| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1184 | | totalAiIsmAdverbs | 1 | | 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) | |
| 53.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1184 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "measured" | | 1 | "glinting" | | 2 | "glint" | | 3 | "pulse" | | 4 | "pristine" | | 5 | "pulsed" | | 6 | "stomach" | | 7 | "echoed" | | 8 | "footsteps" | | 9 | "silence" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "eyes widened" | | 1 | "the air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 152 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 157 | | 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 | 4 | | markdownWords | 10 | | totalWords | 1168 | | ratio | 0.009 | | matches | | 0 | "The Raven’s Nest" | | 1 | "The Veil Market." | | 2 | "Who" | | 3 | "The Raven’s Nest" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1097 | | uniqueNames | 12 | | maxNameDensity | 1.28 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 14 | | Raven | 2 | | Tomás | 2 | | Herrera | 12 | | Saint | 2 | | Christopher | 2 | | Morris | 3 | | English | 1 | | Veil | 1 | | Nest | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" |
| | places | | | globalScore | 0.862 | | windowScore | 0.667 | |
| 80.56% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like distant whispers" | | 1 | "something like burnt copper" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.856 | | wordCount | 1168 | | matches | | 0 | "not with fear, but recognition" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 157 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 16.22 | | std | 18.11 | | cv | 1.116 | | sampleLengths | | 0 | 91 | | 1 | 62 | | 2 | 60 | | 3 | 3 | | 4 | 44 | | 5 | 50 | | 6 | 5 | | 7 | 36 | | 8 | 6 | | 9 | 41 | | 10 | 14 | | 11 | 31 | | 12 | 5 | | 13 | 5 | | 14 | 1 | | 15 | 1 | | 16 | 38 | | 17 | 3 | | 18 | 42 | | 19 | 41 | | 20 | 4 | | 21 | 3 | | 22 | 32 | | 23 | 5 | | 24 | 28 | | 25 | 15 | | 26 | 44 | | 27 | 1 | | 28 | 5 | | 29 | 9 | | 30 | 2 | | 31 | 10 | | 32 | 9 | | 33 | 23 | | 34 | 11 | | 35 | 17 | | 36 | 14 | | 37 | 10 | | 38 | 44 | | 39 | 20 | | 40 | 2 | | 41 | 34 | | 42 | 3 | | 43 | 13 | | 44 | 2 | | 45 | 23 | | 46 | 11 | | 47 | 2 | | 48 | 8 | | 49 | 7 |
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| 91.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 152 | | matches | | 0 | "were lined" | | 1 | "were lined" | | 2 | "was told" | | 3 | "were traded" | | 4 | "was stitched" | | 5 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 224 | | matches | | 0 | "wasn’t running" | | 1 | "was watching" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 18 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 157 | | ratio | 0.102 | | matches | | 0 | "Detective Harlow Quinn kept her stride measured, boots splashing through puddles that reflected the green neon sign of *The Raven’s Nest*—the kind of place where people vanished into the backrooms with no record of entry." | | 1 | "She saw the glint of metal—a door handle—before she registered the motion." | | 2 | "Her watch—worn leather, gifted by Morris—snagged on her jacket cuff as she descended." | | 3 | "The walls were lined with old chalk symbols, faded but unmistakable—warding marks." | | 4 | "To keep people out—or in." | | 5 | "Then she heard it—a voice." | | 6 | "She’d heard rumors—was told about it during an off-the-record briefing last year." | | 7 | "And backup meant radio—radio meant exposure." | | 8 | "Herrera reached into his coat and pulled out a small bone—white, curved." | | 9 | "His dark eyes widened—not with fear, but recognition." | | 10 | "Morris’s face flashed in her mind—wet blood on the pavement, hands slipping from her grip." | | 11 | "A place like this—were there guards?" | | 12 | "Then—movement." | | 13 | "From the corner of her eye—something moved in the stalls." | | 14 | "Back the way she came—back through the tunnels, up the stairs, bursting into the rain-soaked alley of Soho." | | 15 | "But first—she had to decide." |
