| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 3 | | adverbTags | | 0 | "she said instead [instead]" | | 1 | "Harlow said slowly [slowly]" | | 2 | "Eva said quietly [quietly]" |
| | dialogueSentences | 54 | | tagDensity | 0.389 | | leniency | 0.778 | | rawRatio | 0.143 | | effectiveRatio | 0.111 | |
| 84.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1935 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slowly" | | 1 | "lazily" | | 2 | "really" | | 3 | "very" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 66.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1935 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "shattered" | | 1 | "silence" | | 2 | "weight" | | 3 | "scanned" | | 4 | "etched" | | 5 | "flickered" | | 6 | "standard" | | 7 | "raced" | | 8 | "pulse" | | 9 | "could feel" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 173 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 173 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 205 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 16 | | totalWords | 1935 | | ratio | 0.008 | | matches | | 0 | "It's not what we think, Harlow. It's underneath." | | 1 | "Rifts and Thresholds: A Compendium of Veil Crossings" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 84 | | wordCount | 1315 | | uniqueNames | 19 | | maxNameDensity | 2.51 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Harlow" | | discoveredNames | | Tube | 1 | | Camden | 2 | | Harlow | 33 | | Quinn | 3 | | Oyster | 1 | | Patel | 12 | | Morris | 3 | | Verdigris | 1 | | Whitechapel | 1 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Eva | 17 | | Polish | 1 | | Thresholds | 1 | | Compendium | 1 | | Veil | 1 | | London | 1 | | Detective | 2 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Oyster" | | 4 | "Patel" | | 5 | "Morris" | | 6 | "Kowalski" | | 7 | "Eva" |
| | places | | 0 | "Whitechapel" | | 1 | "British" | | 2 | "London" |
| | globalScore | 0.245 | | windowScore | 0 | |
| 98.98% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a human knucklebone, etched w" | | 1 | "shadows that seemed to move independently" |
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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 | 1935 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 205 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 29.32 | | std | 16.5 | | cv | 0.563 | | sampleLengths | | 0 | 71 | | 1 | 53 | | 2 | 34 | | 3 | 41 | | 4 | 34 | | 5 | 47 | | 6 | 35 | | 7 | 5 | | 8 | 25 | | 9 | 54 | | 10 | 68 | | 11 | 7 | | 12 | 10 | | 13 | 42 | | 14 | 25 | | 15 | 47 | | 16 | 41 | | 17 | 22 | | 18 | 54 | | 19 | 10 | | 20 | 17 | | 21 | 30 | | 22 | 39 | | 23 | 17 | | 24 | 42 | | 25 | 8 | | 26 | 58 | | 27 | 12 | | 28 | 26 | | 29 | 23 | | 30 | 13 | | 31 | 31 | | 32 | 14 | | 33 | 11 | | 34 | 33 | | 35 | 8 | | 36 | 12 | | 37 | 25 | | 38 | 19 | | 39 | 55 | | 40 | 5 | | 41 | 15 | | 42 | 56 | | 43 | 19 | | 44 | 33 | | 45 | 28 | | 46 | 51 | | 47 | 35 | | 48 | 24 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 173 | | matches | | 0 | "been positioned" | | 1 | "been patched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 234 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 205 | | ratio | 0 | | matches | (empty) | |
| 98.51% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1319 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 55 | | adverbRatio | 0.04169825625473844 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.01288855193328279 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 205 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 205 | | mean | 9.44 | | std | 7.17 | | cv | 0.76 | | sampleLengths | | 0 | 16 | | 1 | 21 | | 2 | 13 | | 3 | 6 | | 4 | 3 | | 5 | 8 | | 6 | 1 | | 7 | 3 | | 8 | 7 | | 9 | 16 | | 10 | 10 | | 11 | 5 | | 12 | 3 | | 13 | 12 | | 14 | 4 | | 15 | 16 | | 16 | 10 | | 17 | 2 | | 18 | 2 | | 19 | 14 | | 20 | 14 | | 21 | 13 | | 22 | 6 | | 23 | 5 | | 24 | 13 | | 25 | 10 | | 26 | 4 | | 27 | 43 | | 28 | 8 | | 29 | 3 | | 30 | 15 | | 31 | 4 | | 32 | 2 | | 33 | 3 | | 34 | 5 | | 35 | 15 | | 36 | 10 | | 37 | 4 | | 38 | 13 | | 39 | 16 | | 40 | 3 | | 41 | 18 | | 42 | 12 | | 43 | 9 | | 44 | 8 | | 45 | 16 | | 46 | 7 | | 47 | 16 | | 48 | 7 | | 49 | 3 |
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| 56.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.36585365853658536 | | totalSentences | 205 | | uniqueOpeners | 75 | |
| 87.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 153 | | matches | | 0 | "Just the corpse and an" | | 1 | "Further along, near the old" | | 2 | "Instead, the younger woman looked" | | 3 | "Then the lights steadied." |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 153 | | matches | | 0 | "She checked her worn leather" | | 1 | "She crouched, knees cracking." | | 2 | "She noted the absence of" | | 3 | "His face looked pale under" | | 4 | "She studied the blood pattern." | | 5 | "It spread too evenly, as" | | 6 | "She scanned upward." | | 7 | "She had seen plenty of" | | 8 | "She moved closer to the" | | 9 | "She pried the fingers open" | | 10 | "It pointed instead toward the" | | 11 | "she said, holding it up" | | 12 | "She had already spotted the" | | 13 | "It had rolled halfway beneath" | | 14 | "She palmed it before Patel" | | 15 | "Her fingers tingled where it" | | 16 | "she said, voice steady" | | 17 | "They walked the length of" | | 18 | "Her watch felt heavier on" | | 19 | "She glanced at it again." |
| | ratio | 0.268 | |
| 51.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 125 | | totalSentences | 153 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped off" | | 2 | "The air tasted metallic, laced" | | 3 | "She checked her worn leather" | | 4 | "The call had come in" | | 5 | "Harlow swept her torch across" | | 6 | "The body lay sprawled near" | | 7 | "Blood pooled beneath the torso," | | 8 | "She crouched, knees cracking." | | 9 | "The victim was male, mid-thirties," | | 10 | "She noted the absence of" | | 11 | "Sergeant Patel appeared from the" | | 12 | "His face looked pale under" | | 13 | "Harlow didn't look up" | | 14 | "She studied the blood pattern." | | 15 | "It spread too evenly, as" | | 16 | "Gravity should have pulled it" | | 17 | "Patel shifted his weight." | | 18 | "Harlow rose slowly, military precision" | | 19 | "She scanned upward." |
| | ratio | 0.817 | |
| 65.36% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 153 | | matches | | 0 | "As if reality had been" | | 1 | "Now the words crawled back" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 2 | | matches | | 0 | "The edges looked burned, as if something incredibly hot had clawed through concrete." | | 1 | "The compass in her pocket grew warmer with each step, its needle spinning faster and faster, pointing at truths she had spent three years trying to forget." |
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| 77.38% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 2 | | matches | | 0 | "she said, voice steady" | | 1 | "she said, voice hard" |
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| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 3 | | fancyTags | | 0 | "Patel suggested (suggest)" | | 1 | "Eva continued (continue)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 54 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.25 | | effectiveRatio | 0.111 | |