| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1281 | | 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) | |
| 37.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1281 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "reverberated" | | 1 | "loomed" | | 2 | "weight" | | 3 | "traced" | | 4 | "pulse" | | 5 | "stark" | | 6 | "fractured" | | 7 | "electric" | | 8 | "could feel" | | 9 | "silence" | | 10 | "tension" | | 11 | "cascading" | | 12 | "flicker" |
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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 | 114 | | matches | (empty) | |
| 92.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1279 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1274 | | uniqueNames | 11 | | maxNameDensity | 0.71 | | worstName | "You" | | maxWindowNameDensity | 2.5 | | worstWindowName | "You" | | discoveredNames | | Brick | 1 | | Lane | 1 | | Aurora | 5 | | Eva | 1 | | London | 1 | | Underworld | 1 | | Lambeth | 1 | | Rory | 2 | | Avaros | 2 | | Lucien | 5 | | You | 9 |
| | persons | | 0 | "Aurora" | | 1 | "Eva" | | 2 | "Underworld" | | 3 | "Rory" | | 4 | "Lucien" | | 5 | "You" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "London" | | 3 | "Lambeth" | | 4 | "Avaros" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a sigh against her lips, warm" |
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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 | 1279 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 31.98 | | std | 18.04 | | cv | 0.564 | | sampleLengths | | 0 | 61 | | 1 | 68 | | 2 | 4 | | 3 | 37 | | 4 | 20 | | 5 | 34 | | 6 | 24 | | 7 | 45 | | 8 | 26 | | 9 | 38 | | 10 | 14 | | 11 | 68 | | 12 | 14 | | 13 | 36 | | 14 | 30 | | 15 | 21 | | 16 | 42 | | 17 | 51 | | 18 | 22 | | 19 | 21 | | 20 | 43 | | 21 | 58 | | 22 | 19 | | 23 | 58 | | 24 | 15 | | 25 | 21 | | 26 | 45 | | 27 | 49 | | 28 | 21 | | 29 | 6 | | 30 | 51 | | 31 | 27 | | 32 | 12 | | 33 | 41 | | 34 | 12 | | 35 | 17 | | 36 | 10 | | 37 | 61 | | 38 | 32 | | 39 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 218 | | matches | (empty) | |
| 92.73% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 114 | | ratio | 0.018 | | matches | | 0 | "The scent of him—cedar and something colder—filled the narrow entryway." | | 1 | "He didn't look at the mess of her life with judgment; he looked at it with an aching familiarity that made her skin prickle." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 580 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.029310344827586206 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.008620689655172414 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 11.22 | | std | 6.54 | | cv | 0.583 | | sampleLengths | | 0 | 20 | | 1 | 12 | | 2 | 14 | | 3 | 15 | | 4 | 7 | | 5 | 15 | | 6 | 22 | | 7 | 24 | | 8 | 4 | | 9 | 11 | | 10 | 18 | | 11 | 8 | | 12 | 20 | | 13 | 4 | | 14 | 3 | | 15 | 16 | | 16 | 11 | | 17 | 10 | | 18 | 5 | | 19 | 9 | | 20 | 6 | | 21 | 20 | | 22 | 9 | | 23 | 10 | | 24 | 1 | | 25 | 7 | | 26 | 18 | | 27 | 13 | | 28 | 5 | | 29 | 20 | | 30 | 14 | | 31 | 9 | | 32 | 24 | | 33 | 20 | | 34 | 4 | | 35 | 11 | | 36 | 10 | | 37 | 4 | | 38 | 16 | | 39 | 6 | | 40 | 14 | | 41 | 6 | | 42 | 13 | | 43 | 11 | | 44 | 8 | | 45 | 13 | | 46 | 2 | | 47 | 18 | | 48 | 22 | | 49 | 6 |
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| 28.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.2719298245614035 | | totalSentences | 114 | | uniqueOpeners | 31 | |
| 31.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 107 | | matches | | 0 | "Then why are you here?" |
| | ratio | 0.009 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 71 | | totalSentences | 107 | | matches | | 0 | "She pulled the door inward," | | 1 | "He leaned on the ivory-handled" | | 2 | "You look tired, Aurora." | | 3 | "She blocked the doorway, her" | | 4 | "She did not move to" | | 5 | "I don’t recall asking for" | | 6 | "He brought his gaze to" | | 7 | "He tapped his cane against" | | 8 | "I had business in Lambeth." | | 9 | "He didn’t wait for an" | | 10 | "He pushed a fraction further," | | 11 | "She moved back, her heels" | | 12 | "She swept a hand toward" | | 13 | "Her movement was sharp, defensive." | | 14 | "She needed the distance between" | | 15 | "He stepped fully inside, leaving" | | 16 | "He didn't look at the" | | 17 | "He reached out, his gloved" | | 18 | "He didn't make contact." | | 19 | "He withdrew, his amber eye" |
| | ratio | 0.664 | |
| 20.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 107 | | matches | | 0 | "The first deadbolt clicked back," | | 1 | "The second lock turned, followed" | | 2 | "Ptolemy wound around Aurora’s ankles," | | 3 | "She pulled the door inward," | | 4 | "The air in the stairwell" | | 5 | "Lucien loomed in the threshold," | | 6 | "He leaned on the ivory-handled" | | 7 | "The damp scent of London" | | 8 | "You look tired, Aurora." | | 9 | "She blocked the doorway, her" | | 10 | "The crescent scar on her" | | 11 | "She did not move to" | | 12 | "I don’t recall asking for" | | 13 | "Lucien shifted his weight." | | 14 | "The floorboards groaned." | | 15 | "He brought his gaze to" | | 16 | "He tapped his cane against" | | 17 | "The Underworld doesn't offer vacation" | | 18 | "I had business in Lambeth." | | 19 | "The path back seemed to" |
| | ratio | 0.879 | |
| 93.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 107 | | matches | | 0 | "If I’m just a risk," | | 1 | "Because the silence in my" |
| | ratio | 0.019 | |
| 86.17% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 5 | | matches | | 0 | "The first deadbolt clicked back, a heavy metallic rasp that reverberated through the cramped hallway of the Brick Lane flat." | | 1 | "He didn't look at the mess of her life with judgment; he looked at it with an aching familiarity that made her skin prickle." | | 2 | "It clattered to the floor, rolling toward the cat, who batted at it with a disinterested paw." | | 3 | "The shock of his touch was electric, a sudden grounding force that silenced the ringing in her head." | | 4 | "She moved her hand, her thumb brushing the jagged line of her scar near her wrist, then settling against the sharp angle of his jaw." |
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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 | |