| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1368 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 59.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1368 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "fractured" | | 1 | "gloom" | | 2 | "weight" | | 3 | "calculated" | | 4 | "resolve" | | 5 | "rhythmic" | | 6 | "sanctuary" | | 7 | "vibrated" | | 8 | "silence" | | 9 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 197 | | matches | (empty) | |
| 77.59% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 9 | | hedgeCount | 0 | | narrationSentences | 197 | | filterMatches | | 0 | "watch" | | 1 | "know" | | 2 | "think" | | 3 | "see" |
| | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 197 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 20 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1368 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 54.97% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 82 | | wordCount | 1368 | | uniqueNames | 16 | | maxNameDensity | 1.9 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Soho | 1 | | Harlow | 1 | | Quinn | 26 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 19 | | Morris | 5 | | Tube | 2 | | Veil | 4 | | Market | 3 | | Camden | 2 | | Raven | 1 | | Nest | 1 | | You | 13 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tomás" | | 6 | "Morris" | | 7 | "Market" | | 8 | "Raven" | | 9 | "Nest" | | 10 | "You" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Tube" | | 3 | "Veil" |
| | globalScore | 0.55 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a finger bone, carved with st" |
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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 | 1368 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 197 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 19 | | std | 16.45 | | cv | 0.866 | | sampleLengths | | 0 | 75 | | 1 | 42 | | 2 | 1 | | 3 | 46 | | 4 | 5 | | 5 | 50 | | 6 | 8 | | 7 | 35 | | 8 | 10 | | 9 | 53 | | 10 | 5 | | 11 | 7 | | 12 | 13 | | 13 | 3 | | 14 | 52 | | 15 | 15 | | 16 | 19 | | 17 | 4 | | 18 | 22 | | 19 | 36 | | 20 | 7 | | 21 | 14 | | 22 | 9 | | 23 | 29 | | 24 | 6 | | 25 | 10 | | 26 | 9 | | 27 | 40 | | 28 | 6 | | 29 | 10 | | 30 | 4 | | 31 | 37 | | 32 | 24 | | 33 | 31 | | 34 | 6 | | 35 | 22 | | 36 | 21 | | 37 | 40 | | 38 | 2 | | 39 | 2 | | 40 | 14 | | 41 | 21 | | 42 | 34 | | 43 | 9 | | 44 | 9 | | 45 | 21 | | 46 | 6 | | 47 | 37 | | 48 | 19 | | 49 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 197 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 264 | | matches | | 0 | "wasn't looking" | | 1 | "was getting" | | 2 | "was offering" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 197 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1369 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.018991964937910884 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0014609203798392988 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 197 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 197 | | mean | 6.94 | | std | 4.07 | | cv | 0.586 | | sampleLengths | | 0 | 18 | | 1 | 14 | | 2 | 15 | | 3 | 4 | | 4 | 11 | | 5 | 13 | | 6 | 5 | | 7 | 13 | | 8 | 20 | | 9 | 4 | | 10 | 1 | | 11 | 8 | | 12 | 2 | | 13 | 13 | | 14 | 8 | | 15 | 15 | | 16 | 5 | | 17 | 4 | | 18 | 17 | | 19 | 8 | | 20 | 8 | | 21 | 13 | | 22 | 8 | | 23 | 2 | | 24 | 6 | | 25 | 6 | | 26 | 12 | | 27 | 9 | | 28 | 7 | | 29 | 3 | | 30 | 9 | | 31 | 5 | | 32 | 13 | | 33 | 11 | | 34 | 5 | | 35 | 10 | | 36 | 5 | | 37 | 7 | | 38 | 3 | | 39 | 10 | | 40 | 3 | | 41 | 5 | | 42 | 6 | | 43 | 12 | | 44 | 12 | | 45 | 7 | | 46 | 10 | | 47 | 3 | | 48 | 8 | | 49 | 4 |
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| 32.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 34 | | diversityRatio | 0.19289340101522842 | | totalSentences | 197 | | uniqueOpeners | 38 | |
| 54.35% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 184 | | matches | | 0 | "Just her pen, her notebook," | | 1 | "Then I'm doing it my" | | 2 | "Just keep the surface clean." |
| | ratio | 0.016 | |
| 39.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 83 | | totalSentences | 184 | | matches | | 0 | "She checked her wrist." | | 1 | "He moved with the economy" | | 2 | "He touched the silver chain" | | 3 | "You shouldn't be here, Quinn." | | 4 | "She planted her boots on" | | 5 | "She leveled her service weapon" | | 6 | "Her jaw tightened, the sharp" | | 7 | "I have a warrant for" | | 8 | "It was a dry, rasping" | | 9 | "He raised his hands, palms" | | 10 | "You know it." | | 11 | "Her military bearing held her" | | 12 | "She had been chasing the" | | 13 | "You work with the clique." | | 14 | "I work for people who" | | 15 | "He pointed to the entrance" | | 16 | "She holstered her weapon." | | 17 | "It was a calculated move." | | 18 | "She wanted to see his" | | 19 | "I told you." |
| | ratio | 0.451 | |
| 25.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 160 | | totalSentences | 184 | | matches | | 0 | "Detective Harlow Quinn sprinted across" | | 1 | "Water dripped from the brim" | | 2 | "She checked her wrist." | | 3 | "The worn leather strap of" | | 4 | "Quinn spotted the figure ahead." | | 5 | "Tomás Herrera ducked into the" | | 6 | "He moved with the economy" | | 7 | "Quinn closed the distance." | | 8 | "Tomás stopped near a stack" | | 9 | "Water streamed down his face," | | 10 | "He touched the silver chain" | | 11 | "The Saint Christopher medallion flashed" | | 12 | "You shouldn't be here, Quinn." | | 13 | "Quinn didn't slow down." | | 14 | "She planted her boots on" | | 15 | "She leveled her service weapon" | | 16 | "The steel barrel gleamed under" | | 17 | "Her jaw tightened, the sharp" | | 18 | "I have a warrant for" | | 19 | "It was a dry, rasping" |
| | ratio | 0.87 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 184 | | matches | | 0 | "Because you think you're tough" | | 1 | "Because it's a trap." | | 2 | "If you're telling the truth," | | 3 | "If you call them, they" |
| | ratio | 0.022 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 2 | | matches | | 0 | "Her military bearing held her upright despite the exhaustion weighing down her shoulders." | | 1 | "The only sound was the thrumming from below, a heartbeat that didn't belong to either of them." |
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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 | |