| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "The woman gestured vaguely [vaguely]" |
| | dialogueSentences | 24 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.125 | | effectiveRatio | 0.083 | |
| 96.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1389 | | 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) | |
| 71.20% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1389 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "traced" | | 2 | "vibrated" | | 3 | "pulsed" | | 4 | "pulse" | | 5 | "electric" | | 6 | "gloom" | | 7 | "flicker" |
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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 | 142 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 142 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 158 | | 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 | 1389 | | 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 | 40 | | wordCount | 1183 | | uniqueNames | 18 | | maxNameDensity | 1.27 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 4 | | Thames | 1 | | Quinn | 15 | | Morris | 2 | | Whitechapel | 1 | | Tube | 1 | | Transport | 1 | | Veil | 1 | | Market | 1 | | Clinic | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | Bandages | 1 | | Warden | 1 | | Three | 3 | | Tomás | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Clinic" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Warden" | | 8 | "Tomás" |
| | places | | 0 | "London" | | 1 | "Thames" | | 2 | "Whitechapel" | | 3 | "Seville" |
| | globalScore | 0.866 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite but Quinn's hindbrain decoded it as recognition all the same" |
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| 56.01% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.44 | | wordCount | 1389 | | matches | | 0 | "not speech, not quite, but Quinn's hindbrain decoded it as recognition all the same" | | 1 | "not quite, but Quinn's hindbrain decoded it as recognition all the same" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 158 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 27.78 | | std | 20.66 | | cv | 0.744 | | sampleLengths | | 0 | 20 | | 1 | 50 | | 2 | 70 | | 3 | 3 | | 4 | 55 | | 5 | 32 | | 6 | 6 | | 7 | 46 | | 8 | 38 | | 9 | 8 | | 10 | 79 | | 11 | 8 | | 12 | 50 | | 13 | 5 | | 14 | 76 | | 15 | 43 | | 16 | 59 | | 17 | 42 | | 18 | 20 | | 19 | 41 | | 20 | 2 | | 21 | 3 | | 22 | 41 | | 23 | 13 | | 24 | 13 | | 25 | 31 | | 26 | 7 | | 27 | 11 | | 28 | 30 | | 29 | 40 | | 30 | 31 | | 31 | 23 | | 32 | 42 | | 33 | 10 | | 34 | 31 | | 35 | 52 | | 36 | 14 | | 37 | 8 | | 38 | 10 | | 39 | 49 | | 40 | 28 | | 41 | 2 | | 42 | 41 | | 43 | 11 | | 44 | 10 | | 45 | 3 | | 46 | 39 | | 47 | 29 | | 48 | 10 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 142 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 199 | | matches | | 0 | "was going" | | 1 | "was looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 158 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1191 | | adjectiveStacks | 2 | | stackExamples | | 0 | "clean white medical lamps" | | 1 | "Short curly dark hair." |
| | adverbCount | 37 | | adverbRatio | 0.031066330814441646 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0033585222502099076 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 158 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 158 | | mean | 8.79 | | std | 6.24 | | cv | 0.71 | | sampleLengths | | 0 | 4 | | 1 | 16 | | 2 | 2 | | 3 | 11 | | 4 | 18 | | 5 | 16 | | 6 | 3 | | 7 | 3 | | 8 | 17 | | 9 | 13 | | 10 | 22 | | 11 | 1 | | 12 | 1 | | 13 | 13 | | 14 | 2 | | 15 | 1 | | 16 | 4 | | 17 | 7 | | 18 | 19 | | 19 | 9 | | 20 | 8 | | 21 | 8 | | 22 | 12 | | 23 | 10 | | 24 | 6 | | 25 | 4 | | 26 | 2 | | 27 | 2 | | 28 | 2 | | 29 | 7 | | 30 | 10 | | 31 | 7 | | 32 | 4 | | 33 | 2 | | 34 | 16 | | 35 | 5 | | 36 | 13 | | 37 | 11 | | 38 | 9 | | 39 | 4 | | 40 | 2 | | 41 | 2 | | 42 | 4 | | 43 | 16 | | 44 | 19 | | 45 | 5 | | 46 | 6 | | 47 | 10 | | 48 | 19 | | 49 | 8 |
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| 68.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.4620253164556962 | | totalSentences | 158 | | uniqueOpeners | 73 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 121 | | matches | | 0 | "Then he was gone." | | 1 | "Too precise for weathering." | | 2 | "Once from an informant who" | | 3 | "Once from a suspect who" | | 4 | "Too many exits she couldn't" | | 5 | "Too many faces that turned" |
| | ratio | 0.05 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 121 | | matches | | 0 | "Her worn leather watch caught" | | 1 | "She tasted grit." | | 2 | "Her quarry vaulted a chain-link" | | 3 | "She scaled the fence." | | 4 | "Her shoes found the wet" | | 5 | "Their signs flickered and buzzed" | | 6 | "His hand found something at" | | 7 | "Her fingers traced the wall" | | 8 | "She checked her phone." | | 9 | "She had memorised every detail." | | 10 | "She had promised herself she" | | 11 | "Her torch cut a white" | | 12 | "She recognised the architecture, the" | | 13 | "She had heard the name" | | 14 | "She stood out like a" | | 15 | "Her quarry was nowhere in" | | 16 | "He had dissolved into the" | | 17 | "She moved along the platform." | | 18 | "Her eyes, wet and black," | | 19 | "His left hand moved to" |
| | ratio | 0.231 | |
| 71.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 121 | | matches | | 0 | "Boots hit wet pavement." | | 1 | "Her worn leather watch caught" | | 2 | "The rain came sideways off" | | 3 | "She tasted grit." | | 4 | "The alley narrowed." | | 5 | "Brick walls glistened on both" | | 6 | "Graffiti layered the stone, tags" | | 7 | "Her quarry vaulted a chain-link" | | 8 | "She scaled the fence." | | 9 | "The chain link bit into" | | 10 | "Her shoes found the wet" | | 11 | "Chinese takeaway, a locksmith, a" | | 12 | "Their signs flickered and buzzed" | | 13 | "A cat watched her from" | | 14 | "The hooded figure reached the" | | 15 | "His hand found something at" | | 16 | "A scraping sound, metal on" | | 17 | "Quinn crossed the yard in" | | 18 | "Her fingers traced the wall" | | 19 | "The section pivoted inward on" |
| | ratio | 0.777 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 57.14% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 6 | | matches | | 0 | "The section pivoted inward on a hidden axis, revealing a passage that descended into fluorescent murk." | | 1 | "Her torch cut a white cone through the darkness, catching moisture that drifted like suspended glass." | | 2 | "A child, no older than ten, sat cross-legged on a blanket surrounded by bones that clicked and arranged themselves into patterns when no one was looking." | | 3 | "The air grew thicker, the candle smoke layering into a haze that tasted of herbs and chemicals and something electric." | | 4 | "And there, at the end of the row, a stall lit by clean white medical lamps that looked surgical against the candlelit gloom." | | 5 | "The candlelight bent around them, curving away as though even illumination preferred to give them space." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |