| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.392 | | leniency | 0.784 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1553 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 48.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1553 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "stark" | | 1 | "electric" | | 2 | "rhythmic" | | 3 | "sanctuary" | | 4 | "silence" | | 5 | "silk" | | 6 | "weight" | | 7 | "crystal" | | 8 | "potential" | | 9 | "gloom" | | 10 | "warmth" | | 11 | "flicker" |
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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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 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 | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 4 | | totalWords | 1545 | | ratio | 0.003 | | matches | | 0 | "Tessa Williams, Senior Associate." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1078 | | uniqueNames | 14 | | maxNameDensity | 2.5 | | worstName | "Rory" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Rory" | | discoveredNames | | Bristol | 1 | | Channel | 1 | | Carter | 2 | | Silas | 9 | | Blackwood | 1 | | Rory | 27 | | Raven | 1 | | Nest | 1 | | Greater | 1 | | London | 1 | | Williams | 2 | | Tessa | 17 | | Laila | 1 | | Senior | 1 |
| | persons | | 0 | "Carter" | | 1 | "Silas" | | 2 | "Blackwood" | | 3 | "Rory" | | 4 | "Raven" | | 5 | "Williams" | | 6 | "Tessa" | | 7 | "Laila" |
| | places | | | globalScore | 0.248 | | windowScore | 0.167 | |
| 81.51% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed nailed to the floorboards" | | 1 | "looked like the future Rory was supposed" |
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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 | 1545 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 25.75 | | std | 17.09 | | cv | 0.664 | | sampleLengths | | 0 | 73 | | 1 | 41 | | 2 | 47 | | 3 | 19 | | 4 | 46 | | 5 | 11 | | 6 | 46 | | 7 | 17 | | 8 | 41 | | 9 | 10 | | 10 | 25 | | 11 | 1 | | 12 | 21 | | 13 | 13 | | 14 | 44 | | 15 | 26 | | 16 | 31 | | 17 | 45 | | 18 | 23 | | 19 | 32 | | 20 | 43 | | 21 | 33 | | 22 | 6 | | 23 | 43 | | 24 | 3 | | 25 | 3 | | 26 | 12 | | 27 | 46 | | 28 | 18 | | 29 | 28 | | 30 | 8 | | 31 | 39 | | 32 | 9 | | 33 | 17 | | 34 | 28 | | 35 | 39 | | 36 | 22 | | 37 | 26 | | 38 | 42 | | 39 | 4 | | 40 | 2 | | 41 | 56 | | 42 | 15 | | 43 | 7 | | 44 | 43 | | 45 | 5 | | 46 | 30 | | 47 | 29 | | 48 | 23 | | 49 | 28 |
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| 97.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 95 | | matches | | 0 | "was gone" | | 1 | "was supposed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 178 | | matches | | |
| 97.86% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 127 | | ratio | 0.016 | | matches | | 0 | "He placed a tumbler of whiskey in front of Rory—no ice, top shelf—and looked at Tessa with eyes that had seen governments fall." | | 1 | "She looked at her wrist, at the crescent scar, then at the photos on the wall—spies, traitors, heroes, people who lived in the shadows because the light was too blinding." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 576 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.03993055555555555 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.013888888888888888 | |
| 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 | 12.17 | | std | 7.88 | | cv | 0.648 | | sampleLengths | | 0 | 23 | | 1 | 20 | | 2 | 30 | | 3 | 13 | | 4 | 28 | | 5 | 17 | | 6 | 4 | | 7 | 26 | | 8 | 19 | | 9 | 31 | | 10 | 15 | | 11 | 11 | | 12 | 7 | | 13 | 14 | | 14 | 25 | | 15 | 17 | | 16 | 26 | | 17 | 15 | | 18 | 2 | | 19 | 8 | | 20 | 4 | | 21 | 21 | | 22 | 1 | | 23 | 5 | | 24 | 16 | | 25 | 3 | | 26 | 7 | | 27 | 3 | | 28 | 4 | | 29 | 8 | | 30 | 13 | | 31 | 9 | | 32 | 10 | | 33 | 15 | | 34 | 11 | | 35 | 22 | | 36 | 6 | | 37 | 3 | | 38 | 17 | | 39 | 28 | | 40 | 8 | | 41 | 7 | | 42 | 8 | | 43 | 22 | | 44 | 4 | | 45 | 6 | | 46 | 32 | | 47 | 11 | | 48 | 4 | | 49 | 5 |
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| 41.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.29133858267716534 | | totalSentences | 127 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 90.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 93 | | matches | | 0 | "It fell in sheets, drumming" | | 1 | "She stripped off her sodden" | | 2 | "He didn’t look up." | | 3 | "She turned to scan the" | | 4 | "Her gaze swept past the" | | 5 | "She nursed a glass of" | | 6 | "Her feet seemed nailed to" | | 7 | "She looked exactly the same" | | 8 | "She looked like the future" | | 9 | "She gestured to the silk" | | 10 | "She hooked a thumb toward" | | 11 | "It wasn't pity: it was" | | 12 | "She kept her hands under" | | 13 | "He placed a tumbler of" | | 14 | "She sensed the wall closing" | | 15 | "Her hand, manicured and soft," | | 16 | "She pulled Rory’s hand out" | | 17 | "She opened her purse, a" | | 18 | "She slid it across the" | | 19 | "She looked around the bar" |
| | ratio | 0.323 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 93 | | matches | | 0 | "Soho rain tasted of grit" | | 1 | "It fell in sheets, drumming" | | 2 | "Aurora Carter shoved her shoulder" | | 3 | "She stripped off her sodden" | | 4 | "Water dripped from the hem" | | 5 | "He didn’t look up." | | 6 | "The faint hitch in his" | | 7 | "Silas asked, his voice a" | | 8 | "Rory tossed the helmet onto" | | 9 | "Rory snorted, wiping rain from" | | 10 | "She turned to scan the" | | 11 | "The Raven’s Nest was mostly" | | 12 | "Her gaze swept past the" | | 13 | "The woman sat with a" | | 14 | "She nursed a glass of" | | 15 | "The air in her lungs" | | 16 | "The woman looked up." | | 17 | "Recognition hit like a physical" | | 18 | "The name was a ghost." | | 19 | "A dead thing Rory had" |
| | ratio | 0.989 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 51.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 5 | | matches | | 0 | "It fell in sheets, drumming against the neon green sign that buzzed with an irritable electric hum above the door." | | 1 | "Water dripped from the hem of her oversized jacket, pooling on the floorboards that Silas insisted on waxing despite the parade of wet boots that assaulted them…" | | 2 | "The Raven’s Nest was mostly empty, a sanctuary for the lost and the lurking, walls lined with monochrome photos of places that didn't exist anymore." | | 3 | "Recognition hit like a physical blow, widening eyes that had once mirrored Rory’s own back in lecture halls and coffee shops." | | 4 | "She took a sip, the warmth spreading through her limbs, watching the green neon sign flicker against the dark." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 6 | | matches | | 0 | "Tessa remained, a barrier of professional success between them" | | 1 | "Tessa took, her knuckles white against the stem" | | 2 | "Rory said, her voice dropping" | | 3 | "Silas asked, his tone devoid of hospitality" | | 4 | "he asked, not looking at her" | | 5 | "she said, her voice steady" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 51 | | tagDensity | 0.157 | | leniency | 0.314 | | rawRatio | 0.125 | | effectiveRatio | 0.039 | |