| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1197 | | 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) | |
| 62.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1197 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "vibrated" | | 1 | "scanned" | | 2 | "weight" | | 3 | "porcelain" | | 4 | "chill" | | 5 | "flickered" | | 6 | "rhythmic" | | 7 | "echoing" |
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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 | 90 | | matches | (empty) | |
| 47.62% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1196 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 1074 | | uniqueNames | 6 | | maxNameDensity | 1.58 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Quinn | 17 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 1 |
| | persons | | 0 | "Camden" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Morris" |
| | places | (empty) | | globalScore | 0.709 | | windowScore | 0.667 | |
| 81.51% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like wet river stone" | | 1 | "appeared beside her" |
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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 | 1196 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 22.57 | | std | 17.82 | | cv | 0.79 | | sampleLengths | | 0 | 47 | | 1 | 2 | | 2 | 27 | | 3 | 49 | | 4 | 44 | | 5 | 36 | | 6 | 5 | | 7 | 35 | | 8 | 8 | | 9 | 27 | | 10 | 12 | | 11 | 62 | | 12 | 9 | | 13 | 46 | | 14 | 31 | | 15 | 37 | | 16 | 60 | | 17 | 5 | | 18 | 51 | | 19 | 39 | | 20 | 9 | | 21 | 29 | | 22 | 4 | | 23 | 29 | | 24 | 6 | | 25 | 11 | | 26 | 12 | | 27 | 3 | | 28 | 9 | | 29 | 40 | | 30 | 65 | | 31 | 9 | | 32 | 6 | | 33 | 9 | | 34 | 27 | | 35 | 14 | | 36 | 5 | | 37 | 15 | | 38 | 14 | | 39 | 37 | | 40 | 46 | | 41 | 8 | | 42 | 7 | | 43 | 6 | | 44 | 39 | | 45 | 5 | | 46 | 7 | | 47 | 27 | | 48 | 5 | | 49 | 31 |
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| 93.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 90 | | matches | | 0 | "being pulled" | | 1 | "were cracked" | | 2 | "was mirrored" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 189 | | matches | | |
| 63.49% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 108 | | ratio | 0.028 | | matches | | 0 | "As it touched the metal, the door didn't creak or swing; it dissolved, the steel rippling like water to create a shimmering aperture." | | 1 | "Figures moved through the haze—some draped in heavy silks, others wearing masks carved from obsidian or animal bone." | | 2 | "Her military precision kicked in; she scanned the perimeter, noting the exits." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1079 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint, sickly green light." |
| | adverbCount | 23 | | adverbRatio | 0.021316033364226137 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008341056533827619 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 11.07 | | std | 5.61 | | cv | 0.507 | | sampleLengths | | 0 | 18 | | 1 | 10 | | 2 | 19 | | 3 | 2 | | 4 | 9 | | 5 | 5 | | 6 | 13 | | 7 | 3 | | 8 | 16 | | 9 | 18 | | 10 | 11 | | 11 | 1 | | 12 | 15 | | 13 | 19 | | 14 | 10 | | 15 | 19 | | 16 | 17 | | 17 | 5 | | 18 | 8 | | 19 | 15 | | 20 | 12 | | 21 | 8 | | 22 | 14 | | 23 | 13 | | 24 | 12 | | 25 | 7 | | 26 | 15 | | 27 | 17 | | 28 | 23 | | 29 | 9 | | 30 | 4 | | 31 | 13 | | 32 | 17 | | 33 | 12 | | 34 | 6 | | 35 | 4 | | 36 | 21 | | 37 | 7 | | 38 | 8 | | 39 | 17 | | 40 | 5 | | 41 | 14 | | 42 | 10 | | 43 | 18 | | 44 | 18 | | 45 | 5 | | 46 | 15 | | 47 | 12 | | 48 | 3 | | 49 | 21 |
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| 37.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.28703703703703703 | | totalSentences | 108 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 92.73% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 88 | | matches | | 0 | "He pivoted around a corner," | | 1 | "She banked hard, her shoulder" | | 2 | "She glanced at the worn" | | 3 | "She landed in a crouch," | | 4 | "His face remained a blur" | | 5 | "He reached into his pocket" | | 6 | "Her jaw tightened, the sharp" | | 7 | "He pressed the object against" | | 8 | "She thrust her arm forward," | | 9 | "She tumbled through, the sensation" | | 10 | "She hit a hard, dusty" | | 11 | "She found herself in an" | | 12 | "They didn't walk so much" | | 13 | "She stayed in the shadows" | | 14 | "Her military precision kicked in;" | | 15 | "She stood perfectly still, her" | | 16 | "She hadn't given her name." | | 17 | "She hadn't shown a badge." | | 18 | "She thought of DS Morris," | | 19 | "She stepped out of the" |
| | ratio | 0.318 | |
| 11.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 88 | | matches | | 0 | "Rain slicked the cobblestones of" | | 1 | "Harlow Quinn lunged forward, her" | | 2 | "Quinn’s voice cut through the" | | 3 | "The man didn't break stride." | | 4 | "He pivoted around a corner," | | 5 | "Quinn didn't slow." | | 6 | "She banked hard, her shoulder" | | 7 | "She glanced at the worn" | | 8 | "The suspect vaulted a low" | | 9 | "Quinn hit the wall at" | | 10 | "She landed in a crouch," | | 11 | "The alley ended in a" | | 12 | "The man was already halfway" | | 13 | "The man paused, looking back" | | 14 | "His face remained a blur" | | 15 | "He reached into his pocket" | | 16 | "Quinn stepped toward the stairwell," | | 17 | "Her jaw tightened, the sharp" | | 18 | "The man chuckled, a wet," | | 19 | "He pressed the object against" |
| | ratio | 0.898 | |
| 56.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 88 | | matches | | | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 2 | | matches | | 0 | "If she moved now, she could tackle him, but she was one woman in a den of things that didn't follow the rules of biology." | | 1 | "His skin was translucent now, showing veins that glowed with a faint, sickly green light." |
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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 | |