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| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1121 | | adjectiveStacks | 3 | | stackExamples | | 0 | "cold, lifeless against her" | | 1 | "Blue-black light pulsed" | | 2 | "small leather-bound notebook" |
| | adverbCount | 24 | | adverbRatio | 0.021409455842997322 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0026761819803746653 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 157 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 157 | | mean | 7.44 | | std | 6.34 | | cv | 0.852 | | sampleLengths | | 0 | 22 | | 1 | 35 | | 2 | 34 | | 3 | 3 | | 4 | 16 | | 5 | 20 | | 6 | 3 | | 7 | 20 | | 8 | 12 | | 9 | 10 | | 10 | 9 | | 11 | 23 | | 12 | 2 | | 13 | 4 | | 14 | 3 | | 15 | 11 | | 16 | 13 | | 17 | 9 | | 18 | 11 | | 19 | 11 | | 20 | 14 | | 21 | 16 | | 22 | 6 | | 23 | 3 | | 24 | 5 | | 25 | 7 | | 26 | 10 | | 27 | 10 | | 28 | 2 | | 29 | 2 | | 30 | 5 | | 31 | 6 | | 32 | 15 | | 33 | 12 | | 34 | 9 | | 35 | 5 | | 36 | 3 | | 37 | 1 | | 38 | 6 | | 39 | 2 | | 40 | 2 | | 41 | 31 | | 42 | 5 | | 43 | 1 | | 44 | 1 | | 45 | 3 | | 46 | 1 | | 47 | 1 | | 48 | 14 | | 49 | 12 |
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| 51.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.35668789808917195 | | totalSentences | 157 | | uniqueOpeners | 56 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 123 | | matches | | 0 | "Then she heard it—a voice." | | 1 | "Then trace him later, through" | | 2 | "Then at her." | | 3 | "Then she turned and ran." |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 123 | | matches | | 0 | "She adjusted the grip on" | | 1 | "He moved with purpose, his" | | 2 | "She saw the glint of" | | 3 | "She didn’t hesitate." | | 4 | "Her watch—worn leather, gifted by" | | 5 | "Its leather strap was cold," | | 6 | "She ignored the unease creeping" | | 7 | "She moved forward, careful where" | | 8 | "She pulled out her phone," | | 9 | "She chose the left tunnel" | | 10 | "It narrowed quickly, the ceiling" | | 11 | "She passed a rusted sign" | | 12 | "She slowed, pressing against the" | | 13 | "She’d heard rumors—was told about" | | 14 | "She had him." | | 15 | "Her warrant card wouldn’t mean" | | 16 | "It wasn’t in any system." | | 17 | "She could call for support." | | 18 | "She knew the pattern." | | 19 | "He placed it on the" |
| | ratio | 0.293 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 88 | | totalSentences | 123 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "The suspect, Tomás Herrera, had" | | 3 | "Quinn didn’t hesitate." | | 4 | "She adjusted the grip on" | | 5 | "The alley was slick with" | | 6 | "Herrera wasn’t running." | | 7 | "He moved with purpose, his" | | 8 | "She saw the glint of" | | 9 | "Herrera vanished through it, the" | | 10 | "Quinn lunged, catching the edge" | | 11 | "Stairs descending into black." | | 12 | "She didn’t hesitate." | | 13 | "The light from the alley" | | 14 | "Her watch—worn leather, gifted by" | | 15 | "Its leather strap was cold," | | 16 | "She ignored the unease creeping" | | 17 | "The stairs opened into a" | | 18 | "The walls were lined with" | | 19 | "The air hummed with a" |
| | ratio | 0.715 | |
| 81.30% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 123 | | matches | | 0 | "To keep people out—or in." | | 1 | "If anyone found it, they’d" |
| | ratio | 0.016 | |
| 93.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn kept her stride measured, boots splashing through puddles that reflected the green neon sign of *The Raven’s Nest*—the kind of place wher…" | | 1 | "The alley was slick with rain, the cobblestones slick as glass, sending her boots skidding as she rounded a dumpster." | | 2 | "Inside, the air was thick with the scent of damp earth and something older, something that prickled at the back of her throat." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "She passed (pass)" | | 1 | "she shouted (shout)" |
| | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.5 | | effectiveRatio | 0.4 | |