| 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 | 1258 | | 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) | |
| 80.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1258 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "grave" | | 3 | "throbbed" | | 4 | "velvet" |
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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 | 135 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 135 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 143 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1258 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 45.29% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1146 | | uniqueNames | 23 | | maxNameDensity | 2.09 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Old | 1 | | Compton | 1 | | Street | 1 | | Quinn | 24 | | Met | 2 | | Raven | 1 | | Nest | 2 | | Silas | 1 | | Herrera | 11 | | Saint | 2 | | Christopher | 2 | | Shaftesbury | 1 | | Avenue | 1 | | Underground | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Morris | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Met" | | 2 | "Silas" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Old" | | 2 | "Compton" | | 3 | "Street" | | 4 | "Raven" | | 5 | "Nest" | | 6 | "Shaftesbury" | | 7 | "Avenue" | | 8 | "Underground" | | 9 | "Tottenham" | | 10 | "Court" | | 11 | "Road" | | 12 | "Market" |
| | globalScore | 0.453 | | windowScore | 0.667 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 2 | | matches | | 0 | "something like this and did not walk out" | | 1 | "smelled like this same copper cold" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.795 | | wordCount | 1258 | | matches | | 0 | "not to Quinn, but back, an invitation, a warning" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 143 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 19.97 | | std | 14.27 | | cv | 0.715 | | sampleLengths | | 0 | 12 | | 1 | 3 | | 2 | 46 | | 3 | 39 | | 4 | 25 | | 5 | 5 | | 6 | 28 | | 7 | 19 | | 8 | 2 | | 9 | 32 | | 10 | 2 | | 11 | 47 | | 12 | 4 | | 13 | 16 | | 14 | 6 | | 15 | 30 | | 16 | 5 | | 17 | 42 | | 18 | 4 | | 19 | 6 | | 20 | 38 | | 21 | 4 | | 22 | 30 | | 23 | 29 | | 24 | 19 | | 25 | 3 | | 26 | 33 | | 27 | 13 | | 28 | 27 | | 29 | 24 | | 30 | 44 | | 31 | 13 | | 32 | 9 | | 33 | 8 | | 34 | 9 | | 35 | 31 | | 36 | 19 | | 37 | 8 | | 38 | 23 | | 39 | 5 | | 40 | 6 | | 41 | 30 | | 42 | 18 | | 43 | 10 | | 44 | 5 | | 45 | 46 | | 46 | 9 | | 47 | 60 | | 48 | 15 | | 49 | 49 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 135 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 191 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 143 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1154 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 17 | | adverbRatio | 0.014731369150779897 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 143 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 143 | | mean | 8.8 | | std | 6.87 | | cv | 0.781 | | sampleLengths | | 0 | 12 | | 1 | 3 | | 2 | 10 | | 3 | 15 | | 4 | 8 | | 5 | 13 | | 6 | 16 | | 7 | 3 | | 8 | 20 | | 9 | 12 | | 10 | 13 | | 11 | 5 | | 12 | 4 | | 13 | 11 | | 14 | 13 | | 15 | 7 | | 16 | 12 | | 17 | 2 | | 18 | 4 | | 19 | 2 | | 20 | 9 | | 21 | 17 | | 22 | 2 | | 23 | 2 | | 24 | 17 | | 25 | 13 | | 26 | 15 | | 27 | 4 | | 28 | 7 | | 29 | 9 | | 30 | 6 | | 31 | 4 | | 32 | 5 | | 33 | 21 | | 34 | 5 | | 35 | 7 | | 36 | 2 | | 37 | 6 | | 38 | 25 | | 39 | 2 | | 40 | 4 | | 41 | 6 | | 42 | 9 | | 43 | 7 | | 44 | 7 | | 45 | 5 | | 46 | 5 | | 47 | 5 | | 48 | 4 | | 49 | 2 |
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| 57.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.38461538461538464 | | totalSentences | 143 | | uniqueOpeners | 55 | |
| 56.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 118 | | matches | | 0 | "Just rainwater that leaked from" | | 1 | "Then she lifted her hand" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 118 | | matches | | 0 | "Her boots struck pavement and" | | 1 | "He moved fast for a" | | 2 | "Her breath burned." | | 3 | "She knew the bar." | | 4 | "She knew Silas, who poured" | | 5 | "She knew the back room" | | 6 | "He hit the alley beside" | | 7 | "Her eyes adjusted and found" | | 8 | "He did not stop." | | 9 | "His shoulders rose and fell." | | 10 | "He hit the end of" | | 11 | "Her hand reached for his" | | 12 | "His hand shot out and" | | 13 | "She wiped it on her" | | 14 | "He ducked into the Underground" | | 15 | "She dropped flat and rolled" | | 16 | "Her footsteps echoed." | | 17 | "Her radio crackled, dead static." | | 18 | "He stared at the wall." | | 19 | "He pulled something from his" |
| | ratio | 0.288 | |
| 27.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 102 | | totalSentences | 118 | | matches | | 0 | "The rain hammered Soho and" | | 1 | "Harlow Quinn ran." | | 2 | "Her boots struck pavement and" | | 3 | "He moved fast for a" | | 4 | "A smear of red marked" | | 5 | "Quinn's leather watch ticked against" | | 6 | "Her breath burned." | | 7 | "The suspect vaulted a stack" | | 8 | "The green neon above the" | | 9 | "Quinn did not break stride." | | 10 | "She knew the bar." | | 11 | "She knew Silas, who poured" | | 12 | "She knew the back room" | | 13 | "The suspect didn't go for" | | 14 | "He hit the alley beside" | | 15 | "Brick closed around her." | | 16 | "The stink of piss and" | | 17 | "Her eyes adjusted and found" | | 18 | "Seville boy with olive skin" | | 19 | "The file said ex-paramedic, struck" |
| | ratio | 0.864 | |
| 84.75% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 118 | | matches | | 0 | "If she let him descend" | | 1 | "If she followed, she entered" |
| | ratio | 0.017 | |
| 61.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 5 | | matches | | 0 | "Seville boy with olive skin and a Saint Christopher medallion that caught streetlight even in a storm." | | 1 | "He hit the end of the alley and turned left, towards Shaftesbury Avenue, towards the crowds that thinned under the rain." | | 2 | "Her partner died down here, under a different name, in a different market that moved." | | 3 | "If she followed, she entered without backup, without authority, without a token, into a place that sold banned alchemical substances across velvet cloth." | | 4 | "Quinn stared at the stone creature, at the market that breathed, at the token in Herrera's slick palm, at her own raw palm still open from the fall." |
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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 | |