| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 779 | | 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) | |
| 93.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 779 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 58 | | matches | (empty) | |
| 44.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 58 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 60 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 779 | | 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 | 29 | | wordCount | 763 | | uniqueNames | 15 | | maxNameDensity | 0.79 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Dean | 1 | | Street | 2 | | Quinn | 6 | | Tomás | 1 | | Herrera | 6 | | Tube | 1 | | Tottenham | 1 | | Court | 1 | | Road | 2 | | Soho | 1 | | Oxford | 1 | | Euston | 1 | | Three | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Herrera" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Tube" | | 3 | "Tottenham" | | 4 | "Court" | | 5 | "Road" | | 6 | "Soho" | | 7 | "Oxford" | | 8 | "Euston" |
| | globalScore | 1 | | windowScore | 1 | |
| 47.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 2 | | matches | | 0 | "fox that seemed to have business of its own" | | 1 | "as if eavesdropping" |
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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 | 779 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 60 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 33.87 | | std | 25.67 | | cv | 0.758 | | sampleLengths | | 0 | 86 | | 1 | 21 | | 2 | 62 | | 3 | 15 | | 4 | 64 | | 5 | 25 | | 6 | 27 | | 7 | 98 | | 8 | 16 | | 9 | 42 | | 10 | 33 | | 11 | 13 | | 12 | 12 | | 13 | 35 | | 14 | 60 | | 15 | 15 | | 16 | 7 | | 17 | 5 | | 18 | 5 | | 19 | 49 | | 20 | 55 | | 21 | 15 | | 22 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 58 | | matches | (empty) | |
| 98.22% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 131 | | matches | | 0 | "was going" | | 1 | "were already fading" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 60 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 766 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.016971279373368148 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0013054830287206266 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 60 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 60 | | mean | 12.98 | | std | 7.52 | | cv | 0.579 | | sampleLengths | | 0 | 21 | | 1 | 22 | | 2 | 21 | | 3 | 5 | | 4 | 17 | | 5 | 21 | | 6 | 9 | | 7 | 24 | | 8 | 29 | | 9 | 15 | | 10 | 5 | | 11 | 10 | | 12 | 28 | | 13 | 8 | | 14 | 13 | | 15 | 7 | | 16 | 6 | | 17 | 12 | | 18 | 15 | | 19 | 12 | | 20 | 11 | | 21 | 36 | | 22 | 15 | | 23 | 11 | | 24 | 25 | | 25 | 16 | | 26 | 13 | | 27 | 2 | | 28 | 12 | | 29 | 15 | | 30 | 10 | | 31 | 13 | | 32 | 10 | | 33 | 3 | | 34 | 10 | | 35 | 4 | | 36 | 8 | | 37 | 19 | | 38 | 10 | | 39 | 6 | | 40 | 13 | | 41 | 8 | | 42 | 9 | | 43 | 17 | | 44 | 13 | | 45 | 15 | | 46 | 7 | | 47 | 3 | | 48 | 2 | | 49 | 5 |
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| 68.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.48333333333333334 | | totalSentences | 60 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 53 | | matches | | 0 | "Then the door opened, and" | | 1 | "Somewhere a bass line thudded" |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 53 | | matches | | 0 | "She knew the walk before" | | 1 | "He didn't hail a cab." | | 2 | "He didn't drop into the" | | 3 | "He walked north through Soho's" | | 4 | "She tried the radio at" | | 5 | "She swore under her breath" | | 6 | "He stopped at a chain-link" | | 7 | "She stopped at the mouth" | | 8 | "He reached inside his coat" | | 9 | "He held up the disc" | | 10 | "She ran her thumb over" | | 11 | "Her ID would mean nothing" | | 12 | "She kept her hand where" | | 13 | "She looked past it into" | | 14 | "She had hesitated." |
| | ratio | 0.283 | |
| 92.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 53 | | matches | | 0 | "The green neon above the" | | 1 | "Harlow Quinn stood in a" | | 2 | "The leather strap of her" | | 3 | "She knew the walk before" | | 4 | "Hands buried in his pockets," | | 5 | "Quinn pushed off the doorframe" | | 6 | "He didn't hail a cab." | | 7 | "He didn't drop into the" | | 8 | "He walked north through Soho's" | | 9 | "Rain drummed on the roofs" | | 10 | "She tried the radio at" | | 11 | "The handset spat static, then" | | 12 | "She swore under her breath" | | 13 | "Camden at this hour belonged" | | 14 | "Water pooled in the potholes" | | 15 | "He stopped at a chain-link" | | 16 | "She stopped at the mouth" | | 17 | "Herrera turned, and for one" | | 18 | "He reached inside his coat" | | 19 | "A disc, carved with lines" |
| | ratio | 0.736 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 53 | | matches | | 0 | "By the time he crossed" | | 1 | "By the time he reached" |
| | ratio | 0.038 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 2 | | matches | | 0 | "Three hours of drunks, delivery bikes and a fox that seemed to have business of its own." | | 1 | "Light and noise spilled from the pub on the corner, but Herrera ducked past it and cut down a side street where half the lamps were dead and the buildings leane…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |