| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.27% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1457 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "suddenly" | | 2 | "lightly" |
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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) | |
| 69.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1457 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "shattered" | | 1 | "etched" | | 2 | "weight" | | 3 | "familiar" | | 4 | "silk" | | 5 | "implication" | | 6 | "could feel" | | 7 | "lurch" |
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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 | 104 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | 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 | 1457 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1026 | | uniqueNames | 10 | | maxNameDensity | 2.05 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Kowalski | 1 | | Quinn | 21 | | Veil | 1 | | Market | 1 | | Eva | 14 | | Protective | 1 | | Three | 1 | | Aurora | 1 | | London | 1 | | Morris | 3 |
| | persons | | 0 | "Kowalski" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Eva" | | 4 | "Three" | | 5 | "Morris" |
| | places | | | globalScore | 0.477 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared near the bottom, listed beside Morris's with a question mark" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.686 | | wordCount | 1457 | | matches | | 0 | "not on the body, but on the compass in her hand" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 32.38 | | std | 16.79 | | cv | 0.519 | | sampleLengths | | 0 | 37 | | 1 | 15 | | 2 | 4 | | 3 | 40 | | 4 | 30 | | 5 | 71 | | 6 | 21 | | 7 | 18 | | 8 | 41 | | 9 | 29 | | 10 | 50 | | 11 | 23 | | 12 | 5 | | 13 | 43 | | 14 | 32 | | 15 | 24 | | 16 | 52 | | 17 | 40 | | 18 | 25 | | 19 | 35 | | 20 | 48 | | 21 | 43 | | 22 | 56 | | 23 | 40 | | 24 | 66 | | 25 | 6 | | 26 | 24 | | 27 | 4 | | 28 | 38 | | 29 | 47 | | 30 | 37 | | 31 | 50 | | 32 | 45 | | 33 | 5 | | 34 | 9 | | 35 | 54 | | 36 | 22 | | 37 | 45 | | 38 | 32 | | 39 | 17 | | 40 | 42 | | 41 | 32 | | 42 | 22 | | 43 | 35 | | 44 | 3 |
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| 95.14% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 104 | | matches | | 0 | "been drawn" | | 1 | "been dragged" | | 2 | "been broken" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 183 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 126 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1030 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.038834951456310676 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007766990291262136 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 11.56 | | std | 7.68 | | cv | 0.664 | | sampleLengths | | 0 | 7 | | 1 | 21 | | 2 | 9 | | 3 | 7 | | 4 | 6 | | 5 | 2 | | 6 | 4 | | 7 | 13 | | 8 | 14 | | 9 | 13 | | 10 | 10 | | 11 | 20 | | 12 | 11 | | 13 | 20 | | 14 | 40 | | 15 | 7 | | 16 | 14 | | 17 | 12 | | 18 | 6 | | 19 | 4 | | 20 | 8 | | 21 | 29 | | 22 | 7 | | 23 | 11 | | 24 | 11 | | 25 | 13 | | 26 | 6 | | 27 | 7 | | 28 | 24 | | 29 | 10 | | 30 | 2 | | 31 | 5 | | 32 | 6 | | 33 | 5 | | 34 | 13 | | 35 | 20 | | 36 | 10 | | 37 | 32 | | 38 | 5 | | 39 | 19 | | 40 | 14 | | 41 | 2 | | 42 | 3 | | 43 | 14 | | 44 | 19 | | 45 | 17 | | 46 | 2 | | 47 | 2 | | 48 | 5 | | 49 | 14 |
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| 59.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.40476190476190477 | | totalSentences | 126 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 95 | | matches | | 0 | "Too precise for a random" | | 1 | "Instead, the crimson had been" | | 2 | "Too many limbs." | | 3 | "Too much hunger." |
| | ratio | 0.042 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 95 | | matches | | 0 | "She weighed the instrument in" | | 1 | "She adjusted them once, then" | | 2 | "Her worn leather satchel bulged" | | 3 | "She prodded the token with" | | 4 | "She produced a slim volume" | | 5 | "She let the implication drift." | | 6 | "She rose and paced the" | | 7 | "She pushed the memory down." | | 8 | "She had dismissed it then" | | 9 | "She tossed the compass lightly," | | 10 | "Her green eyes met Quinn's" | | 11 | "Her friendship with Aurora and" | | 12 | "She crossed to the wall" | | 13 | "She ran her hand over" | | 14 | "Her own name appeared near" | | 15 | "She positioned herself between Eva" | | 16 | "She pulled it free." |
| | ratio | 0.179 | |
| 44.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 95 | | matches | | 0 | "Quinn pried the dead man's" | | 1 | "The small brass compass came" | | 2 | "The needle whipped in frantic" | | 3 | "She weighed the instrument in" | | 4 | "Eva Kowalski lingered three paces" | | 5 | "She adjusted them once, then" | | 6 | "Her worn leather satchel bulged" | | 7 | "Quinn straightened to her full" | | 8 | "The station pressed in around" | | 9 | "She prodded the token with" | | 10 | "Quinn's voice stayed flat" | | 11 | "Eva shifted her weight." | | 12 | "The nervous gesture with her" | | 13 | "Quinn turned the compass over" | | 14 | "The body lay half off" | | 15 | "Quinn crouched again, following one" | | 16 | "The killing had happened elsewhere." | | 17 | "The placement here served another" | | 18 | "Eva knelt beside her, satchel" | | 19 | "She produced a slim volume" |
| | ratio | 0.832 | |
| 52.63% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 95 | | matches | | 0 | "If this murder connected to" |
| | ratio | 0.011 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "Scattered across the concrete lay the unmistakable debris of the Veil Market: shattered glass vials that still leaked violet residue, bundles of dried herbs tha…" | | 1 | "Instead, the crimson had been drawn into deliberate lines that ran along the concrete, forming half-finished circles that led toward the far tunnel wall." | | 2 | "The tiles here showed faint scratches, as if something heavy had been dragged across them recently." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 34 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 0.5 | | effectiveRatio | 0.059 | |