| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 27 | | tagDensity | 0.259 | | leniency | 0.519 | | rawRatio | 0.143 | | effectiveRatio | 0.074 | |
| 94.94% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1977 | | totalAiIsmAdverbs | 2 | | 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) | |
| 84.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1977 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "flicked" | | 1 | "could feel" | | 2 | "efficient" | | 3 | "measured" | | 4 | "whisper" | | 5 | "gloom" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "hung thick in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 151 | | matches | | 0 | "was afraid" | | 1 | "t with surprise" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 151 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 171 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1977 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 91 | | wordCount | 1780 | | uniqueNames | 24 | | maxNameDensity | 1.63 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 4 | | High | 2 | | Street | 2 | | Harlow | 29 | | Quinn | 2 | | Vane | 2 | | Milo | 15 | | Metropolitan | 1 | | Police | 1 | | Underground | 1 | | Undercroft | 1 | | Veil | 2 | | Market | 2 | | Morris | 7 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Spanish | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Tomás | 8 | | Detective | 2 | | Procedure | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Vane" | | 3 | "Milo" | | 4 | "Police" | | 5 | "Market" | | 6 | "Morris" | | 7 | "Raven" | | 8 | "Saint" | | 9 | "Christopher" | | 10 | "Herrera" | | 11 | "Tomás" | | 12 | "Detective" | | 13 | "Procedure" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Metropolitan" |
| | globalScore | 0.685 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a man carrying a message he w" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.506 | | wordCount | 1977 | | matches | | 0 | "not loudly, but the word cut cleanly through the noise of the street" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 171 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 29.07 | | std | 29.95 | | cv | 1.03 | | sampleLengths | | 0 | 93 | | 1 | 57 | | 2 | 5 | | 3 | 32 | | 4 | 17 | | 5 | 4 | | 6 | 93 | | 7 | 71 | | 8 | 67 | | 9 | 6 | | 10 | 59 | | 11 | 20 | | 12 | 5 | | 13 | 3 | | 14 | 82 | | 15 | 16 | | 16 | 74 | | 17 | 5 | | 18 | 102 | | 19 | 127 | | 20 | 6 | | 21 | 41 | | 22 | 8 | | 23 | 43 | | 24 | 11 | | 25 | 24 | | 26 | 93 | | 27 | 3 | | 28 | 80 | | 29 | 39 | | 30 | 2 | | 31 | 50 | | 32 | 3 | | 33 | 55 | | 34 | 5 | | 35 | 56 | | 36 | 10 | | 37 | 19 | | 38 | 5 | | 39 | 17 | | 40 | 29 | | 41 | 4 | | 42 | 8 | | 43 | 8 | | 44 | 9 | | 45 | 30 | | 46 | 10 | | 47 | 1 | | 48 | 42 | | 49 | 3 |
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| 89.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 151 | | matches | | 0 | "was fixed" | | 1 | "been battened" | | 2 | "been found" | | 3 | "been carved" | | 4 | "been covered" | | 5 | "were shaped" | | 6 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 302 | | matches | | 0 | "was trying" | | 1 | "was going" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 171 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1792 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 58 | | adverbRatio | 0.03236607142857143 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.006696428571428571 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 171 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 171 | | mean | 11.56 | | std | 8.9 | | cv | 0.77 | | sampleLengths | | 0 | 12 | | 1 | 27 | | 2 | 38 | | 3 | 6 | | 4 | 10 | | 5 | 9 | | 6 | 33 | | 7 | 15 | | 8 | 5 | | 9 | 7 | | 10 | 20 | | 11 | 5 | | 12 | 16 | | 13 | 1 | | 14 | 4 | | 15 | 19 | | 16 | 19 | | 17 | 12 | | 18 | 23 | | 19 | 20 | | 20 | 21 | | 21 | 13 | | 22 | 14 | | 23 | 8 | | 24 | 15 | | 25 | 7 | | 26 | 6 | | 27 | 13 | | 28 | 41 | | 29 | 6 | | 30 | 20 | | 31 | 17 | | 32 | 9 | | 33 | 8 | | 34 | 2 | | 35 | 3 | | 36 | 20 | | 37 | 2 | | 38 | 3 | | 39 | 3 | | 40 | 3 | | 41 | 37 | | 42 | 21 | | 43 | 6 | | 44 | 15 | | 45 | 16 | | 46 | 15 | | 47 | 7 | | 48 | 15 | | 49 | 14 |
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| 51.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3567251461988304 | | totalSentences | 171 | | uniqueOpeners | 61 | |
| 71.43% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 140 | | matches | | 0 | "Then Milo’s mouth tightened, and" | | 1 | "Then the tunnel opened." | | 2 | "Then Detective Harlow Quinn stepped" |
| | ratio | 0.021 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 140 | | matches | | 0 | "She did not wipe it" | | 1 | "Her attention was fixed on" | | 2 | "He kept his shoulders hunched," | | 3 | "She gave him three more" | | 4 | "He saw her at once." | | 5 | "she said, not loudly, but" | | 6 | "He crossed the road without" | | 7 | "He turned left toward the" | | 8 | "He knocked over a stack" | | 9 | "She reached the end of" | | 10 | "She had heard the name" | | 11 | "It moved locations every full" | | 12 | "Her radio crackled uselessly against" | | 13 | "She reached into the inner" | | 14 | "It was a token, though" | | 15 | "It had been found in" | | 16 | "She did not know what" | | 17 | "She did not know what" | | 18 | "She suspected the clique Milo" | | 19 | "She suspected them of worse" |
| | ratio | 0.293 | |
| 56.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 113 | | totalSentences | 140 | | matches | | 0 | "Neon bled across the wet" | | 1 | "Detective Harlow Quinn stood in" | | 2 | "She did not wipe it" | | 3 | "Her attention was fixed on" | | 4 | "Milo Vane was trying to" | | 5 | "He kept his shoulders hunched," | | 6 | "She gave him three more" | | 7 | "He saw her at once." | | 8 | "Harlow broke into a run." | | 9 | "she said, not loudly, but" | | 10 | "Milo did not stop." | | 11 | "He crossed the road without" | | 12 | "Harlow followed, slipping on the" | | 13 | "Brown eyes narrowed against the" | | 14 | "He turned left toward the" | | 15 | "Harlow followed, close enough now" | | 16 | "He knocked over a stack" | | 17 | "A dog barked from somewhere" | | 18 | "Harlow’s worn leather watch rode" | | 19 | "Milo cut right into a" |
| | ratio | 0.807 | |
| 71.43% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 140 | | matches | | 0 | "If she went after him," | | 1 | "If she waited, he would" |
| | ratio | 0.014 | |
| 87.20% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 77 | | technicalSentenceCount | 6 | | matches | | 0 | "Then Milo’s mouth tightened, and he spun on his heel, shouldering past a pair of students who shouted after him." | | 1 | "Harlow vaulted after him, landing hard on the balls of her feet, her knees absorbing the impact." | | 2 | "She had heard the name spoken in fragments by informants who went quiet afterward, in case files that had no business being thin, in the last notes DS Morris ha…" | | 3 | "A hidden market that sold enchanted goods, banned alchemical substances, and information of the kind that could get a person killed." | | 4 | "But she knew that Milo Vane had spent eleven minutes in the hidden back room of the Raven’s Nest, behind a bookshelf that should not have led anywhere, and when…" | | 5 | "Off-the-books medical care for people who could not go to hospitals, or who did not want the questions that came with them." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.259 | | leniency | 0.519 | | rawRatio | 0 | | effectiveRatio | 0 | |