| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 2 | | adverbTags | | 0 | "Kowalski stepped aside [aside]" | | 1 | "Kowalski said quietly [quietly]" |
| | dialogueSentences | 60 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.1 | | effectiveRatio | 0.067 | |
| 82.29% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1412 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "very" |
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| 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) | |
| 82.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1412 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanning" | | 1 | "etched" | | 2 | "shimmered" | | 3 | "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 | 109 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 149 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1420 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.12% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 859 | | uniqueNames | 2 | | maxNameDensity | 2.68 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Kowalski" | | discoveredNames | | | persons | | | places | (empty) | | globalScore | 0.161 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 1 | | matches | | 0 | "as if searching for alignment" |
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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 | 1420 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 149 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 65 | | mean | 21.85 | | std | 17.38 | | cv | 0.795 | | sampleLengths | | 0 | 58 | | 1 | 44 | | 2 | 33 | | 3 | 7 | | 4 | 2 | | 5 | 49 | | 6 | 41 | | 7 | 4 | | 8 | 1 | | 9 | 16 | | 10 | 33 | | 11 | 17 | | 12 | 6 | | 13 | 25 | | 14 | 21 | | 15 | 36 | | 16 | 15 | | 17 | 15 | | 18 | 2 | | 19 | 21 | | 20 | 39 | | 21 | 28 | | 22 | 6 | | 23 | 38 | | 24 | 3 | | 25 | 29 | | 26 | 54 | | 27 | 12 | | 28 | 25 | | 29 | 6 | | 30 | 5 | | 31 | 7 | | 32 | 37 | | 33 | 3 | | 34 | 17 | | 35 | 61 | | 36 | 8 | | 37 | 1 | | 38 | 44 | | 39 | 51 | | 40 | 10 | | 41 | 38 | | 42 | 2 | | 43 | 8 | | 44 | 7 | | 45 | 23 | | 46 | 37 | | 47 | 44 | | 48 | 5 | | 49 | 1 |
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| 98.83% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 109 | | matches | | 0 | "been closed" | | 1 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 145 | | matches | | 0 | "were doing" | | 1 | "was watching" |
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| 66.16% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 149 | | ratio | 0.027 | | matches | | 0 | "One of them — young, spotty, probably still thinking about his probation — stepped forward with a clipboard and a face that wanted approval." | | 1 | "Someone had tagged the tiled walls in silver spray paint — a series of angular symbols that caught the light at certain angles." | | 2 | "The symbols on the walls repeated — not random tagging, then." | | 3 | "They looked fresh — the dust around them disturbed but not yet settled." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 696 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.021551724137931036 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007183908045977011 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 149 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 149 | | mean | 9.53 | | std | 8.16 | | cv | 0.856 | | sampleLengths | | 0 | 13 | | 1 | 18 | | 2 | 27 | | 3 | 4 | | 4 | 16 | | 5 | 5 | | 6 | 19 | | 7 | 9 | | 8 | 24 | | 9 | 7 | | 10 | 2 | | 11 | 18 | | 12 | 23 | | 13 | 8 | | 14 | 15 | | 15 | 12 | | 16 | 4 | | 17 | 4 | | 18 | 6 | | 19 | 4 | | 20 | 1 | | 21 | 16 | | 22 | 4 | | 23 | 9 | | 24 | 3 | | 25 | 6 | | 26 | 11 | | 27 | 4 | | 28 | 13 | | 29 | 6 | | 30 | 25 | | 31 | 5 | | 32 | 11 | | 33 | 2 | | 34 | 3 | | 35 | 24 | | 36 | 12 | | 37 | 6 | | 38 | 5 | | 39 | 4 | | 40 | 15 | | 41 | 2 | | 42 | 9 | | 43 | 12 | | 44 | 2 | | 45 | 19 | | 46 | 2 | | 47 | 6 | | 48 | 10 | | 49 | 5 |
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| 51.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.37583892617449666 | | totalSentences | 149 | | uniqueOpeners | 56 | |
| 36.23% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 92 | | matches | | | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 92 | | matches | | 0 | "She checked her watch." | | 1 | "They walked single-file past a" | | 2 | "His shoes were clean." | | 3 | "His hands were clean." | | 4 | "He looked calm." | | 5 | "She tilted her head." | | 6 | "She kept her face still." | | 7 | "They looked fresh — the" | | 8 | "She glanced toward the platform," | | 9 | "Her gaze drifted to the" | | 10 | "She did not touch it." | | 11 | "She looked at the compass." | | 12 | "She looked at the dead" | | 13 | "She looked at the symbols" | | 14 | "She stepped around Kowalski, crouched" | | 15 | "She stood, brushing dust from" | | 16 | "She was watching the compass." | | 17 | "It tracked slowly across the" | | 18 | "It swung toward Kowalski." | | 19 | "It swung toward the officer." |
| | ratio | 0.239 | |
| 30.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 92 | | matches | | 0 | "The platform curved away into" | | 1 | "Quinn descended the last of" | | 2 | "The station had been closed" | | 3 | "She checked her watch." | | 4 | "The leather had cracked at" | | 5 | "They walked single-file past a" | | 6 | "Someone had tagged the tiled" | | 7 | "Quinn's gaze snagged on them," | | 8 | "The body lay between the" | | 9 | "A man, mid-forties, well-dressed in" | | 10 | "His shoes were clean." | | 11 | "His hands were clean." | | 12 | "That was the first wrong" | | 13 | "The young officer blinked." | | 14 | "Quinn crouched, knees protesting, and" | | 15 | "He looked calm." | | 16 | "That was the second wrong" | | 17 | "People who died badly in" | | 18 | "She tilted her head." | | 19 | "The skin at his temples" |
| | ratio | 0.859 | |
| 54.35% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 92 | | matches | | 0 | "Whoever had made them had" |
| | ratio | 0.011 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 4 | | matches | | 0 | "One of them — young, spotty, probably still thinking about his probation — stepped forward with a clipboard and a face that wanted approval." | | 1 | "A man, mid-forties, well-dressed in a charcoal coat that didn't belong underground." | | 2 | "The casing had gone green with verdigris, and when she drew closer she saw that the face was etched with the same symbols that crawled along the platform walls." | | 3 | "It tracked slowly across the etched face, brushing past sigil after sigil, as if searching for alignment." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 1 | | matches | | 0 | "Kowalski moved, eyes widening slightly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "Kowalski whispered (whisper)" | | 1 | "Quinn corrected (correct)" |
| | dialogueSentences | 60 | | tagDensity | 0.117 | | leniency | 0.233 | | rawRatio | 0.286 | | effectiveRatio | 0.067 | |