| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 80.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1503 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slightly" | | 1 | "suddenly" | | 2 | "slowly" | | 3 | "very" |
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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) | |
| 60.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1503 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "fractured" | | 1 | "streaming" | | 2 | "footsteps" | | 3 | "stomach" | | 4 | "could feel" | | 5 | "echoing" | | 6 | "charged" | | 7 | "trembled" | | 8 | "weight" | | 9 | "database" | | 10 | "complex" | | 11 | "echo" |
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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 | 107 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1503 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1503 | | uniqueNames | 18 | | maxNameDensity | 0.4 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Quinn | 5 | | Old | 1 | | Compton | 1 | | Street | 2 | | Raven | 1 | | Nest | 4 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | Morris | 3 | | Soho | 2 | | Camden | 3 | | High | 1 | | Tube | 2 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Morris" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Soho" | | 6 | "Camden" | | 7 | "High" |
| | globalScore | 1 | | windowScore | 1 | |
| 21.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 4 | | matches | | 0 | "looked like it hadn't been touched in a d" | | 1 | "something like burnt copper and old paper an" | | 2 | "lines that seemed to shift in her peripheral vision" | | 3 | "herbs that seemed to twitch, knives with blades that didn't reflect her torch beam the way steel should" |
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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 | 1503 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 107 | | matches | | 0 | "leaving that bar" | | 1 | "trusted that the" |
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| 69.65% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 68.32 | | std | 26.9 | | cv | 0.394 | | sampleLengths | | 0 | 83 | | 1 | 89 | | 2 | 78 | | 3 | 68 | | 4 | 76 | | 5 | 57 | | 6 | 2 | | 7 | 97 | | 8 | 57 | | 9 | 87 | | 10 | 82 | | 11 | 60 | | 12 | 104 | | 13 | 87 | | 14 | 49 | | 15 | 2 | | 16 | 72 | | 17 | 101 | | 18 | 83 | | 19 | 46 | | 20 | 81 | | 21 | 42 |
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| 85.59% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 107 | | matches | | 0 | "been trained" | | 1 | "been removed" | | 2 | "been touched" | | 3 | "was shaped" | | 4 | "been, lost" | | 5 | "been covered" | | 6 | "been redacted" |
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| 12.58% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 249 | | matches | | 0 | "was trailing" | | 1 | "was heading" | | 2 | "was going" | | 3 | "was slipping" | | 4 | "was still falling" | | 5 | "was speaking" | | 6 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 107 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1508 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.03050397877984085 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.005968169761273209 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 14.05 | | std | 9.67 | | cv | 0.689 | | sampleLengths | | 0 | 15 | | 1 | 20 | | 2 | 10 | | 3 | 4 | | 4 | 23 | | 5 | 2 | | 6 | 9 | | 7 | 17 | | 8 | 2 | | 9 | 11 | | 10 | 26 | | 11 | 12 | | 12 | 21 | | 13 | 5 | | 14 | 6 | | 15 | 27 | | 16 | 4 | | 17 | 5 | | 18 | 31 | | 19 | 9 | | 20 | 6 | | 21 | 25 | | 22 | 5 | | 23 | 23 | | 24 | 4 | | 25 | 3 | | 26 | 2 | | 27 | 19 | | 28 | 21 | | 29 | 27 | | 30 | 1 | | 31 | 5 | | 32 | 14 | | 33 | 15 | | 34 | 5 | | 35 | 8 | | 36 | 9 | | 37 | 2 | | 38 | 7 | | 39 | 22 | | 40 | 5 | | 41 | 14 | | 42 | 19 | | 43 | 4 | | 44 | 26 | | 45 | 1 | | 46 | 10 | | 47 | 25 | | 48 | 21 | | 49 | 8 |
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| 46.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.37383177570093457 | | totalSentences | 107 | | uniqueOpeners | 40 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 98 | | matches | | 0 | "Somewhere ahead, footsteps splashed through" | | 1 | "Always carrying a medical bag" | | 2 | "Too deliberate for that." |
| | ratio | 0.031 | |
| 89.39% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 98 | | matches | | 0 | "She checked her watch." | | 1 | "She'd been sitting in an" | | 2 | "She'd seen him a dozen" | | 3 | "She knew what the brass" | | 4 | "They'd ask for evidence." | | 5 | "They'd ask for probable cause." | | 6 | "They'd look at her with" | | 7 | "Her shoes were wrong for" | | 8 | "She kept her breathing even," | | 9 | "He moved like someone who'd" | | 10 | "She caught glimpses of him" | | 11 | "He was heading north, away" | | 12 | "She realized it with a" | | 13 | "It hammered the awnings of" | | 14 | "Her jacket was soaked through," | | 15 | "They descended into blackness, the" | | 16 | "It carried a smell she" | | 17 | "She pulled her torch from" | | 18 | "Her partner would have laughed" | | 19 | "He'd been wrong about that," |
| | ratio | 0.327 | |
| 51.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 98 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn pressed her back" | | 2 | "She checked her watch." | | 3 | "The worn leather band had" | | 4 | "The suspect had a three-minute" | | 5 | "She'd been sitting in an" | | 6 | "She'd seen him a dozen" | | 7 | "The Saint Christopher medallion had" | | 8 | "The chase had been instinct." | | 9 | "She knew what the brass" | | 10 | "They'd ask for evidence." | | 11 | "They'd ask for probable cause." | | 12 | "They'd look at her with" | | 13 | "Quinn rounded the corner and" | | 14 | "Her shoes were wrong for" | | 15 | "Court shoes, not boots, because" | | 16 | "Water squelched with every step." | | 17 | "She kept her breathing even," | | 18 | "The suspect was fast." | | 19 | "He moved like someone who'd" |
| | ratio | 0.816 | |
| 51.02% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 98 | | matches | | 0 | "If she went down there," |
| | ratio | 0.01 | |
| 18.14% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 11 | | matches | | 0 | "Always carrying a medical bag that looked too heavy for legitimate work." | | 1 | "She knew what the brass would say if she told them she was trailing a former paramedic connected to people who moved through the city like smoke." | | 2 | "He was heading north, away from the Nest, away from any route that made sense for someone trying to disappear into a crowded part of the city." | | 3 | "They descended into blackness, the kind of absolute dark that swallowed light." | | 4 | "It carried a smell she couldn't place, something like burnt copper and old paper and something else beneath it, something that made the hair on her forearms sta…" | | 5 | "A hidden world running parallel to the one she'd sworn to protect, operating by rules she didn't understand, populated by people who could vanish like Herrera h…" | | 6 | "About the patients he'd treated with methods that got his NHS license revoked, patients whose charts had vanished from every record system she could access." | | 7 | "The symbol she'd noticed from above was more complex up close, a knot of intersecting lines that seemed to shift in her peripheral vision." | | 8 | "The old ticket barriers were still there, rusted and bent, and beyond them the space widened into an area that had no business existing beneath a closed Tube st…" | | 9 | "The walls had been covered with fabric, dark red and purple, pinned up with nails that looked handmade, irregular and dark." | | 10 | "They sold things she couldn't identify, jars of liquid that caught the light in impossible colors, bundles of dried herbs that seemed to twitch, knives with bla…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |