| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.70% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1164 | | 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) | |
| 65.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1164 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "pulse" | | 1 | "flickered" | | 2 | "maw" | | 3 | "shattered" | | 4 | "vibrated" | | 5 | "footsteps" | | 6 | "glistening" |
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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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | |
| 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 | 1159 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1122 | | uniqueNames | 14 | | maxNameDensity | 1.34 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 15 | | Quinn | 1 | | Arlington | 1 | | Road | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | English | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Arlington" | | 4 | "Road" |
| | globalScore | 0.832 | | windowScore | 1 | |
| 52.60% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 3 | | matches | | 0 | "something like jasmine, rising through the r" | | 1 | "looked like a deer on a lead, except its" | | 2 | "tasted like pennies and jasmine" |
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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 | 1159 | | matches | (empty) | |
| 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 | 28 | | mean | 41.39 | | std | 27.74 | | cv | 0.67 | | sampleLengths | | 0 | 73 | | 1 | 4 | | 2 | 52 | | 3 | 74 | | 4 | 54 | | 5 | 66 | | 6 | 71 | | 7 | 6 | | 8 | 71 | | 9 | 18 | | 10 | 10 | | 11 | 17 | | 12 | 23 | | 13 | 67 | | 14 | 54 | | 15 | 49 | | 16 | 7 | | 17 | 7 | | 18 | 69 | | 19 | 91 | | 20 | 73 | | 21 | 3 | | 22 | 52 | | 23 | 51 | | 24 | 45 | | 25 | 3 | | 26 | 41 | | 27 | 8 |
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| 98.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 106 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 181 | | matches | (empty) | |
| 15.31% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 112 | | ratio | 0.045 | | matches | | 0 | "He wore a waxed jacket with the hood thrown back, and he ran like a man who had done this before—shoulders low, arms tight, feet finding the cracks between puddles." | | 1 | "The floral smell hit her before she saw the platform—jasmine, or something like jasmine, rising through the rot." | | 2 | "The woman’s hand closed over the disc—a bone token, small and yellowed against her grey palm." | | 3 | "In its place rose the noise of the Veil Market—haggling in languages that felt older than English, the chime of brass scales, a wheeze from somewhere deep in the dark." | | 4 | "For the first time, she saw his face clearly—sharp, pale, afraid." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1127 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.031055900621118012 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0026619343389529724 | |
| 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 | 10.35 | | std | 6.67 | | cv | 0.645 | | sampleLengths | | 0 | 25 | | 1 | 18 | | 2 | 30 | | 3 | 4 | | 4 | 3 | | 5 | 12 | | 6 | 14 | | 7 | 11 | | 8 | 5 | | 9 | 7 | | 10 | 18 | | 11 | 4 | | 12 | 3 | | 13 | 10 | | 14 | 30 | | 15 | 9 | | 16 | 12 | | 17 | 12 | | 18 | 7 | | 19 | 16 | | 20 | 7 | | 21 | 6 | | 22 | 11 | | 23 | 3 | | 24 | 15 | | 25 | 13 | | 26 | 18 | | 27 | 9 | | 28 | 8 | | 29 | 11 | | 30 | 12 | | 31 | 16 | | 32 | 15 | | 33 | 3 | | 34 | 3 | | 35 | 5 | | 36 | 16 | | 37 | 11 | | 38 | 5 | | 39 | 12 | | 40 | 13 | | 41 | 9 | | 42 | 11 | | 43 | 4 | | 44 | 3 | | 45 | 4 | | 46 | 5 | | 47 | 1 | | 48 | 7 | | 49 | 10 |
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| 38.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.2767857142857143 | | totalSentences | 112 | | uniqueOpeners | 31 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 101 | | matches | | 0 | "Somewhere below, the suspect’s boots" | | 1 | "Just an open maw in" | | 2 | "Then he was gone into" | | 3 | "Somewhere below, a door slammed." |
| | ratio | 0.04 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 101 | | matches | | 0 | "He wore a waxed jacket" | | 1 | "He didn’t turn." | | 2 | "It twisted around her wrist." | | 3 | "She pulled it tight and" | | 4 | "Her lungs burned." | | 5 | "She had been tailing him" | | 6 | "He vanished through a gap" | | 7 | "She drew her weapon and" | | 8 | "She emerged onto a platform" | | 9 | "She pulled a lever built" | | 10 | "She raised her gun." | | 11 | "They were pale, almost colourless." | | 12 | "She had four seconds before" | | 13 | "Her radio had died three" | | 14 | "She slammed her left boot" | | 15 | "She wedged her shoulder into" | | 16 | "She turned sideways and pushed" | | 17 | "She followed them past a" | | 18 | "She ignored them." | | 19 | "Her focus stayed on the" |
| | ratio | 0.297 | |
| 9.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 101 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "He wore a waxed jacket" | | 3 | "He didn’t turn." | | 4 | "A bus hissed past, throwing" | | 5 | "Harlow vaulted the curb, slipped" | | 6 | "The leather strap of her" | | 7 | "It twisted around her wrist." | | 8 | "She pulled it tight and" | | 9 | "The suspect cut down a" | | 10 | "Harlow gained a stride." | | 11 | "Her lungs burned." | | 12 | "The heavy fabric of her" | | 13 | "She had been tailing him" | | 14 | "He vanished through a gap" | | 15 | "A sheet of corrugated metal" | | 16 | "Harlow reached the opening a" | | 17 | "She drew her weapon and" | | 18 | "A stairwell dropped away into" | | 19 | "The rain stopped at the" |
| | ratio | 0.901 | |
| 49.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 101 | | matches | | | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 3 | | matches | | 0 | "He wore a waxed jacket with the hood thrown back, and he ran like a man who had done this before—shoulders low, arms tight, feet finding the cracks between pudd…" | | 1 | "Just an open maw in the earth where her suspect had vanished and a gatekeeper who looked at her like she was a stray dog at a butcher’s counter." | | 2 | "In its place rose the noise of the Veil Market—haggling in languages that felt older than English, the chime of brass scales, a wheeze from somewhere deep in th…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 8 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.5 | | effectiveRatio | 0.25 | |