| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1488 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "precisely" |
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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) | |
| 36.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1488 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "echoing" | | 1 | "gloom" | | 2 | "familiar" | | 3 | "standard" | | 4 | "pristine" | | 5 | "weight" | | 6 | "scanning" | | 7 | "silence" | | 8 | "etched" | | 9 | "crystalline" | | 10 | "wavered" | | 11 | "trembled" | | 12 | "magnetic" | | 13 | "resonance" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "weight of words/silence" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 151 | | matches | (empty) | |
| 86.09% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 1 | | narrationSentences | 151 | | filterMatches | | 0 | "know look decide" | | 1 | "watch" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1488 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 8 | | matches | | 0 | "Clean job, Quinn, he said, not bothering to look up." | | 1 | "Look at the soles, Quinn said." | | 2 | "Check the entrance turnstile, Miller called back, though his torch beam wavered slightly." | | 3 | "I already did, Quinn said." | | 4 | "They move the veil, she whispered, the syllables rough in her throat." | | 5 | "Morris called it a syndicate, Quinn said, folding the paper and tucking it into a sterile evidence envelope." | | 6 | "Bag the compass, she said, her voice cutting through the damp air like a blade." | | 7 | "Neither do they pay their taxes, Quinn replied." |
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| 99.60% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1488 | | uniqueNames | 11 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Met | 1 | | Miller | 9 | | Quinn | 15 | | Veil | 5 | | Market | 3 | | Morris | 3 | | Scotland | 1 | | Yard | 1 | | London | 1 | | You | 4 |
| | persons | | 0 | "Met" | | 1 | "Miller" | | 2 | "Quinn" | | 3 | "Market" | | 4 | "Morris" | | 5 | "You" |
| | places | | | globalScore | 0.996 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | glossingSentenceCount | 1 | | matches | | 0 | "sigils that seemed to shift in her peripheral vision" |
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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 | 1488 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 151 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 70.86 | | std | 38.17 | | cv | 0.539 | | sampleLengths | | 0 | 111 | | 1 | 40 | | 2 | 37 | | 3 | 119 | | 4 | 91 | | 5 | 82 | | 6 | 105 | | 7 | 65 | | 8 | 64 | | 9 | 116 | | 10 | 13 | | 11 | 67 | | 12 | 37 | | 13 | 87 | | 14 | 24 | | 15 | 30 | | 16 | 55 | | 17 | 149 | | 18 | 52 | | 19 | 20 | | 20 | 124 |
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| 95.97% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 151 | | matches | | 0 | "was etched" | | 1 | "was caught" | | 2 | "been torn" | | 3 | "been bricked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 280 | | matches | | 0 | "was wiping" | | 1 | "wasn't pointing" | | 2 | "was waxing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 151 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1491 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.02682763246143528 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.01006036217303823 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 9.85 | | std | 6.36 | | cv | 0.645 | | sampleLengths | | 0 | 11 | | 1 | 21 | | 2 | 20 | | 3 | 20 | | 4 | 18 | | 5 | 21 | | 6 | 20 | | 7 | 20 | | 8 | 10 | | 9 | 1 | | 10 | 4 | | 11 | 5 | | 12 | 12 | | 13 | 2 | | 14 | 3 | | 15 | 4 | | 16 | 12 | | 17 | 24 | | 18 | 17 | | 19 | 3 | | 20 | 6 | | 21 | 7 | | 22 | 22 | | 23 | 15 | | 24 | 9 | | 25 | 7 | | 26 | 12 | | 27 | 5 | | 28 | 19 | | 29 | 10 | | 30 | 10 | | 31 | 3 | | 32 | 5 | | 33 | 6 | | 34 | 14 | | 35 | 13 | | 36 | 3 | | 37 | 6 | | 38 | 7 | | 39 | 8 | | 40 | 5 | | 41 | 9 | | 42 | 13 | | 43 | 5 | | 44 | 13 | | 45 | 6 | | 46 | 14 | | 47 | 16 | | 48 | 19 | | 49 | 6 |
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| 42.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3333333333333333 | | totalSentences | 150 | | uniqueOpeners | 50 | |
| 70.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 141 | | matches | | 0 | "Just a single item placed" | | 1 | "Probably dropped when the attacker" | | 2 | "Somewhere above, London slept, unaware" |
| | ratio | 0.021 | |
| 95.18% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 141 | | matches | | 0 | "It was the kind that" | | 1 | "She adjusted the worn leather" | | 2 | "He was wiping a smear" | | 3 | "She crossed the platform, her" | | 4 | "Her voice was low, edged" | | 5 | "She knelt beside the body." | | 6 | "It's a paperweight." | | 7 | "We're running prints, scanning for" | | 8 | "They move their goods tonight." | | 9 | "I'm saying he walked into" | | 10 | "I'll log it as homicide." | | 11 | "We process it, bag the" | | 12 | "It was heavy, unnaturally cold," | | 13 | "She had seen this exact" | | 14 | "It was trembling, spinning in" | | 15 | "She rose slowly, her boots" | | 16 | "She angled her torch downward." | | 17 | "He's facing the northern service" | | 18 | "We're not hunting ghosts." | | 19 | "You want a theory about" |
| | ratio | 0.312 | |
| 77.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 141 | | matches | | 0 | "The damp air clung to" | | 1 | "Quinn stepped over the yellow" | | 2 | "It was the kind that" | | 3 | "She adjusted the worn leather" | | 4 | "The air smelled of ozone," | | 5 | "Sergeant Miller stood near the" | | 6 | "He was wiping a smear" | | 7 | "Wallet, phone, watch, all gone." | | 8 | "Looks like a mark out" | | 9 | "Quinn didn't answer immediately." | | 10 | "She crossed the platform, her" | | 11 | "The station was a dead" | | 12 | "A bone token." | | 13 | "Someone had paid to get" | | 14 | "The Veil Market shifted its" | | 15 | "This was a staging ground," | | 16 | "Her voice was low, edged" | | 17 | "She knelt beside the body." | | 18 | "The man was in his" | | 19 | "The fabric was pristine, the" |
| | ratio | 0.766 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 141 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 5 | | matches | | 0 | "He was wiping a smear of condensation from his knuckles, his posture relaxed, his voice carrying that familiar, practiced casualness." | | 1 | "The man was in his forties, dressed in a tailored wool coat that bore no signs of street wear." | | 2 | "The face was etched with tight, angular sigils that seemed to shift in her peripheral vision." | | 3 | "The bricks themselves were cut to impossible uniformity, their edges glowing faintly with a crystalline residue that smelled of burnt sage and copper." | | 4 | "Quinn felt it now, a low hum vibrating through the soles of her boots, the same frequency that had haunted her dreams for three years, the same frequency that h…" |
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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 | |