| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1508 | | 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) | |
| 76.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1508 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "weight" | | 1 | "flicker" | | 2 | "glint" | | 3 | "flicked" | | 4 | "etched" | | 5 | "pulse" |
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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 | 2 | | 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 | 155 | | 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 | 1511 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1396 | | uniqueNames | 21 | | maxNameDensity | 0.72 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 10 | | Raven | 1 | | Nest | 1 | | Metropolitan | 1 | | Police | 1 | | Herrera | 8 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | London | 3 | | Avenue | 1 | | Transport | 1 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Hatton | 1 | | Garden | 1 | | Rain | 3 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Police" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Rain" | | 8 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Seville" | | 2 | "London" | | 3 | "Avenue" | | 4 | "Market" | | 5 | "Hatton" | | 6 | "Garden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 86 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like bird bones" |
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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 | 1511 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 155 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 33.58 | | std | 28.26 | | cv | 0.842 | | sampleLengths | | 0 | 76 | | 1 | 55 | | 2 | 4 | | 3 | 73 | | 4 | 13 | | 5 | 1 | | 6 | 80 | | 7 | 18 | | 8 | 5 | | 9 | 5 | | 10 | 63 | | 11 | 13 | | 12 | 91 | | 13 | 15 | | 14 | 5 | | 15 | 70 | | 16 | 38 | | 17 | 26 | | 18 | 80 | | 19 | 57 | | 20 | 10 | | 21 | 24 | | 22 | 1 | | 23 | 33 | | 24 | 83 | | 25 | 4 | | 26 | 1 | | 27 | 50 | | 28 | 70 | | 29 | 14 | | 30 | 6 | | 31 | 9 | | 32 | 17 | | 33 | 80 | | 34 | 46 | | 35 | 17 | | 36 | 28 | | 37 | 7 | | 38 | 17 | | 39 | 27 | | 40 | 59 | | 41 | 3 | | 42 | 15 | | 43 | 50 | | 44 | 52 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 142 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 230 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 155 | | ratio | 0.013 | | matches | | 0 | "Dust, hot metal, and a sweetness she could not name — resin, or sugar left too long on a flame." | | 1 | "He flicked a pale sliver into that palm — bone, drilled, threaded on a dark cord — and the figure stepped aside as if a lock had turned." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1401 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.022840827980014276 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 155 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 155 | | mean | 9.75 | | std | 7.53 | | cv | 0.772 | | sampleLengths | | 0 | 8 | | 1 | 34 | | 2 | 17 | | 3 | 17 | | 4 | 23 | | 5 | 10 | | 6 | 7 | | 7 | 15 | | 8 | 4 | | 9 | 13 | | 10 | 24 | | 11 | 1 | | 12 | 2 | | 13 | 12 | | 14 | 21 | | 15 | 5 | | 16 | 3 | | 17 | 5 | | 18 | 1 | | 19 | 6 | | 20 | 13 | | 21 | 2 | | 22 | 16 | | 23 | 3 | | 24 | 3 | | 25 | 7 | | 26 | 3 | | 27 | 5 | | 28 | 22 | | 29 | 5 | | 30 | 2 | | 31 | 11 | | 32 | 5 | | 33 | 5 | | 34 | 8 | | 35 | 4 | | 36 | 15 | | 37 | 17 | | 38 | 3 | | 39 | 5 | | 40 | 11 | | 41 | 1 | | 42 | 3 | | 43 | 9 | | 44 | 10 | | 45 | 19 | | 46 | 11 | | 47 | 3 | | 48 | 19 | | 49 | 3 |
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| 67.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.43870967741935485 | | totalSentences | 155 | | uniqueOpeners | 68 | |
| 26.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 125 | | matches | | 0 | "Then he dropped off the" |
| | ratio | 0.008 | |
| 98.40% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 125 | | matches | | 0 | "She had watched that shelf" | | 1 | "They came out with their" | | 2 | "She did not break stride." | | 3 | "He cut left without looking" | | 4 | "He chose it." | | 5 | "She took the same rail." | | 6 | "His voice carried Seville under" | | 7 | "He did not slow." | | 8 | "She kept her eyes on" | | 9 | "He wanted north." | | 10 | "She filed the line of" | | 11 | "Her shoe lost the white" | | 12 | "He vaulted a theatre railing" | | 13 | "She matched him." | | 14 | "Her breath sawed." | | 15 | "He hit the canal bridge" | | 16 | "They stood near of a" | | 17 | "She took the stairs two" | | 18 | "Her hand found the door" | | 19 | "She had heard the phrase" |
| | ratio | 0.304 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 125 | | matches | | 0 | "Rain stitched the Soho pavements" | | 1 | "Detective Harlow Quinn held her" | | 2 | "Water tracked the sharp line" | | 3 | "She had watched that shelf" | | 4 | "Men went in with their" | | 5 | "They came out with their" | | 6 | "The shelf moved again." | | 7 | "Tomás Herrera stepped into the" | | 8 | "Olive skin, short curls plastered" | | 9 | "A name that kept surfacing" | | 10 | "Quinn crossed between two cabs." | | 11 | "A horn blared." | | 12 | "She did not break stride." | | 13 | "He cut left without looking" | | 14 | "The side street narrowed between" | | 15 | "The worn leather of her" | | 16 | "He chose it." | | 17 | "A skip of wet cardboard." | | 18 | "A bolted gate he took" | | 19 | "She took the same rail." |
| | ratio | 0.8 | |
| 80.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 125 | | matches | | 0 | "If she climbed back she" | | 1 | "If she crossed without a" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 2 | | matches | | 0 | "Through the window the place kept its usual dim: old maps, black-and-white photographs, a bookshelf that did not sit flush with the wall." | | 1 | "A name that kept surfacing in statements people later refused to sign." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0 | | effectiveRatio | 0 | |