| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 79.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1191 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slightly" | | 1 | "quickly" | | 2 | "really" | | 3 | "slowly" |
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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) | |
| 74.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1191 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "flickered" | | 1 | "fleeting" | | 2 | "scanned" | | 3 | "gloom" | | 4 | "clandestine" | | 5 | "hulking" |
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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 | 127 | | matches | (empty) | |
| 86.61% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 127 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1189 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1189 | | uniqueNames | 14 | | maxNameDensity | 0.76 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 9 | | Quinn | 1 | | London | 2 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Shoreditch | 1 | | Underground | 2 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 4 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" | | 7 | "Morris" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Shoreditch" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 1 | | matches | | 0 | "eyes that seemed to absorb the light" |
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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 | 1189 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 127 | | matches | | |
| 33.50% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 59.45 | | std | 15.89 | | cv | 0.267 | | sampleLengths | | 0 | 82 | | 1 | 81 | | 2 | 56 | | 3 | 70 | | 4 | 52 | | 5 | 69 | | 6 | 29 | | 7 | 66 | | 8 | 51 | | 9 | 51 | | 10 | 59 | | 11 | 73 | | 12 | 66 | | 13 | 67 | | 14 | 46 | | 15 | 67 | | 16 | 21 | | 17 | 40 | | 18 | 68 | | 19 | 75 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 127 | | matches | | |
| 60.38% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 191 | | matches | | 0 | "was leading" | | 1 | "was haggling" | | 2 | "was standing" | | 3 | "were saying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 127 | | ratio | 0.008 | | matches | | 0 | "This wasn't a panicked flight; it was a controlled retreat." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1191 | | adjectiveStacks | 1 | | stackExamples | | 0 | "young, desperate paramedic, trying" |
| | adverbCount | 25 | | adverbRatio | 0.020990764063811923 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007556675062972292 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 9.36 | | std | 5.86 | | cv | 0.626 | | sampleLengths | | 0 | 15 | | 1 | 26 | | 2 | 17 | | 3 | 10 | | 4 | 14 | | 5 | 11 | | 6 | 24 | | 7 | 10 | | 8 | 13 | | 9 | 9 | | 10 | 14 | | 11 | 5 | | 12 | 14 | | 13 | 13 | | 14 | 9 | | 15 | 5 | | 16 | 10 | | 17 | 14 | | 18 | 11 | | 19 | 20 | | 20 | 6 | | 21 | 11 | | 22 | 5 | | 23 | 3 | | 24 | 7 | | 25 | 2 | | 26 | 2 | | 27 | 14 | | 28 | 13 | | 29 | 1 | | 30 | 5 | | 31 | 8 | | 32 | 6 | | 33 | 5 | | 34 | 7 | | 35 | 7 | | 36 | 9 | | 37 | 12 | | 38 | 23 | | 39 | 9 | | 40 | 6 | | 41 | 7 | | 42 | 7 | | 43 | 6 | | 44 | 17 | | 45 | 5 | | 46 | 4 | | 47 | 14 | | 48 | 20 | | 49 | 8 |
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| 48.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3543307086614173 | | totalSentences | 127 | | uniqueOpeners | 45 | |
| 86.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 115 | | matches | | 0 | "Just a rusted drainpipe and" | | 1 | "Just a smooth, featureless circle" | | 2 | "Then she saw him." |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 115 | | matches | | 0 | "She pushed off, her lungs" | | 1 | "He just ran, his movements" | | 2 | "He was leading her somewhere." | | 3 | "He ducked into a narrow" | | 4 | "He had vanished." | | 5 | "She approached the back wall," | | 6 | "She kicked the bin bags." | | 7 | "Her gaze fell to the" | | 8 | "It was heavy, but it" | | 9 | "Her worn leather watch read" | | 10 | "She holstered her pistol." | | 11 | "It would be useless in" | | 12 | "She took a small, powerful" | | 13 | "She descended quickly, her movements" | | 14 | "She was in a disused" | | 15 | "It was alive." | | 16 | "She’d heard whispers of it" | | 17 | "She hadn't believed it." | | 18 | "Her torch beam caught a" | | 19 | "He was standing at a" |
| | ratio | 0.296 | |
| 64.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 115 | | matches | | 0 | "The soles of Harlow Quinn’s" | | 1 | "A hundred yards ahead, a" | | 2 | "Rain slicked the London streets," | | 3 | "The suspect was fast, unnaturally" | | 4 | "She pushed off, her lungs" | | 5 | "The distinctive green neon sign" | | 6 | "Harlow followed, her sharp jaw" | | 7 | "This one had been at" | | 8 | "The one with the symbols" | | 9 | "The one that had smelled" | | 10 | "The suspect didn’t look back." | | 11 | "He just ran, his movements" | | 12 | "A taxi blared its horn," | | 13 | "Harlow sidestepped it, her eyes" | | 14 | "He was leading her somewhere." | | 15 | "This wasn't a panicked flight;" | | 16 | "He ducked into a narrow" | | 17 | "The smell of stale grease" | | 18 | "Harlow drew her pistol, the" | | 19 | "The alley was a dead" |
| | ratio | 0.791 | |
| 86.96% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 115 | | matches | | 0 | "Now he was here." | | 1 | "To leave the last vestiges" |
| | ratio | 0.017 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 3 | | matches | | 0 | "A hundred yards ahead, a lanky figure in a dark hoodie vaulted over a chained-up bollard, landing with an easy grace that spoke of long practice." | | 1 | "And Herrera, the young, desperate paramedic, trying to save him, his hands slick with blood that had seemed to…" | | 2 | "He said something to the stallholder, who nodded slowly, then turned and began walking towards a darkened tunnel at the end of the platform, one that wasn't on …" |
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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 | |