| 85.71% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 2 | | adverbTags | | 0 | "Then the suspect said [Then]" | | 1 | "he said again [again]" |
| | dialogueSentences | 35 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.2 | | effectiveRatio | 0.114 | |
| 94.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1730 | | 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) | |
| 68.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1730 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "pulsed" | | 1 | "familiar" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "velvet" | | 5 | "etched" | | 6 | "flicked" |
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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 | 188 | | matches | (empty) | |
| 89.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 3 | | narrationSentences | 188 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 213 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1724 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1591 | | uniqueNames | 15 | | maxNameDensity | 2.14 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 34 | | Raven | 2 | | Nest | 3 | | Tomás | 2 | | Herrera | 2 | | Saint | 2 | | Christopher | 2 | | Tube | 1 | | Camden | 1 | | Morris | 5 | | Hackney | 1 | | London | 1 | | Market | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" | | 9 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Hackney" | | 2 | "London" |
| | globalScore | 0.431 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 114 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like a crow choking" |
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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 | 1724 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 213 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 128 | | mean | 13.47 | | std | 15.37 | | cv | 1.141 | | sampleLengths | | 0 | 7 | | 1 | 45 | | 2 | 2 | | 3 | 57 | | 4 | 15 | | 5 | 71 | | 6 | 4 | | 7 | 53 | | 8 | 12 | | 9 | 3 | | 10 | 8 | | 11 | 5 | | 12 | 33 | | 13 | 9 | | 14 | 5 | | 15 | 34 | | 16 | 8 | | 17 | 27 | | 18 | 7 | | 19 | 7 | | 20 | 6 | | 21 | 2 | | 22 | 62 | | 23 | 5 | | 24 | 5 | | 25 | 5 | | 26 | 18 | | 27 | 4 | | 28 | 4 | | 29 | 21 | | 30 | 6 | | 31 | 52 | | 32 | 1 | | 33 | 29 | | 34 | 15 | | 35 | 26 | | 36 | 12 | | 37 | 8 | | 38 | 54 | | 39 | 3 | | 40 | 6 | | 41 | 18 | | 42 | 3 | | 43 | 8 | | 44 | 1 | | 45 | 6 | | 46 | 4 | | 47 | 2 | | 48 | 7 | | 49 | 5 |
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| 92.20% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 188 | | matches | | 0 | "were blacked" | | 1 | "been brewed" | | 2 | "been found" | | 3 | "been trained" | | 4 | "were cracked" | | 5 | "been etched" | | 6 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 275 | | matches | | |
| 89.20% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 213 | | ratio | 0.019 | | matches | | 0 | "Detective Harlow Quinn saw the suspect’s reflection break across it—long coat, narrow shoulders, one hand clamped around a canvas satchel." | | 1 | "She had found no suspect there—only dust, a concealed passage, and the faint chemical smell of something that had not been brewed in any licensed laboratory." | | 2 | "One part—the trained, decorated detective—said wait for backup." | | 3 | "She could drag him back—if there was still a way back—and call in every unit in London." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1601 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.019987507807620236 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006246096189881324 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 213 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 213 | | mean | 8.09 | | std | 5.77 | | cv | 0.713 | | sampleLengths | | 0 | 7 | | 1 | 20 | | 2 | 25 | | 3 | 2 | | 4 | 8 | | 5 | 3 | | 6 | 3 | | 7 | 16 | | 8 | 10 | | 9 | 17 | | 10 | 11 | | 11 | 4 | | 12 | 22 | | 13 | 18 | | 14 | 31 | | 15 | 4 | | 16 | 15 | | 17 | 26 | | 18 | 12 | | 19 | 12 | | 20 | 3 | | 21 | 5 | | 22 | 3 | | 23 | 5 | | 24 | 12 | | 25 | 4 | | 26 | 17 | | 27 | 9 | | 28 | 5 | | 29 | 4 | | 30 | 9 | | 31 | 7 | | 32 | 14 | | 33 | 8 | | 34 | 7 | | 35 | 9 | | 36 | 11 | | 37 | 7 | | 38 | 3 | | 39 | 4 | | 40 | 6 | | 41 | 2 | | 42 | 19 | | 43 | 20 | | 44 | 4 | | 45 | 4 | | 46 | 15 | | 47 | 5 | | 48 | 5 | | 49 | 5 |
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| 48.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.33490566037735847 | | totalSentences | 212 | | uniqueOpeners | 71 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 170 | | matches | | 0 | "Then the man with the" | | 1 | "Somewhere below, footsteps struck tile." | | 2 | "Then came a sound like" | | 3 | "Then the suspect said," | | 4 | "Then something older." | | 5 | "Then nothing she could read." | | 6 | "Then a voice spoke in" |
| | ratio | 0.041 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 170 | | matches | | 0 | "He cut between two parked" | | 1 | "Her boots struck the pavement" | | 2 | "She had believed fewer." | | 3 | "He had lied badly." | | 4 | "She had found no suspect" | | 5 | "She ignored him." | | 6 | "His fear had the frantic," | | 7 | "He plunged down a narrow" | | 8 | "She reached it as the" | | 9 | "She knew the maps." | | 10 | "She knew the closures." | | 11 | "She knew the city’s buried" | | 12 | "She tried again." | | 13 | "He had vanished during a" | | 14 | "His body had never been" | | 15 | "His last transmission had contained" | | 16 | "She had spent six months" | | 17 | "Her instincts divided cleanly." | | 18 | "Her footsteps echoed." | | 19 | "Their light pooled in colors" |
| | ratio | 0.235 | |
| 86.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 170 | | matches | | 0 | "Detective Harlow Quinn saw the" | | 1 | "He cut between two parked" | | 2 | "Her boots struck the pavement" | | 3 | "The watch’s leather strap had" | | 4 | "Quinn had heard stranger things" | | 5 | "She had believed fewer." | | 6 | "The green neon raven above" | | 7 | "The Nest’s windows were blacked" | | 8 | "He had lied badly." | | 9 | "Quinn had caught him before" | | 10 | "She had found no suspect" | | 11 | "The cyclist shouted after her." | | 12 | "She ignored him." | | 13 | "Quinn saw his face for" | | 14 | "His fear had the frantic," | | 15 | "He plunged down a narrow" | | 16 | "Quinn followed into the darkness." | | 17 | "The city fell away." | | 18 | "Traffic noise dulled beneath the" | | 19 | "Water poured from gutters in" |
| | ratio | 0.747 | |
| 58.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 170 | | matches | | 0 | "Now Quinn rounded the corner" | | 1 | "Now the suspect descended into" |
| | ratio | 0.012 | |
| 85.25% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 5 | | matches | | 0 | "She had found no suspect there—only dust, a concealed passage, and the faint chemical smell of something that had not been brewed in any licensed laboratory." | | 1 | "She had spent six months searching stalls, warehouses, pawnshops, and every criminal network that would tolerate her questions." | | 2 | "Rain and diesel fumes faded, replaced by wet stone, old iron, and a sweet metallic odor that caught at the back of her throat." | | 3 | "Market stalls crowded the abandoned platform, their canopies stitched from velvet, oilcloth, and something that looked disturbingly like skin." | | 4 | "She thought of the hidden room, the illegal medicines, the lies that formed a careful wall around this place." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 64.29% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 3 | | fancyTags | | 0 | "the informant had (have)" | | 1 | "she snapped (snap)" | | 2 | "The woman in red whispered (whisper)" |
| | dialogueSentences | 35 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.3 | | effectiveRatio | 0.171 | |