| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.381 | | leniency | 0.762 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1435 | | totalAiIsmAdverbs | 3 | | 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) | |
| 75.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1435 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "silk" | | 1 | "crystal" | | 2 | "etched" | | 3 | "wavered" | | 4 | "magnetic" | | 5 | "quivered" |
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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 | 94 | | matches | (empty) | |
| 82.07% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | | 0 | "managed to" | | 1 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1436 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 980 | | uniqueNames | 10 | | maxNameDensity | 0.71 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 7 | | Camden | 1 | | High | 1 | | Street | 1 | | Adeyemi | 5 | | British | 1 | | Museum | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Adeyemi" | | 3 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 1 | | matches | | 0 | "as though reaching for something" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.696 | | wordCount | 1436 | | matches | | 0 | "not pointing at the wall at all, but through it" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 33.4 | | std | 26.21 | | cv | 0.785 | | sampleLengths | | 0 | 88 | | 1 | 57 | | 2 | 61 | | 3 | 1 | | 4 | 82 | | 5 | 42 | | 6 | 67 | | 7 | 8 | | 8 | 6 | | 9 | 5 | | 10 | 5 | | 11 | 30 | | 12 | 4 | | 13 | 18 | | 14 | 77 | | 15 | 5 | | 16 | 3 | | 17 | 32 | | 18 | 3 | | 19 | 46 | | 20 | 14 | | 21 | 55 | | 22 | 17 | | 23 | 34 | | 24 | 4 | | 25 | 44 | | 26 | 9 | | 27 | 68 | | 28 | 24 | | 29 | 59 | | 30 | 59 | | 31 | 4 | | 32 | 52 | | 33 | 15 | | 34 | 55 | | 35 | 27 | | 36 | 54 | | 37 | 49 | | 38 | 9 | | 39 | 84 | | 40 | 15 | | 41 | 28 | | 42 | 17 |
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| 86.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 94 | | matches | | 0 | "been rigged" | | 1 | "was unmarked" | | 2 | "was meant" | | 3 | "was etched" | | 4 | "been swept" | | 5 | "is swept" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 155 | | matches | | 0 | "was spinning" | | 1 | "was not pointing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 120 | | ratio | 0.008 | | matches | | 0 | "RESTRICTED ARCHIVES — DAY ACCESS." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 982 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.023421588594704685 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0050916496945010185 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 11.97 | | std | 9.69 | | cv | 0.81 | | sampleLengths | | 0 | 7 | | 1 | 26 | | 2 | 35 | | 3 | 16 | | 4 | 3 | | 5 | 1 | | 6 | 17 | | 7 | 7 | | 8 | 6 | | 9 | 27 | | 10 | 12 | | 11 | 14 | | 12 | 11 | | 13 | 24 | | 14 | 1 | | 15 | 18 | | 16 | 40 | | 17 | 24 | | 18 | 3 | | 19 | 18 | | 20 | 5 | | 21 | 8 | | 22 | 8 | | 23 | 15 | | 24 | 30 | | 25 | 4 | | 26 | 1 | | 27 | 17 | | 28 | 5 | | 29 | 3 | | 30 | 2 | | 31 | 4 | | 32 | 5 | | 33 | 5 | | 34 | 12 | | 35 | 18 | | 36 | 4 | | 37 | 18 | | 38 | 14 | | 39 | 12 | | 40 | 13 | | 41 | 10 | | 42 | 2 | | 43 | 2 | | 44 | 17 | | 45 | 7 | | 46 | 5 | | 47 | 2 | | 48 | 1 | | 49 | 32 |
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| 88.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5583333333333333 | | totalSentences | 120 | | uniqueOpeners | 67 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 82 | | matches | | 0 | "Then back at the wall," |
| | ratio | 0.012 | |
| 59.02% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 82 | | matches | | 0 | "He was thirty-four and tidy" | | 1 | "He pointed with his pen" | | 2 | "He nodded toward a scatter" | | 3 | "She crouched at the edge" | | 4 | "She had learned that early." | | 5 | "It began at the mouth" | | 6 | "She pointed with her pen" | | 7 | "She rose, knees clicking, and" | | 8 | "He was well dressed for" | | 9 | "His shoes were oxfords with" | | 10 | "He had not walked down" | | 11 | "She lifted the man's wrist" | | 12 | "She turned the wrist over" | | 13 | "She looked at it" | | 14 | "He was quiet for several" | | 15 | "She surprised herself with the" | | 16 | "She went through the coat" | | 17 | "She opened it under the" | | 18 | "It was a reader's ticket," | | 19 | "She folded the paper away" |
| | ratio | 0.402 | |
| 63.66% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 82 | | matches | | 0 | "The Tube station had no" | | 1 | "Someone had pried the enamel" | | 2 | "Detective Harlow Quinn descended the" | | 3 | "Floodlights had been rigged along" | | 4 | "A curved ceiling furred with" | | 5 | "DS Adeyemi came toward her," | | 6 | "He was thirty-four and tidy" | | 7 | "Quinn liked him for it," | | 8 | "He pointed with his pen" | | 9 | "He nodded toward a scatter" | | 10 | "Quinn said nothing." | | 11 | "She crouched at the edge" | | 12 | "She had learned that early." | | 13 | "Bodies told you what had" | | 14 | "Floors told you what everyone" | | 15 | "The dust on the platform" | | 16 | "The sweeping was neat." | | 17 | "It began at the mouth" | | 18 | "She pointed with her pen" | | 19 | "She rose, knees clicking, and" |
| | ratio | 0.793 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 1 | | matches | | 0 | "But down the center ran a broad swept lane, six feet wide, as though a great many people had crossed it and someone had come after them with a broom." |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn walked, and the needle followed the angle of her body, steady as a plumb line" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |