly, just below the jaw, but not deep enough to kill outright unless something else intervened." | | 6 | "Too many to count; but one set, booted, had tracked a grainy lime residue peculiar to the platform edge, then stopped abruptly half a meter from the body." | | 7 | "She knelt again, nose wrinkling at the scent—alchemical, not chemical; sharper, almost mossy." | | 8 | "Eva inhaled sharply; Quinn caught her reaction." | | 9 | "\"Those marks… I’ve seen them before. On a case file. Medieval, actually—records of illegal dueling, um, with enchanted implements.\" She bit her lip; Quinn stored the note away." | | 10 | "\"Thank you.\" Quinn’s gaze swept the rest of the scene—lamplight, dust, old advertising boards for War-era musicals, corners thick with darkness." | | 11 | "She thought of her partner, Morris, and the London night three years lost—of wounds that did not close and explanations that didn’t fit statistics or training manuals." | | 12 | "The sigils, though scuffed, were unmistakable protection wards—old, possibly Shade craft, the kind one saw in seized Veil Market contraband." | | 13 | "\"Look here—our victim came through that entrance, alone. He stops by the crate. No sign of forced entry, so he knew how to get in. He’s not here by accident. Nor are you, Miss Kowalski.\" Quinn fixed Eva in her gaze; the younger woman wilted slightly." | | 14 | "Quinn took it, weighing it in her palm; it was warm despite the chill." | | 15 | "From the edge of the lamplight, the sound of a far door slamming echoed up the tunnel—too loud, too deliberate, not just the settling bones of this place." | | 16 | "The compass, the token, the crate’s inlaid ward, the occult signatures—these were not accidents." | | 17 | "But the evidence here was incomplete, curated—a staging." | | 18 | "She saw now what others missed: the blood was pooled, not thrown; the phone tossed too far from a dying hand; the absence of a watch left an unacknowledged tan line." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1092 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint crescent-shaped patch—" |
| | adverbCount | 40 | | adverbRatio | 0.03663003663003663 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.010073260073260074 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 12.8 | | std | 8.37 | | cv | 0.654 | | sampleLengths | | 0 | 21 | | 1 | 20 | | 2 | 14 | | 3 | 24 | | 4 | 21 | | 5 | 21 | | 6 | 17 | | 7 | 8 | | 8 | 18 | | 9 | 14 | | 10 | 21 | | 11 | 17 | | 12 | 18 | | 13 | 14 | | 14 | 8 | | 15 | 11 | | 16 | 15 | | 17 | 7 | | 18 | 11 | | 19 | 4 | | 20 | 26 | | 21 | 9 | | 22 | 9 | | 23 | 29 | | 24 | 16 | | 25 | 11 | | 26 | 10 | | 27 | 6 | | 28 | 8 | | 29 | 7 | | 30 | 10 | | 31 | 17 | | 32 | 7 | | 33 | 21 | | 34 | 3 | | 35 | 5 | | 36 | 3 | | 37 | 14 | | 38 | 17 | | 39 | 24 | | 40 | 8 | | 41 | 9 | | 42 | 10 | | 43 | 12 | | 44 | 2 | | 45 | 21 | | 46 | 6 | | 47 | 6 | | 48 | 8 | | 49 | 3 |
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| 69.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4247787610619469 | | totalSentences | 113 | | uniqueOpeners | 48 | |
| 78.43% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 85 | | matches | | 0 | "Too many to count; but" | | 1 | "Only magic or madness could" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 85 | | matches | | 0 | "Her boots landed with a" | | 1 | "She paused, knuckles brushing the" | | 2 | "She glanced up, anxiety flickering" | | 3 | "She nodded but did not" | | 4 | "She was clutching her satchel" | | 5 | "He dumped a small brass" | | 6 | "She felt the urge to" | | 7 | "His left sleeve, though, retained" | | 8 | "She stepped closer to the" | | 9 | "His pockets had been turned" | | 10 | "Her lips thinned." | | 11 | "She stepped past him, scanning" | | 12 | "She knelt again, nose wrinkling" | | 13 | "Her gaze caught on a" | | 14 | "She pressed a gloved finger" | | 15 | "She scraped a bit into" | | 16 | "Her hand floated over the" | | 17 | "She bit her lip; Quinn" | | 18 | "She thought of her partner," | | 19 | "she asked, addressing Choudhury" |
| | ratio | 0.294 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 85 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "Her boots landed with a" | | 2 | "She paused, knuckles brushing the" | | 3 | "The time didn't match the" | | 4 | "The abandoned Tube station beneath" | | 5 | "Quinn’s silhouette, tall and crisp" | | 6 | "The scene itself unraveled farther" | | 7 | "Light pooled from a pair" | | 8 | "The corpse, male, mid-twenties at" | | 9 | "Blood, too little for the" | | 10 | "A slender woman, red curls" | | 11 | "Eva Kowalski—aurora’s childhood friend, but" | | 12 | "She glanced up, anxiety flickering" | | 13 | "Quinn took in the nervous" | | 14 | "She nodded but did not" | | 15 | "Eva’s voice cracked" | | 16 | "She was clutching her satchel" | | 17 | "A stocky constable, Choudhury, sidled" | | 18 | "He dumped a small brass" | | 19 | "The compass’s casing glimmered dull" |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "His left sleeve, though, retained a faint crescent-shaped patch—as though he often wore a watch, now missing." | | 1 | "She stepped closer to the body, lowering herself on her haunches, ignoring the protest in her knees." | | 2 | "She thought of her partner, Morris, and the London night three years lost—of wounds that did not close and explanations that didn’t fit statistics or training m…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Eva whispered (whisper)" | | 1 | "Quinn muttered (mutter)" |
| | dialogueSentences | 40 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.25 | | effectiveRatio | 0.1 | |