| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "He stepped aside [aside]" |
| | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.25 | | effectiveRatio | 0.1 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1241 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 87.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1241 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "flickered" | | 1 | "pulsed" | | 2 | "mosaic" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | 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 | 1249 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 1156 | | uniqueNames | 11 | | maxNameDensity | 0.43 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Tomás | 1 | | Herrera | 5 | | Soho | 2 | | Camden | 2 | | Spanish | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 | | Warm | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a man in a hurry, which told" |
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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 | 1249 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 94 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 26.57 | | std | 27.08 | | cv | 1.019 | | sampleLengths | | 0 | 21 | | 1 | 97 | | 2 | 8 | | 3 | 10 | | 4 | 114 | | 5 | 37 | | 6 | 65 | | 7 | 2 | | 8 | 2 | | 9 | 46 | | 10 | 18 | | 11 | 6 | | 12 | 6 | | 13 | 29 | | 14 | 56 | | 15 | 48 | | 16 | 1 | | 17 | 9 | | 18 | 39 | | 19 | 6 | | 20 | 45 | | 21 | 6 | | 22 | 73 | | 23 | 24 | | 24 | 11 | | 25 | 34 | | 26 | 34 | | 27 | 46 | | 28 | 15 | | 29 | 2 | | 30 | 5 | | 31 | 37 | | 32 | 1 | | 33 | 13 | | 34 | 46 | | 35 | 9 | | 36 | 33 | | 37 | 2 | | 38 | 3 | | 39 | 23 | | 40 | 13 | | 41 | 97 | | 42 | 1 | | 43 | 14 | | 44 | 23 | | 45 | 11 | | 46 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 78 | | matches | | |
| 95.83% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 192 | | matches | | 0 | "was checking" | | 1 | "was working" | | 2 | "was running" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 94 | | ratio | 0.074 | | matches | | 0 | "When the bar door swung shut behind him she caught a slice of the interior through the gap — old maps on the walls, photographs in black and white — and then the night closed over it again." | | 1 | "It hit the paving ahead of her with a bright click and skittered — a disc the size of a two-pound coin, yellowed like an old tooth." | | 2 | "He landed, spun, his hand already out—" | | 3 | "—then made his choice." | | 4 | "He cut off the towpath at the lock and threw himself through a gap in the hoarding — plywood painted to look like brickwork, a flaking sign screwed to it, LONDON UNDERGROUND: NO PUBLIC ACCESS — and dropped into the dark on the far side." | | 5 | "Amber light climbed the stairs from somewhere deep, and it flickered, but it didn't move the way flame moves — it pulsed, slow, like something breathing." | | 6 | "Stalls crowded the platform and spilled down onto the trackbed — glass jars with things turning slow circles inside them, a tray of teeth sorted by size, bottles that fogged from within — and the crowd moved between them in hoods and high collars, human-shaped, most of them." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1152 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Ahead, far below, Herrera's" |
| | adverbCount | 33 | | adverbRatio | 0.028645833333333332 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0008680555555555555 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 13.29 | | std | 10.73 | | cv | 0.808 | | sampleLengths | | 0 | 21 | | 1 | 24 | | 2 | 35 | | 3 | 38 | | 4 | 8 | | 5 | 10 | | 6 | 23 | | 7 | 6 | | 8 | 3 | | 9 | 22 | | 10 | 33 | | 11 | 9 | | 12 | 2 | | 13 | 16 | | 14 | 8 | | 15 | 29 | | 16 | 4 | | 17 | 20 | | 18 | 6 | | 19 | 35 | | 20 | 2 | | 21 | 2 | | 22 | 18 | | 23 | 28 | | 24 | 14 | | 25 | 4 | | 26 | 6 | | 27 | 6 | | 28 | 22 | | 29 | 7 | | 30 | 13 | | 31 | 23 | | 32 | 6 | | 33 | 14 | | 34 | 14 | | 35 | 27 | | 36 | 7 | | 37 | 1 | | 38 | 4 | | 39 | 3 | | 40 | 2 | | 41 | 7 | | 42 | 1 | | 43 | 17 | | 44 | 14 | | 45 | 6 | | 46 | 45 | | 47 | 6 | | 48 | 7 | | 49 | 15 |
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| 83.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5212765957446809 | | totalSentences | 94 | | uniqueOpeners | 49 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 72 | | matches | | 0 | "—then made his choice." | | 1 | "Then past him, down the" |
| | ratio | 0.028 | |
| 42.22% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 72 | | matches | | 0 | "He paused under the sign" | | 1 | "He turned his collar up" | | 2 | "She gave him half a" | | 3 | "He took the back ways" | | 4 | "He paused at shop glass" | | 5 | "She checked the worn leather" | | 6 | "It told her he was" | | 7 | "He threw a look over" | | 8 | "He dropped onto the towpath" | | 9 | "She took the wall the" | | 10 | "He spat something in Spanish" | | 11 | "He vaulted a site fence" | | 12 | "It hit the paving ahead" | | 13 | "He landed, spun, his hand" | | 14 | "He left it." | | 15 | "She snatched the disc without" | | 16 | "He cut off the towpath" | | 17 | "She reached the gap and" | | 18 | "It smelled of copper and" | | 19 | "She keyed it anyway." |
| | ratio | 0.444 | |
| 78.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 72 | | matches | | 0 | "The green neon above the" | | 1 | "Quinn watched him from the" | | 2 | "He paused under the sign" | | 3 | "He turned his collar up" | | 4 | "She gave him half a" | | 5 | "He took the back ways" | | 6 | "Herrera was working." | | 7 | "He paused at shop glass" | | 8 | "She checked the worn leather" | | 9 | "It told her he was" | | 10 | "Soho fell away behind them" | | 11 | "Camden swallowed them both." | | 12 | "The market stalls stood chained" | | 13 | "A night bus came down" | | 14 | "He threw a look over" | | 15 | "He dropped onto the towpath" | | 16 | "The words reached her in" | | 17 | "She took the wall the" | | 18 | "He spat something in Spanish" | | 19 | "The Saint Christopher medallion had" |
| | ratio | 0.764 | |
| 69.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 72 | | matches | | 0 | "By the time the pavements" |
| | ratio | 0.014 | |
| 90.59% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 3 | | matches | | 0 | "In a dark window her own reflection kept pace with her, the rain having pasted her salt-and-pepper crop flat to her skull, jaw set, brown eyes that gave nothing…" | | 1 | "Behind her stood streetlights she could name and a whole world that would ask why she'd let him go." | | 2 | "Stalls crowded the platform and spilled down onto the trackbed — glass jars with things turning slow circles inside them, a tray of teeth sorted by size, bottle…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |