| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "He laughed once [once]" |
| | dialogueSentences | 20 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.125 | | effectiveRatio | 0.1 | |
| 93.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1531 | | totalAiIsmAdverbs | 2 | | 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) | |
| 60.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1531 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "tracing" | | 1 | "weight" | | 2 | "traced" | | 3 | "shattered" | | 4 | "silence" | | 5 | "silk" | | 6 | "flickered" | | 7 | "tension" | | 8 | "familiar" |
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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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 87 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 63 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1531 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.77% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 942 | | uniqueNames | 9 | | maxNameDensity | 1.8 | | worstName | "Silas" | | maxWindowNameDensity | 3 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Blackwood | 1 | | Silas | 17 | | Thomas | 12 | | Berlin | 1 | | Marrakech | 1 | | Prague | 1 | | London | 2 |
| | persons | | 0 | "Blackwood" | | 1 | "Silas" | | 2 | "Thomas" |
| | places | | 0 | "Raven" | | 1 | "Berlin" | | 2 | "Marrakech" | | 3 | "Prague" | | 4 | "London" |
| | globalScore | 0.598 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | 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 | 1531 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 99 | | matches | | |
| 69.17% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 52.79 | | std | 20.7 | | cv | 0.392 | | sampleLengths | | 0 | 82 | | 1 | 86 | | 2 | 86 | | 3 | 1 | | 4 | 36 | | 5 | 24 | | 6 | 33 | | 7 | 44 | | 8 | 63 | | 9 | 60 | | 10 | 37 | | 11 | 69 | | 12 | 34 | | 13 | 67 | | 14 | 43 | | 15 | 87 | | 16 | 55 | | 17 | 43 | | 18 | 83 | | 19 | 59 | | 20 | 68 | | 21 | 58 | | 22 | 39 | | 23 | 25 | | 24 | 51 | | 25 | 51 | | 26 | 38 | | 27 | 47 | | 28 | 62 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 87 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 152 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 99 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 947 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.03273495248152059 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004223864836325237 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 15.46 | | std | 13.04 | | cv | 0.843 | | sampleLengths | | 0 | 23 | | 1 | 18 | | 2 | 13 | | 3 | 12 | | 4 | 16 | | 5 | 19 | | 6 | 25 | | 7 | 27 | | 8 | 15 | | 9 | 3 | | 10 | 15 | | 11 | 15 | | 12 | 18 | | 13 | 24 | | 14 | 11 | | 15 | 1 | | 16 | 13 | | 17 | 23 | | 18 | 24 | | 19 | 8 | | 20 | 8 | | 21 | 17 | | 22 | 8 | | 23 | 6 | | 24 | 15 | | 25 | 15 | | 26 | 36 | | 27 | 9 | | 28 | 18 | | 29 | 12 | | 30 | 4 | | 31 | 17 | | 32 | 19 | | 33 | 8 | | 34 | 37 | | 35 | 9 | | 36 | 8 | | 37 | 52 | | 38 | 8 | | 39 | 4 | | 40 | 8 | | 41 | 14 | | 42 | 63 | | 43 | 4 | | 44 | 3 | | 45 | 6 | | 46 | 34 | | 47 | 51 | | 48 | 6 | | 49 | 30 |
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| 49.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3434343434343434 | | totalSentences | 99 | | uniqueOpeners | 34 | |
| 41.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 81 | | matches | | 0 | "Just the quiet acknowledgment that" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 81 | | matches | | 0 | "His grey-streaked auburn hair caught" | | 1 | "He paused just inside, water" | | 2 | "His hair, once a thick" | | 3 | "He straightened, hazel gaze steady" | | 4 | "His hands, still broad, showed" | | 5 | "His face relaxed a fraction" | | 6 | "He did not smile." | | 7 | "His finger traced a knot" | | 8 | "His eyes moved over Silas's" | | 9 | "He laughed once, short and" | | 10 | "He folded it with precise" | | 11 | "They sat in the silence" | | 12 | "He nodded toward Thomas's right" | | 13 | "His voice stayed low, almost" | | 14 | "His limp made itself known" | | 15 | "Their eyes met over the" | | 16 | "They simply had to be" | | 17 | "Their conversation continued in low" |
| | ratio | 0.222 | |
| 21.73% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 81 | | matches | | 0 | "The green neon sign hummed" | | 1 | "Maps covered the walls, their" | | 2 | "A handful of patrons nursed" | | 3 | "Silas Blackwood moved behind the" | | 4 | "His grey-streaked auburn hair caught" | | 5 | "The slight limp in his" | | 6 | "The silver signet ring on" | | 7 | "The door opened." | | 8 | "He paused just inside, water" | | 9 | "His hair, once a thick" | | 10 | "The face beneath it carried" | | 11 | "Those eyes swept the room" | | 12 | "Silas felt the name land" | | 13 | "He straightened, hazel gaze steady" | | 14 | "The newcomer's voice carried the" | | 15 | "Silas poured two measures of" | | 16 | "The bottle clinked against glass" | | 17 | "Thomas crossed the floor and" | | 18 | "The sharp intelligence that once" | | 19 | "His hands, still broad, showed" |
| | ratio | 0.877 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 84.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 3 | | matches | | 0 | "His grey-streaked auburn hair caught the low light, and the neatly trimmed beard framed a jaw that had once clenched around orders in distant capitals." | | 1 | "The face beneath it carried new weight, cheeks heavier, eyes shadowed in ways that spoke of desks and dead ends rather than the field." | | 2 | "His eyes moved over Silas's face, cataloging the new lines, the beard that hid scars from a knife in Marrakech, the way the limp made him favor his right side e…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "He laughed once (laugh)" |
| | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.25 | | effectiveRatio | 0.1 | |