| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "Herrera said softly [softly]" |
| | dialogueSentences | 26 | | tagDensity | 0.577 | | leniency | 1 | | rawRatio | 0.067 | | effectiveRatio | 0.067 | |
| 96.16% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1302 | | 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) | |
| 65.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1302 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "flicker" | | 1 | "intricate" | | 2 | "vibrated" | | 3 | "gloom" | | 4 | "echoed" | | 5 | "scanning" | | 6 | "velvet" | | 7 | "flickered" |
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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 | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 88 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1302 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1100 | | uniqueNames | 19 | | maxNameDensity | 1.27 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Herrera" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 14 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Tomás | 1 | | Herrera | 14 | | Glock | 3 | | Saint | 1 | | Christopher | 1 | | Morris | 1 | | London | 2 | | Victorian | 1 | | Seville | 1 | | Tube | 3 | | Transport | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Glock" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "London" | | 4 | "Seville" | | 5 | "Transport" | | 6 | "Market" |
| | globalScore | 0.864 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1302 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 88 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 31 | | std | 17.85 | | cv | 0.576 | | sampleLengths | | 0 | 54 | | 1 | 16 | | 2 | 43 | | 3 | 75 | | 4 | 13 | | 5 | 20 | | 6 | 38 | | 7 | 27 | | 8 | 11 | | 9 | 12 | | 10 | 7 | | 11 | 37 | | 12 | 49 | | 13 | 18 | | 14 | 23 | | 15 | 50 | | 16 | 20 | | 17 | 19 | | 18 | 52 | | 19 | 5 | | 20 | 87 | | 21 | 30 | | 22 | 29 | | 23 | 28 | | 24 | 36 | | 25 | 34 | | 26 | 54 | | 27 | 32 | | 28 | 1 | | 29 | 51 | | 30 | 36 | | 31 | 47 | | 32 | 21 | | 33 | 44 | | 34 | 17 | | 35 | 33 | | 36 | 25 | | 37 | 18 | | 38 | 14 | | 39 | 23 | | 40 | 27 | | 41 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 77 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 180 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 88 | | ratio | 0.011 | | matches | | 0 | "He did not point a weapon; he simply dropped the token through the iron bars of the grate." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1112 | | adjectiveStacks | 1 | | stackExamples | | 0 | "jagged, black-edged lacerations" |
| | adverbCount | 22 | | adverbRatio | 0.019784172661870502 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008093525179856115 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 88 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 88 | | mean | 14.8 | | std | 6.87 | | cv | 0.464 | | sampleLengths | | 0 | 28 | | 1 | 26 | | 2 | 4 | | 3 | 12 | | 4 | 7 | | 5 | 18 | | 6 | 18 | | 7 | 17 | | 8 | 9 | | 9 | 8 | | 10 | 29 | | 11 | 12 | | 12 | 13 | | 13 | 20 | | 14 | 12 | | 15 | 18 | | 16 | 8 | | 17 | 11 | | 18 | 16 | | 19 | 11 | | 20 | 12 | | 21 | 7 | | 22 | 19 | | 23 | 18 | | 24 | 11 | | 25 | 11 | | 26 | 27 | | 27 | 9 | | 28 | 9 | | 29 | 23 | | 30 | 20 | | 31 | 19 | | 32 | 4 | | 33 | 5 | | 34 | 2 | | 35 | 2 | | 36 | 2 | | 37 | 16 | | 38 | 19 | | 39 | 17 | | 40 | 11 | | 41 | 24 | | 42 | 5 | | 43 | 23 | | 44 | 15 | | 45 | 14 | | 46 | 35 | | 47 | 17 | | 48 | 13 | | 49 | 15 |
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| 64.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4431818181818182 | | totalSentences | 88 | | uniqueOpeners | 39 | |
| 46.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 72 | | matches | | 0 | "Just an unlicensed medic and" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 72 | | matches | | 0 | "Her right hand rested heavily" | | 1 | "He sprinted past the rusted" | | 2 | "Her leather watch face read" | | 3 | "He glanced over his shoulder," | | 4 | "His olive skin glistened with" | | 5 | "His voice carried the soft," | | 6 | "He did not point a" | | 7 | "She checked her radio." | | 8 | "She gripped her flashlight tight" | | 9 | "She stepped off the bottom" | | 10 | "Her sharp jaw set tight" | | 11 | "She caught a flash of" | | 12 | "She pressed forward through the" | | 13 | "She kept her gaze locked" | | 14 | "He ducked into the rusted" | | 15 | "She took a deep breath," | | 16 | "she whispered, looking toward the" |
| | ratio | 0.236 | |
| 8.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 72 | | matches | | 0 | "The heel of Detective Harlow" | | 1 | "Her right hand rested heavily" | | 2 | "The paramedic did not even" | | 3 | "He sprinted past the rusted" | | 4 | "The Saint Christopher medallion dangling" | | 5 | "Quinn squeezed through the narrow" | | 6 | "Pain flared, sharp and cold," | | 7 | "Her leather watch face read" | | 8 | "Tonight, the trail led straight" | | 9 | "Herrera veered left into a" | | 10 | "Quinn said, rounding the corner" | | 11 | "Herrera stood before a padlocked" | | 12 | "He glanced over his shoulder," | | 13 | "His olive skin glistened with" | | 14 | "His voice carried the soft," | | 15 | "Herrera reached into his jacket" | | 16 | "Herrera pulled out a piece" | | 17 | "He did not point a" | | 18 | "A heavy, metallic thrum vibrated" | | 19 | "The iron padlock snapped with" |
| | ratio | 0.903 | |
| 69.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 72 | | matches | | 0 | "Before she could acquire a" |
| | ratio | 0.014 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 4 | | matches | | 0 | "Ten yards ahead, Tomás Herrera vaulted a row of overflowing wheelbins with the fluid grace of a man who spent his life running from the law." | | 1 | "Three years she had spent chasing shadows since DS Morris died in that burned-out warehouse, three years of silent dead ends and missing reports that did not ad…" | | 2 | "The heavy grate swung upward on oiled hinges, releasing a gust of warm, stagnant air that carried the copper tang of old blood, ozone, and wet earth." | | 3 | "On the table lay a young man, his torso torn open in jagged, black-edged lacerations that seeped thick, iridescent bile instead of red blood." |
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| 58.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn said, her voice cutting through the market noise with military precision" | | 1 | "Quinn said, her voice dropping an octave" |
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| 34.62% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn shouted (shout)" | | 1 | "Herrera snapped (snap)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 26 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.231 | | effectiveRatio | 0.231 | |