| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1251 | | 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) | |
| 24.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1251 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "silence" | | 1 | "rhythmic" | | 2 | "fluttered" | | 3 | "perfect" | | 4 | "standard" | | 5 | "vibrated" | | 6 | "traced" | | 7 | "scanned" | | 8 | "etched" | | 9 | "gloom" | | 10 | "firmly" | | 11 | "flicker" | | 12 | "oppressive" | | 13 | "chill" | | 14 | "processed" |
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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 | 1 | | narrationSentences | 107 | | matches | | |
| 89.45% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1249 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1023 | | uniqueNames | 11 | | maxNameDensity | 0.78 | | worstName | "Miller" | | maxWindowNameDensity | 2 | | worstWindowName | "Miller" | | discoveredNames | | Harlow | 6 | | Quinn | 7 | | Camden | 1 | | Inspector | 1 | | Miller | 8 | | Morris | 2 | | London | 1 | | Veil | 3 | | Market | 1 | | Tube | 1 | | Compass | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Inspector" | | 3 | "Miller" | | 4 | "Morris" | | 5 | "Compass" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 4 | | matches | | 0 | "looked like oil on water, but it didn't s" | | 1 | "looked like heat haze over a summer highw" | | 2 | "sigils that seemed to glow faintly in the dark" | | 3 | "chanting that seemed to vibrate in her marrow" |
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| 39.87% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.601 | | wordCount | 1249 | | matches | | 0 | "not the harsh white of a police torch, but the warm, amber glow of lanterns" | | 1 | "not by the body, but by the spot" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 21.53 | | std | 17.01 | | cv | 0.79 | | sampleLengths | | 0 | 54 | | 1 | 34 | | 2 | 26 | | 3 | 3 | | 4 | 52 | | 5 | 25 | | 6 | 49 | | 7 | 4 | | 8 | 2 | | 9 | 11 | | 10 | 51 | | 11 | 8 | | 12 | 24 | | 13 | 46 | | 14 | 6 | | 15 | 2 | | 16 | 7 | | 17 | 36 | | 18 | 9 | | 19 | 8 | | 20 | 35 | | 21 | 54 | | 22 | 28 | | 23 | 6 | | 24 | 5 | | 25 | 60 | | 26 | 21 | | 27 | 3 | | 28 | 47 | | 29 | 52 | | 30 | 3 | | 31 | 25 | | 32 | 41 | | 33 | 7 | | 34 | 19 | | 35 | 4 | | 36 | 14 | | 37 | 30 | | 38 | 10 | | 39 | 25 | | 40 | 12 | | 41 | 13 | | 42 | 10 | | 43 | 24 | | 44 | 36 | | 45 | 6 | | 46 | 5 | | 47 | 6 | | 48 | 40 | | 49 | 10 |
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| 92.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 107 | | matches | | 0 | "were curled" | | 1 | "was carved" | | 2 | "was etched" | | 3 | "was gone" |
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| 84.39% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 173 | | matches | | 0 | "was jogging" | | 1 | "was still, pointing" | | 2 | "was using" |
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| 76.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 129 | | ratio | 0.023 | | matches | | 0 | "She noticed a shimmer on the ground—a thin, iridescent film that looked like oil on water, but it didn't smear when she nudged it with her pen." | | 1 | "The department had called it a psychotic break; the coroner had called it an anomaly." | | 2 | "Through the haze, she saw a flicker of light—not the harsh white of a police torch, but the warm, amber glow of lanterns." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1027 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, pressing against her" |
| | adverbCount | 17 | | adverbRatio | 0.016553067185978577 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008763388510223954 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 9.68 | | std | 5.6 | | cv | 0.579 | | sampleLengths | | 0 | 13 | | 1 | 15 | | 2 | 12 | | 3 | 10 | | 4 | 4 | | 5 | 18 | | 6 | 16 | | 7 | 12 | | 8 | 14 | | 9 | 3 | | 10 | 3 | | 11 | 11 | | 12 | 17 | | 13 | 2 | | 14 | 3 | | 15 | 16 | | 16 | 3 | | 17 | 22 | | 18 | 3 | | 19 | 10 | | 20 | 7 | | 21 | 6 | | 22 | 10 | | 23 | 13 | | 24 | 4 | | 25 | 2 | | 26 | 11 | | 27 | 8 | | 28 | 11 | | 29 | 11 | | 30 | 21 | | 31 | 8 | | 32 | 6 | | 33 | 18 | | 34 | 9 | | 35 | 8 | | 36 | 27 | | 37 | 2 | | 38 | 6 | | 39 | 2 | | 40 | 7 | | 41 | 8 | | 42 | 7 | | 43 | 21 | | 44 | 9 | | 45 | 8 | | 46 | 10 | | 47 | 13 | | 48 | 8 | | 49 | 4 |
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| 37.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2713178294573643 | | totalSentences | 129 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 43.16% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 95 | | matches | | 0 | "She checked the worn leather" | | 1 | "He looked tired, his tie" | | 2 | "She knelt beside the victim," | | 3 | "She ignored the blood and" | | 4 | "She pried the stiff digits" | | 5 | "It was carved with an" | | 6 | "She pointed her light toward" | | 7 | "She walked the perimeter of" | | 8 | "She noticed a shimmer on" | | 9 | "She traced the arc of" | | 10 | "It didn't project outward from" | | 11 | "It flowed inward, as if" | | 12 | "It matched the carving on" | | 13 | "She remembered the file on" | | 14 | "She stepped further into the" | | 15 | "It looked like heat haze" | | 16 | "She reached into her coat" | | 17 | "It spun violently, a blurred" | | 18 | "She stepped forward, her boot" | | 19 | "She saw stalls of carved" |
| | ratio | 0.442 | |
| 12.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 95 | | matches | | 0 | "Detective Harlow Quinn stepped over" | | 1 | "Dust motes danced in the" | | 2 | "The subterranean silence felt heavy," | | 3 | "She checked the worn leather" | | 4 | "A perimeter of yellow tape" | | 5 | "Detective Inspector Miller stood over" | | 6 | "He looked tired, his tie" | | 7 | "Harlow didn't answer." | | 8 | "She knelt beside the victim," | | 9 | "The man lay sprawled across" | | 10 | "Blood had pooled in a" | | 11 | "Quinn leaned closer." | | 12 | "She ignored the blood and" | | 13 | "The fingers were curled, clutching" | | 14 | "She pried the stiff digits" | | 15 | "A small, bleached fragment of" | | 16 | "It was carved with an" | | 17 | "She pointed her light toward" | | 18 | "The rails vanished into a" | | 19 | "The light simply stopped, swallowed" |
| | ratio | 0.895 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 3 | | matches | | 0 | "A perimeter of yellow tape fluttered in a draft that smelled of ozone and wet copper." | | 1 | "She noticed a shimmer on the ground—a thin, iridescent film that looked like oil on water, but it didn't smear when she nudged it with her pen." | | 2 | "The casing bore a heavy patina of verdigris, and the face was etched with protective sigils that seemed to glow faintly in the dark." |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice flat" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 5 | | fancyTags | | 0 | "Miller sighed (sigh)" | | 1 | "she whispered (whisper)" | | 2 | "Harlow noted (note)" | | 3 | "she muttered (mutter)" | | 4 | "she whispered (whisper)" |
| | dialogueSentences | 32 | | tagDensity | 0.281 | | leniency | 0.563 | | rawRatio | 0.556 | | effectiveRatio | 0.313 | |