| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1250 | | 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) | |
| 84.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1250 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "glint" | | 1 | "rhythmic" | | 2 | "flickered" | | 3 | "echoing" |
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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 | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1250 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1250 | | uniqueNames | 21 | | maxNameDensity | 0.72 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 9 | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Morrison | 3 | | Old | 1 | | Compton | 1 | | Street | 1 | | Morris | 5 | | London | 3 | | Saint | 1 | | Christopher | 1 | | European | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Herrera | 4 | | Tube | 1 | | Transport | 1 | | Museum | 1 | | Veil | 2 | | Market | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Morrison" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Herrera" | | 6 | "Market" |
| | places | | 0 | "Raven" | | 1 | "Soho" | | 2 | "Old" | | 3 | "Compton" | | 4 | "Street" | | 5 | "London" | | 6 | "Shaftesbury" | | 7 | "Avenue" | | 8 | "Veil" |
| | globalScore | 1 | | windowScore | 1 | |
| 75.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 2 | | matches | | 0 | "not quite melody, not quite percussion" | | 1 | "not quite percussion" | | 2 | "looked like an abandoned Tube station" |
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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 | 1250 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 96 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 41.67 | | std | 29.36 | | cv | 0.705 | | sampleLengths | | 0 | 53 | | 1 | 8 | | 2 | 83 | | 3 | 4 | | 4 | 52 | | 5 | 28 | | 6 | 4 | | 7 | 90 | | 8 | 37 | | 9 | 7 | | 10 | 45 | | 11 | 54 | | 12 | 6 | | 13 | 63 | | 14 | 16 | | 15 | 75 | | 16 | 59 | | 17 | 5 | | 18 | 71 | | 19 | 31 | | 20 | 100 | | 21 | 47 | | 22 | 14 | | 23 | 34 | | 24 | 71 | | 25 | 4 | | 26 | 74 | | 27 | 75 | | 28 | 8 | | 29 | 32 |
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| 90.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 96 | | matches | | 0 | "being funneled" | | 1 | "been redacted" | | 2 | "been converted" | | 3 | "being sold" |
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| 75.97% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 215 | | matches | | 0 | "was tracking" | | 1 | "was leading" | | 2 | "wasn't running" | | 3 | "was disappearing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 96 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1259 | | adjectiveStacks | 1 | | stackExamples | | 0 | "underground supernatural black market," |
| | adverbCount | 40 | | adverbRatio | 0.03177124702144559 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006354249404289118 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 13.02 | | std | 9.33 | | cv | 0.716 | | sampleLengths | | 0 | 18 | | 1 | 26 | | 2 | 2 | | 3 | 7 | | 4 | 8 | | 5 | 21 | | 6 | 34 | | 7 | 28 | | 8 | 4 | | 9 | 20 | | 10 | 10 | | 11 | 5 | | 12 | 17 | | 13 | 4 | | 14 | 18 | | 15 | 6 | | 16 | 4 | | 17 | 16 | | 18 | 5 | | 19 | 16 | | 20 | 17 | | 21 | 36 | | 22 | 5 | | 23 | 19 | | 24 | 4 | | 25 | 4 | | 26 | 5 | | 27 | 7 | | 28 | 8 | | 29 | 3 | | 30 | 11 | | 31 | 3 | | 32 | 20 | | 33 | 9 | | 34 | 36 | | 35 | 5 | | 36 | 4 | | 37 | 6 | | 38 | 11 | | 39 | 14 | | 40 | 23 | | 41 | 15 | | 42 | 6 | | 43 | 10 | | 44 | 4 | | 45 | 8 | | 46 | 4 | | 47 | 17 | | 48 | 13 | | 49 | 27 |
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| 50.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3958333333333333 | | totalSentences | 96 | | uniqueOpeners | 38 | |
| 36.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 91 | | matches | | | ratio | 0.011 | |
| 88.13% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 91 | | matches | | 0 | "Her shift had ended two" | | 1 | "She'd spotted him outside the" | | 2 | "She'd been watching the place" | | 3 | "He'd noticed her anyway." | | 4 | "Her shoes slapped wet concrete." | | 5 | "She caught a glimpse of" | | 6 | "They were ugly, exhausting things." | | 7 | "Her lungs burned as she" | | 8 | "He was leading her somewhere." | | 9 | "He wanted her to follow." | | 10 | "They'd never found a body." | | 11 | "They'd never found anything." | | 12 | "She landed in a narrow" | | 13 | "She could hear music from" | | 14 | "She'd seen it clearly when" | | 15 | "She pushed through the door." | | 16 | "She reached a landing." | | 17 | "She recognized the architecture from" | | 18 | "She'd believed the senior officer." | | 19 | "She'd stopped believing senior officers" |
| | ratio | 0.33 | |
| 36.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 91 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn ducked under the" | | 2 | "Her shift had ended two" | | 3 | "The figure she was tracking" | | 4 | "She'd spotted him outside the" | | 5 | "She'd been watching the place" | | 6 | "The man had come out" | | 7 | "He'd noticed her anyway." | | 8 | "Quinn pushed off from the" | | 9 | "Her shoes slapped wet concrete." | | 10 | "Rain plastered her closely cropped" | | 11 | "The suspect glanced back." | | 12 | "She caught a glimpse of" | | 13 | "Quinn swore and followed." | | 14 | "They were ugly, exhausting things." | | 15 | "Her lungs burned as she" | | 16 | "People on the sidewalk scattered," | | 17 | "The suspect cut left onto" | | 18 | "He was leading her somewhere." | | 19 | "That was the calculation running" |
| | ratio | 0.846 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 91 | | matches | | 0 | "Now he was fifty meters" | | 1 | "If she walked away now," |
| | ratio | 0.022 | |
| 86.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 4 | | matches | | 0 | "The suspect cut left onto a side street, then right, then left again, working through the maze of Soho's narrow back ways with a confidence that told her he kne…" | | 1 | "She landed in a narrow alley that sloped downward, the kind of forgotten passage that existed in the gaps between London's older buildings." | | 2 | "The amber light came from fixtures mounted at irregular intervals, bulbs that flickered with a frequency that made her eyes ache." | | 3 | "She thought about the radio on her belt, crackling with static that had gotten worse as she descended." |
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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 | |