| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.314 | | leniency | 0.627 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1674 | | 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) | |
| 52.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1674 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "velvet" | | 2 | "echoing" | | 3 | "pristine" | | 4 | "etched" | | 5 | "traced" | | 6 | "silk" | | 7 | "chaotic" | | 8 | "looming" | | 9 | "flickered" | | 10 | "pulse" | | 11 | "scanning" | | 12 | "navigate" | | 13 | "shimmered" |
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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 | 96 | | matches | (empty) | |
| 83.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1664 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1091 | | uniqueNames | 18 | | maxNameDensity | 2.38 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 26 | | Tube | 1 | | Miller | 13 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 | | Sergeant | 2 | | Kowalski | 1 | | Eva | 12 | | Slumping | 1 | | Morris | 1 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Sergeant" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "British" |
| | globalScore | 0.308 | | windowScore | 0 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 2 | | matches | | 0 | "sigils that seemed to vibrate under the beam of her torch" | | 1 | "looked like a student who had wandered in" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.601 | | wordCount | 1664 | | matches | | 0 | "not blood, but a translucent film" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 24.47 | | std | 19.06 | | cv | 0.779 | | sampleLengths | | 0 | 79 | | 1 | 49 | | 2 | 3 | | 3 | 34 | | 4 | 7 | | 5 | 15 | | 6 | 26 | | 7 | 76 | | 8 | 42 | | 9 | 61 | | 10 | 30 | | 11 | 20 | | 12 | 5 | | 13 | 8 | | 14 | 18 | | 15 | 18 | | 16 | 56 | | 17 | 26 | | 18 | 47 | | 19 | 2 | | 20 | 11 | | 21 | 32 | | 22 | 55 | | 23 | 9 | | 24 | 6 | | 25 | 60 | | 26 | 13 | | 27 | 14 | | 28 | 12 | | 29 | 14 | | 30 | 23 | | 31 | 42 | | 32 | 12 | | 33 | 4 | | 34 | 15 | | 35 | 14 | | 36 | 4 | | 37 | 4 | | 38 | 29 | | 39 | 53 | | 40 | 35 | | 41 | 8 | | 42 | 22 | | 43 | 10 | | 44 | 14 | | 45 | 40 | | 46 | 13 | | 47 | 37 | | 48 | 9 | | 49 | 10 |
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| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 96 | | matches | | 0 | "were twisted" | | 1 | "been crushed" |
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| 90.71% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 183 | | matches | | 0 | "wasn't pointing" | | 1 | "was bleeding" | | 2 | "was coming" |
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| 99.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 132 | | ratio | 0.015 | | matches | | 0 | "She found a faint, shimmering residue on the vic's throat—not blood, but a translucent film that smelled of mountain air and burnt sugar." | | 1 | "Eva Kowalski didn't look like a killer; she looked like a student who had wandered into a nightmare." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1068 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, leather-bound tome" |
| | adverbCount | 19 | | adverbRatio | 0.017790262172284643 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003745318352059925 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 12.61 | | std | 7.39 | | cv | 0.586 | | sampleLengths | | 0 | 13 | | 1 | 22 | | 2 | 20 | | 3 | 24 | | 4 | 23 | | 5 | 6 | | 6 | 1 | | 7 | 19 | | 8 | 3 | | 9 | 10 | | 10 | 11 | | 11 | 13 | | 12 | 7 | | 13 | 15 | | 14 | 26 | | 15 | 9 | | 16 | 15 | | 17 | 13 | | 18 | 13 | | 19 | 10 | | 20 | 16 | | 21 | 6 | | 22 | 3 | | 23 | 24 | | 24 | 9 | | 25 | 9 | | 26 | 17 | | 27 | 9 | | 28 | 5 | | 29 | 21 | | 30 | 9 | | 31 | 21 | | 32 | 20 | | 33 | 5 | | 34 | 5 | | 35 | 3 | | 36 | 18 | | 37 | 11 | | 38 | 7 | | 39 | 7 | | 40 | 7 | | 41 | 25 | | 42 | 4 | | 43 | 13 | | 44 | 9 | | 45 | 17 | | 46 | 3 | | 47 | 7 | | 48 | 23 | | 49 | 14 |
