| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 84.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 957 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "very" | | 2 | "sharply" |
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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) | |
| 5.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 957 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "footsteps" | | 2 | "navigating" | | 3 | "flickered" | | 4 | "loomed" | | 5 | "echoing" | | 6 | "silence" | | 7 | "vibrated" | | 8 | "chaotic" | | 9 | "velvet" | | 10 | "weight" | | 11 | "cacophony" | | 12 | "scanning" | | 13 | "maw" | | 14 | "silk" |
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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 | 87 | | matches | (empty) | |
| 93.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 957 | | 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 | 17 | | wordCount | 949 | | uniqueNames | 9 | | maxNameDensity | 0.95 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | London | 1 | | Camden | 1 | | Tube | 1 | | Metropolitan | 1 | | Police | 1 | | Quinn | 9 |
| | persons | | | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Metropolitan" |
| | globalScore | 1 | | windowScore | 1 | |
| 73.08% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a threat" | | 1 | "wave that seemed to swallow her voice" |
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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 | 957 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 27.34 | | std | 16.94 | | cv | 0.62 | | sampleLengths | | 0 | 16 | | 1 | 39 | | 2 | 61 | | 3 | 34 | | 4 | 37 | | 5 | 29 | | 6 | 34 | | 7 | 46 | | 8 | 10 | | 9 | 55 | | 10 | 29 | | 11 | 27 | | 12 | 48 | | 13 | 18 | | 14 | 55 | | 15 | 7 | | 16 | 35 | | 17 | 58 | | 18 | 31 | | 19 | 1 | | 20 | 10 | | 21 | 37 | | 22 | 23 | | 23 | 30 | | 24 | 17 | | 25 | 10 | | 26 | 1 | | 27 | 34 | | 28 | 3 | | 29 | 38 | | 30 | 39 | | 31 | 5 | | 32 | 11 | | 33 | 23 | | 34 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 70.13% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 154 | | matches | | 0 | "was currently sprinting" | | 1 | "were dimming" | | 2 | "were growing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 90 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 950 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.02 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.010526315789473684 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 10.63 | | std | 6.12 | | cv | 0.576 | | sampleLengths | | 0 | 16 | | 1 | 17 | | 2 | 4 | | 3 | 18 | | 4 | 3 | | 5 | 15 | | 6 | 7 | | 7 | 15 | | 8 | 21 | | 9 | 12 | | 10 | 3 | | 11 | 19 | | 12 | 8 | | 13 | 5 | | 14 | 24 | | 15 | 7 | | 16 | 9 | | 17 | 13 | | 18 | 4 | | 19 | 23 | | 20 | 7 | | 21 | 10 | | 22 | 12 | | 23 | 4 | | 24 | 20 | | 25 | 8 | | 26 | 2 | | 27 | 14 | | 28 | 8 | | 29 | 11 | | 30 | 22 | | 31 | 5 | | 32 | 24 | | 33 | 8 | | 34 | 9 | | 35 | 5 | | 36 | 5 | | 37 | 10 | | 38 | 15 | | 39 | 11 | | 40 | 12 | | 41 | 2 | | 42 | 5 | | 43 | 11 | | 44 | 13 | | 45 | 15 | | 46 | 9 | | 47 | 7 | | 48 | 11 | | 49 | 7 |
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| 32.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2222222222222222 | | totalSentences | 90 | | uniqueOpeners | 20 | |
| 39.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 84 | | matches | | 0 | "Then, a cold, clawed hand" |
| | ratio | 0.012 | |
| 72.38% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 84 | | matches | | 0 | "He didn't look back." | | 1 | "He moved with a frantic," | | 2 | "Her lungs burned, a sharp," | | 3 | "She ignored the protest of" | | 4 | "She kept her eyes locked" | | 5 | "She passed it without a" | | 6 | "He slammed his shoulder into" | | 7 | "It stood slightly ajar, the" | | 8 | "She stepped through, her hand" | | 9 | "It stung the back of" | | 10 | "She descended a concrete staircase," | | 11 | "It didn't come from the" | | 12 | "Her heart hammered a frantic" | | 13 | "He reached into a small" | | 14 | "He handed the token to" | | 15 | "He grabbed the box and" | | 16 | "She felt the weight of" | | 17 | "She stepped out from behind" | | 18 | "It was a living, pulsing" | | 19 | "She moved through the crowd," |
| | ratio | 0.369 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 84 | | matches | | 0 | "A boot skidded on slick" | | 1 | "The suspect, a wiry man" | | 2 | "He didn't look back." | | 3 | "He moved with a frantic," | | 4 | "Quinn pushed harder." | | 5 | "Her lungs burned, a sharp," | | 6 | "She ignored the protest of" | | 7 | "She kept her eyes locked" | | 8 | "The neon green glow of" | | 9 | "The Raven's Nest." | | 10 | "She passed it without a" | | 11 | "A heavy iron gate loomed" | | 12 | "The man didn't slow down." | | 13 | "He slammed his shoulder into" | | 14 | "Quinn reached the gate a" | | 15 | "It stood slightly ajar, the" | | 16 | "She stepped through, her hand" | | 17 | "The air changed instantly." | | 18 | "The damp, metallic scent of" | | 19 | "It stung the back of" |
| | ratio | 0.94 | |
| 59.52% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 84 | | matches | | | ratio | 0.012 | |
| 97.26% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 3 | | matches | | 0 | "Her lungs burned, a sharp, rhythmic ache that matched the heavy thud of her footsteps." | | 1 | "The door swung open, revealing a stairwell that spiraled even deeper into the earth." | | 2 | "The whispers were growing louder, more insistent, turning into a rhythmic chant that vibrated through the floorboards." |
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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 | |