| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 71 | | tagDensity | 0.141 | | leniency | 0.282 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1657 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "sweetly" | | 1 | "very" | | 2 | "sharply" |
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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) | |
| 78.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1657 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "pulsed" | | 1 | "flicked" | | 2 | "velvet" | | 3 | "etched" | | 4 | "trembled" | | 5 | "quivered" | | 6 | "perfect" |
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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 | 1 | | narrationSentences | 142 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 142 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 202 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1656 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1326 | | uniqueNames | 11 | | maxNameDensity | 2.19 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 29 | | Camden | 1 | | Bell | 10 | | Kowalski | 1 | | Eva | 15 | | Vale | 2 | | Highgate | 1 | | Morris | 2 | | Southwark | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Bell" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Morris" |
| | places | | | globalScore | 0.406 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1656 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 202 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 113 | | mean | 14.65 | | std | 18.17 | | cv | 1.24 | | sampleLengths | | 0 | 19 | | 1 | 73 | | 2 | 5 | | 3 | 74 | | 4 | 12 | | 5 | 35 | | 6 | 9 | | 7 | 2 | | 8 | 4 | | 9 | 8 | | 10 | 32 | | 11 | 65 | | 12 | 9 | | 13 | 8 | | 14 | 3 | | 15 | 1 | | 16 | 3 | | 17 | 60 | | 18 | 5 | | 19 | 53 | | 20 | 4 | | 21 | 2 | | 22 | 5 | | 23 | 12 | | 24 | 2 | | 25 | 13 | | 26 | 10 | | 27 | 3 | | 28 | 7 | | 29 | 15 | | 30 | 6 | | 31 | 82 | | 32 | 26 | | 33 | 7 | | 34 | 3 | | 35 | 4 | | 36 | 6 | | 37 | 50 | | 38 | 5 | | 39 | 4 | | 40 | 9 | | 41 | 7 | | 42 | 7 | | 43 | 21 | | 44 | 6 | | 45 | 1 | | 46 | 1 | | 47 | 1 | | 48 | 1 | | 49 | 4 |
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| 75.61% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 12 | | totalSentences | 142 | | matches | | 0 | "been sealed" | | 1 | "were arranged" | | 2 | "was gathered" | | 3 | "been placed" | | 4 | "been etched" | | 5 | "was tiled" | | 6 | "was stenciled" | | 7 | "been dragged" | | 8 | "been closed" | | 9 | "been hidden" | | 10 | "been killed" | | 11 | "been killed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 212 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 202 | | ratio | 0.01 | | matches | | 0 | "They were cold—colder than the air, cold enough to sting through the glove." | | 1 | "His blood had not spilled from the wound; it had emerged from the dark and poured across him afterward." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1331 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.018782870022539443 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004507888805409466 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 202 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 202 | | mean | 8.2 | | std | 5.5 | | cv | 0.671 | | sampleLengths | | 0 | 19 | | 1 | 15 | | 2 | 7 | | 3 | 12 | | 4 | 15 | | 5 | 24 | | 6 | 5 | | 7 | 18 | | 8 | 12 | | 9 | 17 | | 10 | 27 | | 11 | 12 | | 12 | 14 | | 13 | 9 | | 14 | 12 | | 15 | 9 | | 16 | 2 | | 17 | 4 | | 18 | 8 | | 19 | 20 | | 20 | 5 | | 21 | 7 | | 22 | 11 | | 23 | 16 | | 24 | 26 | | 25 | 12 | | 26 | 9 | | 27 | 4 | | 28 | 4 | | 29 | 3 | | 30 | 1 | | 31 | 3 | | 32 | 8 | | 33 | 13 | | 34 | 22 | | 35 | 17 | | 36 | 5 | | 37 | 20 | | 38 | 14 | | 39 | 8 | | 40 | 11 | | 41 | 4 | | 42 | 2 | | 43 | 5 | | 44 | 12 | | 45 | 2 | | 46 | 7 | | 47 | 6 | | 48 | 6 | | 49 | 4 |
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| 44.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3069306930693069 | | totalSentences | 202 | | uniqueOpeners | 62 | |
| 52.08% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 128 | | matches | | 0 | "Then she reached into her" | | 1 | "Somewhere beyond the opening, something" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 128 | | matches | | 0 | "It hung above the tiled" | | 1 | "Her leather watch pressed cool" | | 2 | "It was no larger than" | | 3 | "She moved through the market" | | 4 | "Her curly red hair was" | | 5 | "She wore a brown coat" | | 6 | "She tucked a curl behind" | | 7 | "He was in his fifties," | | 8 | "His coat was dark green" | | 9 | "She had found him twenty" | | 10 | "It crossed in front of" | | 11 | "His right fist was closed" | | 12 | "His left had been cut" | | 13 | "It was small enough to" | | 14 | "It did not point at" | | 15 | "It pointed through him, toward" | | 16 | "They were cold—colder than the" | | 17 | "She pointed to the bare" | | 18 | "They began at the body," | | 19 | "She lifted it aside." |
| | ratio | 0.242 | |
| 34.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 109 | | totalSentences | 128 | | matches | | 0 | "It hung above the tiled" | | 1 | "The clock had no business" | | 2 | "Quinn had studied the abandoned" | | 3 | "The station had been sealed" | | 4 | "Figures in dark coats and" | | 5 | "A woman with silver teeth" | | 6 | "A man in a butcher’s" | | 7 | "Stalls occupied the platform where" | | 8 | "The air smelled of damp" | | 9 | "Quinn kept one hand near" | | 10 | "Her leather watch pressed cool" | | 11 | "Sergeant Bell came behind her," | | 12 | "Each carried a hooked staff." | | 13 | "Neither looked surprised to see" | | 14 | "A bone token lay in" | | 15 | "It was no larger than" | | 16 | "A plainclothes officer had found" | | 17 | "The token had opened a" | | 18 | "Bell stared at it." | | 19 | "Military precision had served Quinn" |
| | ratio | 0.852 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 128 | | matches | | 0 | "By the time Detective Harlow" | | 1 | "Now the wall stood behind" | | 2 | "Now the black opening breathed" |
| | ratio | 0.023 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 1 | | matches | | 0 | "A man in a butcher’s apron held a cage containing something that moved under a black cloth." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | 0 | "Bell whispered (whisper)" |
| | dialogueSentences | 71 | | tagDensity | 0.141 | | leniency | 0.282 | | rawRatio | 0.1 | | effectiveRatio | 0.028 | |