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| 60.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.40458015267175573 | | totalSentences | 131 | | uniqueOpeners | 53 | |
| 37.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 90 | | matches | | 0 | "Just a look of absolute" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 90 | | matches | | 0 | "She ignored the neon glare" | | 1 | "Her focus remained locked on" | | 2 | "Her salt-and-pepper hair, shorn close" | | 3 | "He wiped condensation from his" | | 4 | "They descended into the belly" | | 5 | "It wasn't empty." | | 6 | "She pulled out a small" | | 7 | "It wasn't pointing north." | | 8 | "She traced the perimeter of" | | 9 | "She found a faint, shimmering" | | 10 | "She stood, her gaze drifting" | | 11 | "She walked toward the stall," | | 12 | "She didn't draw it." | | 13 | "She hugged a worn leather" | | 14 | "She noted the way Eva’s" | | 15 | "She didn't find a hammer." | | 16 | "She pulled out a heavy," | | 17 | "She looked at Miller, then" | | 18 | "They called it a heart" | | 19 | "They called it a fluke." |
| | ratio | 0.289 | |
| 32.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 90 | | matches | | 0 | "Detective Harlow Quinn stepped over" | | 1 | "She ignored the neon glare" | | 2 | "Her focus remained locked on" | | 3 | "Quinn checked her worn leather" | | 4 | "Her salt-and-pepper hair, shorn close" | | 5 | "The voice drifted up from" | | 6 | "Sergeant Miller emerged, his uniform" | | 7 | "He wiped condensation from his" | | 8 | "Miller shook his head, gesturing" | | 9 | "They descended into the belly" | | 10 | "The air shifted, growing thick" | | 11 | "Dust motes danced in the" | | 12 | "This was the threshold of" | | 13 | "The platform opened up before" | | 14 | "It wasn't empty." | | 15 | "Stalls made of salvaged scrap" | | 16 | "Quinn crouched beside the corpse," | | 17 | "The victim was male, mid-thirties," | | 18 | "Miller spat, kicking a piece" | | 19 | "Quinn pulled a pair of" |
| | ratio | 0.856 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 90 | | matches | | 0 | "To him, it was just" | | 1 | "To Quinn, it was a" | | 2 | "Yet, his limbs were twisted" |
| | ratio | 0.033 | |
| 65.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 5 | | matches | | 0 | "Blue strobe lights from the patrol cars sliced through the mist, illuminating the yellow tape that cordoned off the descent into the earth." | | 1 | "To Quinn, it was a crime scene that defied every rule of the Metropolitan Police handbook." | | 2 | "Yet, his limbs were twisted at impossible angles, his chest cavity caved in as if crushed by an invisible hydraulic press." | | 3 | "The casing bore a heavy patina of verdigris, its face etched with jagged, circular sigils that seemed to vibrate under the beam of her torch." | | 4 | "She found a faint, shimmering residue on the vic's throat—not blood, but a translucent film that smelled of mountain air and burnt sugar." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 5 | | matches | | 0 | "Miller shrugged, his radio crackling with static" | | 1 | "Quinn said, her voice dropping to a low, dangerous register" | | 2 | "Quinn read, her eyes scanning the page" | | 3 | "Quinn snapped, her military precision returning as she squared her shoulders" | | 4 | "Quinn called back, not turning around" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 9 | | fancyTags | | 0 | "Miller spat (spit)" | | 1 | "Miller grumbled (grumble)" | | 2 | "Quinn corrected (correct)" | | 3 | "Miller laughed (laugh)" | | 4 | "Quinn snapped (snap)" | | 5 | "Miller shouted (shout)" | | 6 | "Quinn called back (call back)" | | 7 | "Eva whispered (whisper)" | | 8 | "Quinn ordered (order)" |
| | dialogueSentences | 51 | | tagDensity | 0.196 | | leniency | 0.392 | | rawRatio | 0.9 | | effectiveRatio | 0.353 | |