| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1145 | | 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) | |
| 51.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1145 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "electric" | | 1 | "jaw clenched" | | 2 | "hulking" | | 3 | "tinged" | | 4 | "raced" | | 5 | "footsteps" | | 6 | "reminder" | | 7 | "crystallized" | | 8 | "echo" | | 9 | "velvet" |
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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 | "jaw/fists clenched" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 113 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 113 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1138 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.17% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1041 | | uniqueNames | 14 | | maxNameDensity | 1.06 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Camden | 3 | | Lock | 1 | | Morris | 5 | | Tube | 2 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 | | Glock | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Police" |
| | places | | | globalScore | 0.972 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.879 | | wordCount | 1138 | | matches | | 0 | "not as he was in life, but as she’d imagined him in his final moments" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 121 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 29.18 | | std | 21.04 | | cv | 0.721 | | sampleLengths | | 0 | 59 | | 1 | 70 | | 2 | 69 | | 3 | 45 | | 4 | 3 | | 5 | 68 | | 6 | 68 | | 7 | 52 | | 8 | 8 | | 9 | 58 | | 10 | 29 | | 11 | 4 | | 12 | 29 | | 13 | 38 | | 14 | 9 | | 15 | 24 | | 16 | 9 | | 17 | 1 | | 18 | 4 | | 19 | 24 | | 20 | 11 | | 21 | 19 | | 22 | 15 | | 23 | 12 | | 24 | 20 | | 25 | 24 | | 26 | 34 | | 27 | 12 | | 28 | 40 | | 29 | 61 | | 30 | 13 | | 31 | 22 | | 32 | 4 | | 33 | 8 | | 34 | 40 | | 35 | 43 | | 36 | 22 | | 37 | 40 | | 38 | 27 |
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| 99.05% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 113 | | matches | | 0 | "was unarmed" | | 1 | "was lost" |
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| 80.95% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 168 | | matches | | 0 | "was leading" | | 1 | "was already scaling" | | 2 | "was already closing" |
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| 1.18% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 121 | | ratio | 0.05 | | matches | | 0 | "He pulled something from his pocket—a small, pale object—and held it up to a slit in the door." | | 1 | "Smells leaked out—ozone, burnt herbs, wet fur." | | 2 | "She raised her fist and knocked—three sharp, authoritative raps." | | 3 | "She saw Morris’s face then, not as he was in life, but as she’d imagined him in his final moments—confused, afraid, alone in the dark." | | 4 | "The sounds and smells washed over her—a bazaar of whispers and shadows." | | 5 | "Creatures—some almost human, others decidedly not—milled about." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1057 | | adjectiveStacks | 1 | | stackExamples | | 0 | "eerie, green-tinged light" |
| | adverbCount | 29 | | adverbRatio | 0.027436140018921477 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003784295175023652 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 9.4 | | std | 6.4 | | cv | 0.681 | | sampleLengths | | 0 | 14 | | 1 | 16 | | 2 | 13 | | 3 | 16 | | 4 | 12 | | 5 | 9 | | 6 | 17 | | 7 | 14 | | 8 | 18 | | 9 | 14 | | 10 | 3 | | 11 | 2 | | 12 | 27 | | 13 | 4 | | 14 | 2 | | 15 | 4 | | 16 | 13 | | 17 | 10 | | 18 | 12 | | 19 | 11 | | 20 | 7 | | 21 | 5 | | 22 | 3 | | 23 | 5 | | 24 | 9 | | 25 | 13 | | 26 | 2 | | 27 | 4 | | 28 | 13 | | 29 | 22 | | 30 | 8 | | 31 | 6 | | 32 | 12 | | 33 | 3 | | 34 | 18 | | 35 | 18 | | 36 | 3 | | 37 | 10 | | 38 | 4 | | 39 | 3 | | 40 | 4 | | 41 | 1 | | 42 | 6 | | 43 | 3 | | 44 | 9 | | 45 | 7 | | 46 | 5 | | 47 | 8 | | 48 | 4 | | 49 | 9 |
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| 39.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3140495867768595 | | totalSentences | 121 | | uniqueOpeners | 38 | |
| 64.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 104 | | matches | | 0 | "Then, with a deep, metallic" | | 1 | "Just a gun, a dead" |
| | ratio | 0.019 | |
| 50.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 104 | | matches | | 0 | "Her breath came in sharp" | | 1 | "She’d picked up the tail" | | 2 | "He’d emerged from the hidden" | | 3 | "He moved with a furtive" | | 4 | "Her left wrist ached where" | | 5 | "She ignored it." | | 6 | "She’d filed the report." | | 7 | "She knew better now." | | 8 | "He was already scaling it." | | 9 | "He didn’t even glance back." | | 10 | "He vaulted the top and" | | 11 | "Her sharp jaw clenched." | | 12 | "She landed in a crouch" | | 13 | "He was thirty yards ahead," | | 14 | "He reached a rusted metal" | | 15 | "He didn’t knock." | | 16 | "He pulled something from his" | | 17 | "He slipped inside." | | 18 | "It was already closing." | | 19 | "Her mind raced." |
| | ratio | 0.423 | |
| 17.69% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 104 | | matches | | 0 | "Rain lashed the pavements of" | | 1 | "Detective Harlow Quinn’s boots hit" | | 2 | "Her breath came in sharp" | | 3 | "She’d picked up the tail" | | 4 | "The green neon had buzzed" | | 5 | "He’d emerged from the hidden" | | 6 | "He moved with a furtive" | | 7 | "Her left wrist ached where" | | 8 | "She ignored it." | | 9 | "She’d filed the report." | | 10 | "She knew better now." | | 11 | "The things that moved in" | | 12 | "The suspect darted into an" | | 13 | "Quinn followed, her hand going" | | 14 | "The alley was a canyon" | | 15 | "He was already scaling it." | | 16 | "He didn’t even glance back." | | 17 | "He vaulted the top and" | | 18 | "Quinn reached the fence, grabbed" | | 19 | "Her sharp jaw clenched." |
| | ratio | 0.885 | |
| 48.08% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 104 | | matches | | 0 | "Now he was leading her" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 1 | | matches | | 0 | "Then, with a deep, metallic groan, the door began to swing inward, revealing a steep, narrow staircase lit by flickering gas lamps that burned with a green flam…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |