| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 86.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1096 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "suddenly" | | 1 | "very" | | 2 | "slowly" |
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| 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) | |
| 8.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1096 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "pulse" | | 1 | "gloom" | | 2 | "rhythmic" | | 3 | "weight" | | 4 | "vibrated" | | 5 | "echoed" | | 6 | "silence" | | 7 | "familiar" | | 8 | "etched" | | 9 | "loomed" | | 10 | "scanned" | | 11 | "shimmered" | | 12 | "velvet" | | 13 | "sanctuary" | | 14 | "predator" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 71 | | matches | (empty) | |
| 82.49% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 71 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1094 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1094 | | uniqueNames | 13 | | maxNameDensity | 0.55 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Soho | 1 | | Harlow | 2 | | Quinn | 6 | | Herrera | 2 | | Saint | 1 | | Christopher | 1 | | Detective | 3 | | Underground | 1 | | Metropolitan | 1 | | Police | 1 | | Morris | 1 | | Tomás | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Underground" | | 6 | "Morris" | | 7 | "Tomás" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 27.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 3 | | matches | | 0 | "seemed older suddenly, the lines on his face deepened by the subterranean light" | | 1 | "looked like a man returning home to a mas" | | 2 | "felt like a cold stone against her ches" |
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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 | 1094 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 71 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 47.57 | | std | 29.46 | | cv | 0.619 | | sampleLengths | | 0 | 80 | | 1 | 61 | | 2 | 9 | | 3 | 42 | | 4 | 17 | | 5 | 56 | | 6 | 71 | | 7 | 68 | | 8 | 77 | | 9 | 19 | | 10 | 14 | | 11 | 73 | | 12 | 11 | | 13 | 66 | | 14 | 12 | | 15 | 82 | | 16 | 13 | | 17 | 73 | | 18 | 60 | | 19 | 10 | | 20 | 81 | | 21 | 10 | | 22 | 89 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 175 | | matches | (empty) | |
| 22.13% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 71 | | ratio | 0.042 | | matches | | 0 | "He didn't look like a man fleeing an arrest; he looked like a man returning home to a master he feared far more than the police." | | 1 | "Yet, the ghost of the evidence he held—a vial of something that defied the laws of biology—dangled just out of reach." | | 2 | "They didn't watch her with the curious stare of civilians; they looked at her like a predator watching a rabbit stumble into a lion’s den." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1100 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, iron-wrought gate" |
| | adverbCount | 34 | | adverbRatio | 0.03090909090909091 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.00909090909090909 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 15.41 | | std | 7.66 | | cv | 0.497 | | sampleLengths | | 0 | 19 | | 1 | 20 | | 2 | 12 | | 3 | 15 | | 4 | 14 | | 5 | 5 | | 6 | 21 | | 7 | 9 | | 8 | 26 | | 9 | 3 | | 10 | 6 | | 11 | 21 | | 12 | 21 | | 13 | 8 | | 14 | 9 | | 15 | 18 | | 16 | 19 | | 17 | 19 | | 18 | 19 | | 19 | 24 | | 20 | 28 | | 21 | 16 | | 22 | 22 | | 23 | 9 | | 24 | 21 | | 25 | 16 | | 26 | 14 | | 27 | 16 | | 28 | 10 | | 29 | 21 | | 30 | 2 | | 31 | 14 | | 32 | 3 | | 33 | 10 | | 34 | 4 | | 35 | 4 | | 36 | 32 | | 37 | 18 | | 38 | 19 | | 39 | 2 | | 40 | 9 | | 41 | 18 | | 42 | 19 | | 43 | 3 | | 44 | 26 | | 45 | 5 | | 46 | 7 | | 47 | 20 | | 48 | 22 | | 49 | 14 |
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| 65.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4507042253521127 | | totalSentences | 71 | | uniqueOpeners | 32 | |
| 49.02% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 68 | | matches | | 0 | "Only solid, damp, weeping concrete" |
| | ratio | 0.015 | |
| 43.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 68 | | matches | | 0 | "Her gaze remained locked on" | | 1 | "He rounded a corner, his" | | 2 | "She gained ground, her sharp" | | 3 | "He reached instinctively for the" | | 4 | "You should have stayed in" | | 5 | "He shoved a heavy metal" | | 6 | "She grabbed the edge of" | | 7 | "She ignored the warning bells" | | 8 | "She descended the concrete stairs," | | 9 | "She pulled her flashlight from" | | 10 | "It landed momentarily on a" | | 11 | "We can do this in" | | 12 | "Your choice, Herrera." | | 13 | "I don't think you realize" | | 14 | "She realized then that there" | | 15 | "I didn't come this far" | | 16 | "He took a step toward" | | 17 | "He drew a small, jagged" | | 18 | "It's a sanctuary you aren't" | | 19 | "He dove through the gap" |
| | ratio | 0.441 | |
| 26.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 68 | | matches | | 0 | "Rain slicked the London pavement," | | 1 | "Detective Harlow Quinn’s boots hammered" | | 2 | "Her gaze remained locked on" | | 3 | "Tomás Herrera moved with a" | | 4 | "He rounded a corner, his" | | 5 | "Quinn checked her left wrist." | | 6 | "The leather strap of her" | | 7 | "She gained ground, her sharp" | | 8 | "Hands where I can see" | | 9 | "Tomás spun, his breath hitching" | | 10 | "He reached instinctively for the" | | 11 | "You should have stayed in" | | 12 | "The law doesn't hold much" | | 13 | "He shoved a heavy metal" | | 14 | "Quinn surged forward, her hand" | | 15 | "She grabbed the edge of" | | 16 | "The path ahead plunged downward" | | 17 | "A flickering torchlight hummed deep" | | 18 | "She ignored the warning bells" | | 19 | "She descended the concrete stairs," |
| | ratio | 0.868 | |
| 73.53% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 68 | | matches | | | ratio | 0.015 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 8 | | matches | | 0 | "Tomás spun, his breath hitching as he scrambled backward, eyes darting toward the crumbling brick façade of an abandoned service stairwell." | | 1 | "He shoved a heavy metal door inward, the hinges shrieking a protest that vibrated through the damp air." | | 2 | "She ignored the warning bells clanging in her mind, the military precision of her training screaming for backup, for a perimeter, for anything other than this b…" | | 3 | "She descended the concrete stairs, her heels clicking a staccato rhythm that echoed into the abyss." | | 4 | "The air grew thick, vibrating with a low, dissonant tone that rattled her teeth." | | 5 | "Hundreds of items hung from ropes made of woven hair: glass vials filled with shifting smoke, rusted blades pulsing with internal heat, and books bound in scale…" | | 6 | "He took a step toward the gate, his boots crunching on a layer of ash covering the floor." | | 7 | "Yet, the ghost of the evidence he held—a vial of something that defied the laws of biology—dangled just out of reach." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |