| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1110 | | 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) | |
| 18.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1110 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "chill" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "pulsed" | | 4 | "velvet" | | 5 | "weight" | | 6 | "lilt" | | 7 | "sentinels" | | 8 | "sentinel" | | 9 | "pulse" | | 10 | "loomed" | | 11 | "silence" |
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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 | 2 | | 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 | 103 | | 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 | 1 | | markdownWords | 5 | | totalWords | 1097 | | ratio | 0.005 | | matches | | 0 | "Off-books medical care. Supernatural clientele." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 80.07% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1001 | | uniqueNames | 13 | | maxNameDensity | 1.4 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 14 | | Maglite | 1 | | Morris | 3 | | Tube | 1 | | Veil | 1 | | Market | 2 | | Victorian | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 7 | | Sevillan | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Market" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" |
| | places | (empty) | | globalScore | 0.801 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like they belonged to the same cen" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 4 | | per1kWords | 3.646 | | wordCount | 1097 | | matches | | 0 | "not the steady white of fluorescents but a warm, unsteady amber, as if the walls themselves smoldered" | | 1 | "not toward the surgery’s rear exit but sideways, into a shadow" | | 2 | "not toward her, but toward the bone token in Herrera’s hand" | | 3 | "not a smile, but recognition" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 103 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 31.34 | | std | 19.46 | | cv | 0.621 | | sampleLengths | | 0 | 69 | | 1 | 18 | | 2 | 58 | | 3 | 48 | | 4 | 36 | | 5 | 4 | | 6 | 55 | | 7 | 11 | | 8 | 65 | | 9 | 3 | | 10 | 36 | | 11 | 61 | | 12 | 37 | | 13 | 38 | | 14 | 19 | | 15 | 37 | | 16 | 8 | | 17 | 55 | | 18 | 32 | | 19 | 30 | | 20 | 10 | | 21 | 26 | | 22 | 36 | | 23 | 60 | | 24 | 18 | | 25 | 55 | | 26 | 13 | | 27 | 21 | | 28 | 9 | | 29 | 30 | | 30 | 13 | | 31 | 4 | | 32 | 45 | | 33 | 11 | | 34 | 26 |
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| 97.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 95 | | matches | | 0 | "was outnumbered" | | 1 | "were written" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 172 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 103 | | ratio | 0.117 | | matches | | 0 | "Her focus had narrowed to the figure thirty yards ahead—a slim shape in a waxed jacket, feet kicking up spray as it veered sharp into an alley clogged with dumpsters." | | 1 | "The jacket snagged for one heartbeat on a rusted fire escape—enough." | | 2 | "A faint green glow pulsed once against the tunnel walls—the suspect’s torch, maybe." | | 3 | "She moved by sound, by instinct, the way Morris had taught her before he—" | | 4 | "The market didn’t pause for her—merchants with filed teeth and patrons in Victorian mourning veils merely shifted aside like water around a stone." | | 5 | "The suspect slammed through the third—ornate iron, studded with blackened rivets—and a sharp cry echoed from within." | | 6 | "Tomás Herrera—a name from her caseboard, a former paramedic who’d slipped off the grid after losing his license." | | 7 | "“Detective.” Herrera’s voice carried a Sevillan lilt, calm even as his patient—a woman with scales where her skin should be—whimpered on the table." | | 8 | "Already, a subsonic hum prickled along her spine—the wardings of the Market noticing her uninvited presence." | | 9 | "Quinn lunged—and hit a wall of cold air that reeked of clove and old bones." | | 10 | "Her military training screamed tactical retreat—she was outnumbered, out-grounded, and the rules of engagement were written in a language she didn’t speak." | | 11 | "Herrera’s lips twitched—not a smile, but recognition." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1020 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.013725490196078431 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.00196078431372549 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 10.65 | | std | 6.04 | | cv | 0.567 | | sampleLengths | | 0 | 19 | | 1 | 16 | | 2 | 4 | | 3 | 30 | | 4 | 5 | | 5 | 13 | | 6 | 14 | | 7 | 11 | | 8 | 12 | | 9 | 15 | | 10 | 4 | | 11 | 2 | | 12 | 11 | | 13 | 5 | | 14 | 16 | | 15 | 7 | | 16 | 9 | | 17 | 13 | | 18 | 9 | | 19 | 14 | | 20 | 4 | | 21 | 15 | | 22 | 5 | | 23 | 13 | | 24 | 22 | | 25 | 11 | | 26 | 15 | | 27 | 23 | | 28 | 13 | | 29 | 14 | | 30 | 3 | | 31 | 12 | | 32 | 13 | | 33 | 3 | | 34 | 6 | | 35 | 2 | | 36 | 20 | | 37 | 18 | | 38 | 23 | | 39 | 9 | | 40 | 4 | | 41 | 17 | | 42 | 5 | | 43 | 2 | | 44 | 10 | | 45 | 11 | | 46 | 17 | | 47 | 4 | | 48 | 7 | | 49 | 8 |
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| 48.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.33980582524271846 | | totalSentences | 103 | | uniqueOpeners | 35 | |
| 74.91% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 89 | | matches | | 0 | "Then she saw it: a" | | 1 | "Already, a subsonic hum prickled" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 89 | | matches | | 0 | "She didn’t feel it." | | 1 | "Her focus had narrowed to" | | 2 | "She didn’t shout a warning." | | 3 | "Her worn leather watch bit" | | 4 | "She descended without breaking stride." | | 5 | "She moved by sound, by" | | 6 | "She crushed that thought." | | 7 | "She stood on a grated" | | 8 | "She’d read the files, the" | | 9 | "Her jaw tightened." | | 10 | "She’d laughed at those reports" | | 11 | "She lost visual on the" | | 12 | "She nudged the iron door" | | 13 | "She’d seen his photo pinned" | | 14 | "He held a bone token" | | 15 | "She heard the truth in" | | 16 | "Her military training screamed tactical" | | 17 | "She locked eyes with the" |
| | ratio | 0.202 | |
| 38.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 89 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s salt-and-pepper hair" | | 2 | "She didn’t feel it." | | 3 | "Her focus had narrowed to" | | 4 | "She didn’t shout a warning." | | 5 | "Her worn leather watch bit" | | 6 | "The jacket snagged for one" | | 7 | "Quinn closed the gap with" | | 8 | "The suspect twisted free and" | | 9 | "A dead-end service entrance." | | 10 | "Quinn’s hand found the railing," | | 11 | "She descended without breaking stride." | | 12 | "The steps bottomed out into" | | 13 | "The suspect’s footsteps echoed ahead," | | 14 | "The air thickened, tasting of" | | 15 | "A faint green glow pulsed" | | 16 | "Quinn palmed her own Maglite" | | 17 | "She moved by sound, by" | | 18 | "She crushed that thought." | | 19 | "The tunnel terminated at a" |
| | ratio | 0.843 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 2 | | matches | | 0 | "The rain came down in sheets that turned the Camden backstreets into blurred mirrors of neon and sodium glare." | | 1 | "Light bled through the gap, not the steady white of fluorescents but a warm, unsteady amber, as if the walls themselves smoldered." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "the sentinel hissed, its voice a chorus of whispers" |
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| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "the sentinel hissed (hiss)" |
| | dialogueSentences | 11 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0.5 | | effectiveRatio | 0.182 | |