| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" |
| | dialogueSentences | 22 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0.125 | | effectiveRatio | 0.091 | |
| 90.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1039 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | |
| 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) | |
| 3.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1039 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "fractured" | | 1 | "chill" | | 2 | "unravel" | | 3 | "tapestry" | | 4 | "silence" | | 5 | "whisper" | | 6 | "navigating" | | 7 | "sense of" | | 8 | "foreboding" | | 9 | "weight" | | 10 | "familiar" | | 11 | "throbbed" | | 12 | "stark" | | 13 | "glint" | | 14 | "scanning" | | 15 | "clandestine" | | 16 | "warmth" | | 17 | "flickered" | | 18 | "unreadable" |
| |
| 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 | 85 | | matches | (empty) | |
| 75.63% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | | 0 | "tended to" | | 1 | "seem to" | | 2 | "began to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1033 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 838 | | uniqueNames | 17 | | maxNameDensity | 1.19 | | worstName | "Herrera" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 9 | | Soho | 1 | | Herrera | 10 | | Tomás | 2 | | Rupert | 1 | | Street | 1 | | Browning | 1 | | Eighteen | 1 | | London | 2 | | Raven | 1 | | Nest | 1 | | Morris | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Tomás" | | 4 | "Raven" | | 5 | "Nest" | | 6 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Rupert" | | 2 | "Street" | | 3 | "London" | | 4 | "Market" |
| | globalScore | 0.903 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | 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 | 1033 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 28.69 | | std | 18.93 | | cv | 0.66 | | sampleLengths | | 0 | 60 | | 1 | 57 | | 2 | 57 | | 3 | 56 | | 4 | 3 | | 5 | 59 | | 6 | 15 | | 7 | 45 | | 8 | 39 | | 9 | 51 | | 10 | 47 | | 11 | 57 | | 12 | 6 | | 13 | 24 | | 14 | 9 | | 15 | 20 | | 16 | 47 | | 17 | 39 | | 18 | 13 | | 19 | 8 | | 20 | 30 | | 21 | 29 | | 22 | 2 | | 23 | 39 | | 24 | 8 | | 25 | 20 | | 26 | 15 | | 27 | 16 | | 28 | 13 | | 29 | 11 | | 30 | 9 | | 31 | 44 | | 32 | 10 | | 33 | 34 | | 34 | 8 | | 35 | 33 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 85 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 149 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 99 | | ratio | 0.061 | | matches | | 0 | "Ahead, a figure—Herrera, Tomás Herrera—darted across Rupert Street, disappearing into a narrow alleyway." | | 1 | "Turning back, filing a report, getting a warrant – procedure." | | 2 | "The woman's eyes—a startling shade of violet—held a knowing glint." | | 3 | "He handed something over – a small, silver object." | | 4 | "The figure’s hands moved, counting out something – money, or something else." | | 5 | "She was almost within earshot when a hand grabbed her arm—a grip like iron." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 844 | | adjectiveStacks | 1 | | stackExamples | | 0 | "long, low-ceilinged tunnel," |
| | adverbCount | 26 | | adverbRatio | 0.030805687203791468 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.017772511848341232 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 10.43 | | std | 5.43 | | cv | 0.521 | | sampleLengths | | 0 | 18 | | 1 | 12 | | 2 | 17 | | 3 | 13 | | 4 | 14 | | 5 | 13 | | 6 | 3 | | 7 | 4 | | 8 | 10 | | 9 | 13 | | 10 | 17 | | 11 | 7 | | 12 | 15 | | 13 | 5 | | 14 | 11 | | 15 | 2 | | 16 | 13 | | 17 | 11 | | 18 | 3 | | 19 | 16 | | 20 | 9 | | 21 | 4 | | 22 | 3 | | 23 | 3 | | 24 | 12 | | 25 | 18 | | 26 | 8 | | 27 | 10 | | 28 | 7 | | 29 | 1 | | 30 | 5 | | 31 | 10 | | 32 | 4 | | 33 | 3 | | 34 | 19 | | 35 | 7 | | 36 | 10 | | 37 | 2 | | 38 | 6 | | 39 | 14 | | 40 | 19 | | 41 | 9 | | 42 | 14 | | 43 | 13 | | 44 | 8 | | 45 | 4 | | 46 | 3 | | 47 | 4 | | 48 | 12 | | 49 | 6 |
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| 63.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.40404040404040403 | | totalSentences | 99 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 68.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 82 | | matches | | 0 | "Her trench coat, usually immaculate," | | 1 | "She ran, boots splashing through" | | 2 | "she bit out, adjusting her" | | 3 | "He was a thread, and" | | 4 | "She pushed into the alley," | | 5 | "It dead-ended at a heavy," | | 6 | "He’d gone down." | | 7 | "She paused, debating." | | 8 | "It was a primal unease," | | 9 | "Her partner, DS Morris, had" | | 10 | "She tested the grate." | | 11 | "It wasn't locked." | | 12 | "She drew a breath, the" | | 13 | "She descended slowly, deliberately, her" | | 14 | "She found herself in a" | | 15 | "It was organized chaos." | | 16 | "She recognized a few types" | | 17 | "He didn’t seem to be" | | 18 | "She ignored the jab, scanning" | | 19 | "He handed something over –" |
| | ratio | 0.378 | |
| 14.88% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 82 | | matches | | 0 | "Rain lashed against Detective Harlow" | | 1 | "Her trench coat, usually immaculate," | | 2 | "She ran, boots splashing through" | | 3 | "she bit out, adjusting her" | | 4 | "this one felt different." | | 5 | "Herrera wasn’t just a cut-rate" | | 6 | "He was a thread, and" | | 7 | "She pushed into the alley," | | 8 | "It dead-ended at a heavy," | | 9 | "The distinctive green neon sign" | | 10 | "A quick scan revealed a" | | 11 | "Quinn ascended the metal steps," | | 12 | "The fire escape opened onto" | | 13 | "Herrera was gone." | | 14 | "Steam rose from it, carrying" | | 15 | "A service access point." | | 16 | "He’d gone down." | | 17 | "She paused, debating." | | 18 | "The air tasted wrong, thick" | | 19 | "It was a primal unease," |
| | ratio | 0.89 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice tight" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.136 | | leniency | 0.273 | | rawRatio | 0 | | effectiveRatio | 0 | |