| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1213 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 50.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1213 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "scanning" | | 1 | "weight" | | 2 | "flicked" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "pulse" | | 6 | "velvet" | | 7 | "silk" | | 8 | "chilled" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "flicker of emotion" | | count | 1 |
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| | highlights | | 0 | "a flash of recognition" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 108 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 108 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1206 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 95.29% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1188 | | uniqueNames | 16 | | maxNameDensity | 1.09 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Tomás | 2 | | Herrera | 11 | | Raven | 1 | | Nest | 2 | | Quinn | 3 | | Saint | 2 | | Christopher | 2 | | Greek | 1 | | Street | 2 | | Old | 1 | | Compton | 1 | | Zam-Buk | 1 | | Underground | 1 | | Tube | 1 | | Morris | 2 | | Harlow | 13 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Quinn" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Harlow" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Old" | | 3 | "Compton" |
| | globalScore | 0.953 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.829 | | wordCount | 1206 | | matches | | 0 | "not the memorial photo, but the last one, eyes open" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 40.2 | | std | 31.77 | | cv | 0.79 | | sampleLengths | | 0 | 62 | | 1 | 84 | | 2 | 21 | | 3 | 6 | | 4 | 66 | | 5 | 2 | | 6 | 15 | | 7 | 103 | | 8 | 26 | | 9 | 88 | | 10 | 72 | | 11 | 7 | | 12 | 42 | | 13 | 41 | | 14 | 64 | | 15 | 71 | | 16 | 90 | | 17 | 12 | | 18 | 1 | | 19 | 49 | | 20 | 18 | | 21 | 16 | | 22 | 69 | | 23 | 69 | | 24 | 5 | | 25 | 30 | | 26 | 2 | | 27 | 7 | | 28 | 65 | | 29 | 3 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 108 | | matches | | 0 | "were heaped" | | 1 | "was plastered" | | 2 | "was soaked" |
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| 42.02% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 211 | | matches | | 0 | "was being" | | 1 | "was already slamming" | | 2 | "was disorienting" | | 3 | "were sweating" | | 4 | "was thinking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 2 | | flaggedSentences | 8 | | totalSentences | 112 | | ratio | 0.071 | | matches | | 0 | "His eyes were warm brown but cagey tonight; he wore a denim jacket soaked instantly, and the chain of a Saint Christopher medallion glinted at his throat." | | 1 | "Her leather boots were old but good; she moved with military precision, arms loose, breathing through her nose even as the rain hammered." | | 2 | "Harlow cut between a black cab and a delivery van, saw Herrera glance back once—a flash of recognition, fear." | | 3 | "The stairwell bottomed out into a wider passage—old service tunnel, arched and tiled, with puddles that reflected her light like broken mirrors." | | 4 | "DS Morris’s face surfaced unbidden—not the memorial photo, but the last one, eyes open and not seeing." | | 5 | "Harlow saw Herrera fumble at his neck, not the medallion—something else." | | 6 | "The market rustled and glowed beyond him—not a normal black market." | | 7 | "The gaunt man made a sound—not angry, almost amused—but he didn’t stop her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1202 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.024126455906821963 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0024958402662229617 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 10.77 | | std | 6.73 | | cv | 0.625 | | sampleLengths | | 0 | 21 | | 1 | 23 | | 2 | 3 | | 3 | 15 | | 4 | 18 | | 5 | 8 | | 6 | 7 | | 7 | 27 | | 8 | 24 | | 9 | 21 | | 10 | 6 | | 11 | 5 | | 12 | 8 | | 13 | 23 | | 14 | 8 | | 15 | 3 | | 16 | 19 | | 17 | 2 | | 18 | 12 | | 19 | 3 | | 20 | 14 | | 21 | 18 | | 22 | 10 | | 23 | 9 | | 24 | 6 | | 25 | 16 | | 26 | 11 | | 27 | 19 | | 28 | 7 | | 29 | 19 | | 30 | 19 | | 31 | 6 | | 32 | 9 | | 33 | 21 | | 34 | 13 | | 35 | 20 | | 36 | 5 | | 37 | 16 | | 38 | 11 | | 39 | 17 | | 40 | 4 | | 41 | 4 | | 42 | 7 | | 43 | 8 | | 44 | 7 | | 45 | 3 | | 46 | 9 | | 47 | 11 | | 48 | 10 | | 49 | 9 |
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| 40.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.26785714285714285 | | totalSentences | 112 | | uniqueOpeners | 30 | |
| 32.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 104 | | matches | | 0 | "Somewhere ahead, a Saint Christopher" |
| | ratio | 0.01 | |
| 70.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 104 | | matches | | 0 | "She didn’t move." | | 1 | "She’d learned long ago that" | | 2 | "His eyes were warm brown" | | 3 | "He turned left, heading toward" | | 4 | "Her leather boots were old" | | 5 | "She said it more as" | | 6 | "He didn’t stop." | | 7 | "He bolted up a narrow" | | 8 | "She vaulted a fallen crate" | | 9 | "Her leather watch strap was" | | 10 | "He cut left into a" | | 11 | "It looked locked, painted over," | | 12 | "His sleeve caught on the" | | 13 | "She reached it just in" | | 14 | "She pulled a small torch" | | 15 | "She’d walked this mews a" | | 16 | "She kept the torch low," | | 17 | "She could still hear him," | | 18 | "She followed the sound." | | 19 | "Her pulse was a steady" |
| | ratio | 0.375 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 104 | | matches | | 0 | "The rain had been falling" | | 1 | "Harlow Quinn stood across the" | | 2 | "She didn’t move." | | 3 | "She’d learned long ago that" | | 4 | "The green neon of the" | | 5 | "Herrera paused on the threshold," | | 6 | "Harlow shifted her weight behind" | | 7 | "His eyes were warm brown" | | 8 | "The interior of the bar" | | 9 | "He turned left, heading toward" | | 10 | "Harlow waited seven seconds, then" | | 11 | "The street ran with water." | | 12 | "Taxis threw sheets of spray" | | 13 | "Her leather boots were old" | | 14 | "Herrera crossed Old Compton Street" | | 15 | "A cyclist swore." | | 16 | "Harlow cut between a black" | | 17 | "She said it more as" | | 18 | "He didn’t stop." | | 19 | "He bolted up a narrow" |
| | ratio | 0.933 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 104 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 2 | | matches | | 0 | "He turned left, heading toward Greek Street with the quick, unpleasant walk of a man who knew he was being watched." | | 1 | "The stairwell bottomed out into a wider passage—old service tunnel, arched and tiled, with puddles that reflected her light like broken mirrors." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.5 | | effectiveRatio | 0.333 | |