| 63.64% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 3 | | adverbTags | | 0 | "Patel pointed upward [upward]" | | 1 | "Harlow crouched again [again]" | | 2 | "she said softly [softly]" |
| | dialogueSentences | 44 | | tagDensity | 0.318 | | leniency | 0.636 | | rawRatio | 0.214 | | effectiveRatio | 0.136 | |
| 89.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1936 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "suddenly" | | 1 | "lazily" | | 2 | "softly" | | 3 | "perfectly" |
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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) | |
| 66.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1936 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "fluttered" | | 1 | "familiar" | | 2 | "weight" | | 3 | "etched" | | 4 | "perfect" | | 5 | "stomach" | | 6 | "standard" | | 7 | "trembled" | | 8 | "echoes" | | 9 | "shimmered" |
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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 | 131 | | matches | (empty) | |
| 66.52% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 4 | | narrationSentences | 131 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "seem to" | | 2 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 161 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1936 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 1 | | matches | | 0 | "Behind her, Patel shouted her name once." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 1293 | | uniqueNames | 15 | | maxNameDensity | 1.31 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Quinn | 1 | | Tube | 1 | | Police | 2 | | Patel | 14 | | Harlow | 17 | | London | 1 | | Underground | 1 | | Limehouse | 2 | | Morris | 5 | | Kowalski | 1 | | Oxford-educated | 1 | | Eva | 10 | | Metropolitan | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Patel" | | 3 | "Harlow" | | 4 | "Morris" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Market" |
| | places | | | globalScore | 0.843 | | windowScore | 0.667 | |
| 94.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 2 | | matches | | 0 | "shadows that seemed to twitch when she looked away" | | 1 | "sounded like her own name stretched across" |
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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 | 1936 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 161 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 30.25 | | std | 17.42 | | cv | 0.576 | | sampleLengths | | 0 | 67 | | 1 | 44 | | 2 | 1 | | 3 | 32 | | 4 | 31 | | 5 | 46 | | 6 | 30 | | 7 | 21 | | 8 | 6 | | 9 | 33 | | 10 | 13 | | 11 | 54 | | 12 | 50 | | 13 | 34 | | 14 | 67 | | 15 | 16 | | 16 | 9 | | 17 | 59 | | 18 | 17 | | 19 | 6 | | 20 | 48 | | 21 | 3 | | 22 | 33 | | 23 | 25 | | 24 | 30 | | 25 | 8 | | 26 | 22 | | 27 | 19 | | 28 | 42 | | 29 | 31 | | 30 | 34 | | 31 | 57 | | 32 | 6 | | 33 | 38 | | 34 | 29 | | 35 | 56 | | 36 | 25 | | 37 | 56 | | 38 | 22 | | 39 | 43 | | 40 | 45 | | 41 | 12 | | 42 | 46 | | 43 | 41 | | 44 | 22 | | 45 | 21 | | 46 | 52 | | 47 | 12 | | 48 | 55 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 131 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 214 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 161 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1297 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.03777949113338473 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.01156515034695451 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 161 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 161 | | mean | 12.02 | | std | 8.76 | | cv | 0.728 | | sampleLengths | | 0 | 17 | | 1 | 18 | | 2 | 32 | | 3 | 4 | | 4 | 15 | | 5 | 12 | | 6 | 6 | | 7 | 7 | | 8 | 1 | | 9 | 9 | | 10 | 12 | | 11 | 11 | | 12 | 11 | | 13 | 20 | | 14 | 5 | | 15 | 10 | | 16 | 16 | | 17 | 15 | | 18 | 6 | | 19 | 19 | | 20 | 5 | | 21 | 6 | | 22 | 15 | | 23 | 4 | | 24 | 2 | | 25 | 16 | | 26 | 17 | | 27 | 3 | | 28 | 10 | | 29 | 14 | | 30 | 40 | | 31 | 9 | | 32 | 16 | | 33 | 16 | | 34 | 3 | | 35 | 6 | | 36 | 3 | | 37 | 31 | | 38 | 15 | | 39 | 21 | | 40 | 17 | | 41 | 7 | | 42 | 7 | | 43 | 5 | | 44 | 11 | | 45 | 2 | | 46 | 7 | | 47 | 6 | | 48 | 15 | | 49 | 6 |
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| 54.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.37267080745341613 | | totalSentences | 161 | | uniqueOpeners | 60 | |
| 80.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 124 | | matches | | 0 | "Instead the blood simply stopped" | | 1 | "Instead Harlow turned toward the" | | 2 | "Somewhere in its twisting lanes," |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 124 | | matches | | 0 | "She straightened, jaw tight." | | 1 | "His bearing lacked her military" | | 2 | "She turned the victim’s satchel" | | 3 | "She picked up the smallest" | | 4 | "she repeated, voice low" | | 5 | "She held it up between" | | 6 | "She moved along the platform" | | 7 | "Her breath fogged." | | 8 | "He followed anyway." | | 9 | "She pressed her palm to" | | 10 | "They felt warmer than the" | | 11 | "She remembered different details: chalk" | | 12 | "She pulled her hand away." | | 13 | "He did, though reluctance showed" | | 14 | "She lifted the largest grimoire" | | 15 | "Its cover showed fresh fingerprints" | | 16 | "She had seen prints like" | | 17 | "She had never told anyone." | | 18 | "She stood, brushing dust from" | | 19 | "She met his gaze" |
| | ratio | 0.29 | |
| 8.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 112 | | totalSentences | 124 | | matches | | 0 | "Harlow Quinn crouched beside the" | | 1 | "Blood pooled in the cracked" | | 2 | "The victim, a man in" | | 3 | "She straightened, jaw tight." | | 4 | "The air tasted of rust" | | 5 | "Someone had already photographed the" | | 6 | "Someone had already decided this" | | 7 | "The voice came from behind" | | 8 | "DS Patel stepped over a" | | 9 | "His bearing lacked her military" | | 10 | "Patel pointed upward" | | 11 | "Harlow did not answer immediately." | | 12 | "She turned the victim’s satchel" | | 13 | "Books spilled out: leather-bound volumes" | | 14 | "She picked up the smallest" | | 15 | "Pages fell open to a" | | 16 | "The ink still looked wet." | | 17 | "she repeated, voice low" | | 18 | "Patel shifted his weight." | | 19 | "She held it up between" |
| | ratio | 0.903 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 124 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 4 | | matches | | 0 | "Pages fell open to a hand-drawn map of tunnels that did not appear on any official London Underground chart." | | 1 | "Instead the blood simply stopped at an invisible line, as though something had drunk the rest." | | 2 | "Her worn leather satchel bulged with books that looked suspiciously similar to those scattered around the corpse." | | 3 | "She looked only at Harlow, as though they continued a conversation begun three years earlier in a different tunnel." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "she repeated, voice low" | | 1 | "she told, voice steady as her military bearing demanded" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 44 | | tagDensity | 0.068 | | leniency | 0.136 | | rawRatio | 0.333 | | effectiveRatio | 0.045 | |