| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 149 | | tagDensity | 0.154 | | leniency | 0.309 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2836 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "really" | | 1 | "perfectly" | | 2 | "suddenly" | | 3 | "very" |
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| 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) | |
| 85.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2836 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "scanned" | | 2 | "efficient" | | 3 | "pulse" | | 4 | "traced" | | 5 | "glint" | | 6 | "flicked" |
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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 | 183 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 183 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 309 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 79 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2834 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 34 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 122 | | wordCount | 1725 | | uniqueNames | 7 | | maxNameDensity | 3.07 | | worstName | "Eva" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 50 | | Eva | 53 | | Silas | 13 | | Cardiff | 1 | | Rain | 3 |
| | persons | | 0 | "Raven" | | 1 | "Aurora" | | 2 | "Eva" | | 3 | "Silas" | | 4 | "Rain" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 117 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.353 | | wordCount | 2834 | | matches | | 0 | "not in the face but in the way she held herself upright, as if the bones did the" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 309 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 224 | | mean | 12.65 | | std | 14.08 | | cv | 1.113 | | sampleLengths | | 0 | 76 | | 1 | 60 | | 2 | 58 | | 3 | 3 | | 4 | 9 | | 5 | 3 | | 6 | 6 | | 7 | 24 | | 8 | 25 | | 9 | 80 | | 10 | 6 | | 11 | 33 | | 12 | 11 | | 13 | 3 | | 14 | 9 | | 15 | 3 | | 16 | 19 | | 17 | 1 | | 18 | 27 | | 19 | 1 | | 20 | 17 | | 21 | 8 | | 22 | 19 | | 23 | 30 | | 24 | 11 | | 25 | 15 | | 26 | 10 | | 27 | 25 | | 28 | 8 | | 29 | 7 | | 30 | 59 | | 31 | 4 | | 32 | 3 | | 33 | 18 | | 34 | 7 | | 35 | 2 | | 36 | 18 | | 37 | 14 | | 38 | 2 | | 39 | 6 | | 40 | 7 | | 41 | 11 | | 42 | 12 | | 43 | 2 | | 44 | 7 | | 45 | 31 | | 46 | 38 | | 47 | 18 | | 48 | 8 | | 49 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 183 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 289 | | matches | | 0 | "weren’t looking" | | 1 | "was already doing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 309 | | ratio | 0.006 | | matches | | 0 | "The scrape of stools, the mutter near the darts board, the clink of bottle against bottle—all of it dropped behind the pulse in Aurora’s ears." | | 1 | "Silas set two steaming plates between them—chips, bread, a wedge of pie split in half by the kitchen with no ceremony at all." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1729 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 56 | | adverbRatio | 0.032388663967611336 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.003470213996529786 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 309 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 309 | | mean | 9.17 | | std | 8.21 | | cv | 0.895 | | sampleLengths | | 0 | 20 | | 1 | 21 | | 2 | 35 | | 3 | 23 | | 4 | 14 | | 5 | 23 | | 6 | 21 | | 7 | 20 | | 8 | 17 | | 9 | 3 | | 10 | 9 | | 11 | 3 | | 12 | 6 | | 13 | 12 | | 14 | 12 | | 15 | 3 | | 16 | 9 | | 17 | 13 | | 18 | 18 | | 19 | 10 | | 20 | 30 | | 21 | 7 | | 22 | 15 | | 23 | 6 | | 24 | 8 | | 25 | 25 | | 26 | 4 | | 27 | 7 | | 28 | 3 | | 29 | 9 | | 30 | 3 | | 31 | 5 | | 32 | 7 | | 33 | 7 | | 34 | 1 | | 35 | 12 | | 36 | 15 | | 37 | 1 | | 38 | 17 | | 39 | 8 | | 40 | 19 | | 41 | 30 | | 42 | 6 | | 43 | 5 | | 44 | 15 | | 45 | 10 | | 46 | 17 | | 47 | 8 | | 48 | 8 | | 49 | 4 |
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| 44.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.1779935275080906 | | totalSentences | 309 | | uniqueOpeners | 55 | |
| 83.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 159 | | matches | | 0 | "Then her gaze landed on" | | 1 | "Then something gave way around" | | 2 | "Just the skin around the" | | 3 | "Just the hard glint of" |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 159 | | matches | | 0 | "Her courier bag lay on" | | 1 | "He moved with that slight" | | 2 | "He set a pint down," | | 3 | "She wore a camel coat" | | 4 | "Her hair, once a tangle" | | 5 | "She scanned the room in" | | 6 | "His hand went still on" | | 7 | "Her face did not shift" | | 8 | "It came out of the" | | 9 | "He moved off with his" | | 10 | "He took it, delivered it," | | 11 | "It drifted through the room" | | 12 | "he told the bar in" | | 13 | "She winced, and Aurora almost" | | 14 | "She swore under her breath," | | 15 | "It reached under the armour" | | 16 | "She reached for the whisky" | | 17 | "It vanished before it settled." | | 18 | "He did not look over," | | 19 | "she said at last" |
| | ratio | 0.151 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 150 | | totalSentences | 159 | | matches | | 0 | "The green neon over the" | | 1 | "Aurora stood at the far" | | 2 | "Her courier bag lay on" | | 3 | "A curl of steam still" | | 4 | "Silas worked the taps with" | | 5 | "He moved with that slight" | | 6 | "He set a pint down," | | 7 | "Silas snorted into his beard" | | 8 | "The silver signet ring on" | | 9 | "The door opened." | | 10 | "A wash of cold air" | | 11 | "Aurora reached for the next" | | 12 | "The woman in the doorway" | | 13 | "She wore a camel coat" | | 14 | "Her hair, once a tangle" | | 15 | "A thin white scar nicked" | | 16 | "She scanned the room in" | | 17 | "The scrape of stools, the" | | 18 | "Silas followed her stare." | | 19 | "His hand went still on" |
| | ratio | 0.943 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 159 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 3 | | matches | | 0 | "Aurora lifted a finger without looking away, reached for the tap, filled the pint in one clean pull, and sent it down the bar to where Silas had reappeared as i…" | | 1 | "At nineteen she had wept after failing a module with mascara down to her chin, laughing through it." | | 2 | "Rain tapped the window with less force now, as if it had spent itself." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 20 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 149 | | tagDensity | 0.134 | | leniency | 0.268 | | rawRatio | 0.05 | | effectiveRatio | 0.013 | |