| 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 | 1953 | | 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) | |
| 71.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1953 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "scanned" | | 1 | "flicked" | | 2 | "etched" | | 3 | "tension" | | 4 | "weight" | | 5 | "stark" | | 6 | "quivered" | | 7 | "pulsed" | | 8 | "traced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 259 | | matches | (empty) | |
| 93.22% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 8 | | hedgeCount | 1 | | narrationSentences | 259 | | filterMatches | | 0 | "watch" | | 1 | "look" | | 2 | "know" | | 3 | "see" | | 4 | "think" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 259 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 6 | | totalWords | 1953 | | ratio | 0.003 | | matches | | 0 | "N. Alcove. Second arch. After midnight." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 87 | | wordCount | 1869 | | uniqueNames | 15 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 27 | | Tube | 1 | | Northern | 1 | | Eva | 18 | | Kowalski | 1 | | Market | 5 | | Shah | 12 | | Veil | 2 | | Compass | 3 | | Shade | 2 | | Nadia | 3 | | You | 5 | | One | 5 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Market" | | 6 | "Shah" | | 7 | "Compass" | | 8 | "Shade" | | 9 | "Nadia" | | 10 | "You" | | 11 | "One" |
| | places | | | globalScore | 0.778 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 146 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.512 | | wordCount | 1953 | | matches | | 0 | "not brick but something thinner, like smoke stretched over glass" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 259 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 101 | | mean | 19.34 | | std | 15.66 | | cv | 0.81 | | sampleLengths | | 0 | 10 | | 1 | 68 | | 2 | 50 | | 3 | 42 | | 4 | 20 | | 5 | 31 | | 6 | 6 | | 7 | 26 | | 8 | 11 | | 9 | 10 | | 10 | 43 | | 11 | 4 | | 12 | 11 | | 13 | 21 | | 14 | 15 | | 15 | 4 | | 16 | 6 | | 17 | 37 | | 18 | 13 | | 19 | 40 | | 20 | 46 | | 21 | 7 | | 22 | 21 | | 23 | 22 | | 24 | 16 | | 25 | 12 | | 26 | 2 | | 27 | 6 | | 28 | 3 | | 29 | 68 | | 30 | 3 | | 31 | 10 | | 32 | 8 | | 33 | 17 | | 34 | 32 | | 35 | 9 | | 36 | 16 | | 37 | 34 | | 38 | 34 | | 39 | 5 | | 40 | 5 | | 41 | 2 | | 42 | 4 | | 43 | 29 | | 44 | 27 | | 45 | 9 | | 46 | 2 | | 47 | 12 | | 48 | 27 | | 49 | 29 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 259 | | matches | | 0 | "got scared" | | 1 | "was bricked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 333 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 259 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1955 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 45 | | adverbRatio | 0.023017902813299233 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0010230179028132991 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 259 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 259 | | mean | 7.22 | | std | 4.81 | | cv | 0.666 | | sampleLengths | | 0 | 10 | | 1 | 17 | | 2 | 5 | | 3 | 23 | | 4 | 7 | | 5 | 16 | | 6 | 9 | | 7 | 16 | | 8 | 12 | | 9 | 13 | | 10 | 4 | | 11 | 8 | | 12 | 12 | | 13 | 18 | | 14 | 8 | | 15 | 12 | | 16 | 12 | | 17 | 19 | | 18 | 4 | | 19 | 7 | | 20 | 19 | | 21 | 9 | | 22 | 2 | | 23 | 6 | | 24 | 11 | | 25 | 12 | | 26 | 8 | | 27 | 6 | | 28 | 6 | | 29 | 4 | | 30 | 7 | | 31 | 2 | | 32 | 6 | | 33 | 8 | | 34 | 3 | | 35 | 4 | | 36 | 8 | | 37 | 7 | | 38 | 2 | | 39 | 6 | | 40 | 4 | | 41 | 10 | | 42 | 9 | | 43 | 6 | | 44 | 6 | | 45 | 2 | | 46 | 11 | | 47 | 8 | | 48 | 2 | | 49 | 9 |
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| 44.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3088803088803089 | | totalSentences | 259 | | uniqueOpeners | 80 | |
| 44.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 225 | | matches | | 0 | "Maybe he had two." | | 1 | "Then the foot moved." | | 2 | "Then she looked at the" |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 53 | | totalSentences | 225 | | matches | | 0 | "Her boots hit cracked tile." | | 1 | "She checked the time out" | | 2 | "You got my message." | | 3 | "She stepped forward and the" | | 4 | "You said not to call" | | 5 | "His coat spread around him" | | 6 | "They told me not to" | | 7 | "Her cropped salt-and-pepper hair caught" | | 8 | "Her sharp jaw set." | | 9 | "He lifted his head as" | | 10 | "We got a stabbing." | | 11 | "He paid his way in." | | 12 | "Her shoes scuffed the tile." | | 13 | "She peered over Quinn's shoulder" | | 14 | "It burns out after one" | | 15 | "She used the pen to" | | 16 | "It points to the nearest" | | 17 | "She crouched and her satchel" | | 18 | "It finds portals, weak spots" | | 19 | "I know which one the" |
| | ratio | 0.236 | |
| 93.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 165 | | totalSentences | 225 | | matches | | 0 | "The air beneath Camden tasted" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "Her boots hit cracked tile." | | 3 | "The old Northern line platform" | | 4 | "The light buzzed and threw" | | 5 | "Someone had set up shop" | | 6 | "Chalk sigils smeared half away" | | 7 | "The smell of incense and" | | 8 | "Quinn's left wrist ticked." | | 9 | "The worn leather watch caught" | | 10 | "She checked the time out" | | 11 | "Military precision lived in the" | | 12 | "A uniform held a clipboard" | | 13 | "Eva's green eyes found Quinn" | | 14 | "You got my message." | | 15 | "Eva's voice cracked in the" | | 16 | "She stepped forward and the" | | 17 | "You said not to call" | | 18 | "Quinn's gaze flicked past Eva" | | 19 | "A man in his thirties," |
| | ratio | 0.733 | |
| 44.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 225 | | matches | | 0 | "If the needle spins, it" | | 1 | "Before it opens again." |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 1 | | matches | | 0 | "In the centre of the wall, the bricks darkened in a vertical seam, almost invisible, as if the mortar held a shadow that swallowed light." |
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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 | |