| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 61 | | tagDensity | 0.033 | | leniency | 0.066 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1434 | | 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) | |
| 79.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1434 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "flickered" | | 3 | "tracing" | | 4 | "echoed" | | 5 | "predator" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 102 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 162 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1434 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 997 | | uniqueNames | 7 | | maxNameDensity | 1 | | worstName | "Evan" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Evan" | | discoveredNames | | Cardiff | 2 | | Si | 1 | | Prague | 2 | | London | 1 | | Evan | 10 | | Rory | 7 | | Silas | 9 |
| | persons | | | places | | 0 | "Cardiff" | | 1 | "Prague" | | 2 | "London" |
| | globalScore | 0.998 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | 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 | 1434 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 162 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 105 | | mean | 13.66 | | std | 15.39 | | cv | 1.127 | | sampleLengths | | 0 | 8 | | 1 | 5 | | 2 | 22 | | 3 | 4 | | 4 | 13 | | 5 | 11 | | 6 | 9 | | 7 | 63 | | 8 | 20 | | 9 | 21 | | 10 | 2 | | 11 | 35 | | 12 | 5 | | 13 | 1 | | 14 | 23 | | 15 | 5 | | 16 | 5 | | 17 | 105 | | 18 | 5 | | 19 | 14 | | 20 | 5 | | 21 | 17 | | 22 | 43 | | 23 | 5 | | 24 | 4 | | 25 | 9 | | 26 | 3 | | 27 | 1 | | 28 | 31 | | 29 | 3 | | 30 | 3 | | 31 | 2 | | 32 | 15 | | 33 | 13 | | 34 | 7 | | 35 | 1 | | 36 | 5 | | 37 | 22 | | 38 | 37 | | 39 | 5 | | 40 | 9 | | 41 | 28 | | 42 | 2 | | 43 | 3 | | 44 | 1 | | 45 | 1 | | 46 | 8 | | 47 | 28 | | 48 | 36 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 170 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 162 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 689 | | adjectiveStacks | 2 | | stackExamples | | 0 | "small crescent-shaped scar" | | 1 | "small crescent-shaped scar," |
| | adverbCount | 18 | | adverbRatio | 0.026124818577648767 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0043541364296081275 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 162 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 162 | | mean | 8.85 | | std | 7.69 | | cv | 0.868 | | sampleLengths | | 0 | 8 | | 1 | 5 | | 2 | 7 | | 3 | 15 | | 4 | 4 | | 5 | 5 | | 6 | 8 | | 7 | 11 | | 8 | 2 | | 9 | 4 | | 10 | 3 | | 11 | 4 | | 12 | 20 | | 13 | 20 | | 14 | 13 | | 15 | 6 | | 16 | 17 | | 17 | 3 | | 18 | 21 | | 19 | 2 | | 20 | 6 | | 21 | 29 | | 22 | 5 | | 23 | 1 | | 24 | 8 | | 25 | 5 | | 26 | 10 | | 27 | 5 | | 28 | 3 | | 29 | 2 | | 30 | 4 | | 31 | 20 | | 32 | 18 | | 33 | 15 | | 34 | 17 | | 35 | 4 | | 36 | 27 | | 37 | 5 | | 38 | 14 | | 39 | 5 | | 40 | 17 | | 41 | 4 | | 42 | 16 | | 43 | 23 | | 44 | 5 | | 45 | 4 | | 46 | 9 | | 47 | 3 | | 48 | 1 | | 49 | 10 |
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| 49.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3271604938271605 | | totalSentences | 162 | | uniqueOpeners | 53 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 90 | | matches | | 0 | "Slowly, he turned." | | 1 | "Bright blue eyes, straight shoulder-length" | | 2 | "Then he laughed, low and" | | 3 | "Somewhere behind her, the bookshelf" | | 4 | "Then the door opened." | | 5 | "Then Evan reached into his" |
| | ratio | 0.067 | |
| 46.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 90 | | matches | | 0 | "He set down a glass," | | 1 | "She checked the scuffed receipt." | | 2 | "They were older, bracketed by" | | 3 | "He leaned his weight on" | | 4 | "He wore it like a" | | 5 | "His voice had the rough" | | 6 | "She kept her face blank," | | 7 | "He came around the bar," | | 8 | "She flexed her left hand." | | 9 | "She hadn't expected him." | | 10 | "She hadn't expected the way" | | 11 | "She nodded at the bag." | | 12 | "His gaze dropped to her" | | 13 | "It wasn't a question." | | 14 | "She shrugged, the leather of" | | 15 | "She didn't flinch." | | 16 | "She never flinched." | | 17 | "She let the lie sit" | | 18 | "She hadn't called him Si" | | 19 | "He tapped his left knee." |
| | ratio | 0.433 | |
| 21.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 90 | | matches | | 0 | "Rory shoved the warm bag" | | 1 | "The man behind the bar" | | 2 | "He set down a glass," | | 3 | "She checked the scuffed receipt." | | 4 | "The cheap ink had smudged" | | 5 | "The rag stopped moving." | | 6 | "Hazel eyes met hers." | | 7 | "They were older, bracketed by" | | 8 | "He leaned his weight on" | | 9 | "A silver signet ring glinted" | | 10 | "He wore it like a" | | 11 | "His voice had the rough" | | 12 | "She kept her face blank," | | 13 | "He came around the bar," | | 14 | "The limp slowed him, but" | | 15 | "The word came out sharper" | | 16 | "She flexed her left hand." | | 17 | "The small crescent-shaped scar on" | | 18 | "That hung between them." | | 19 | "The tick-tock of a wall" |
| | ratio | 0.878 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 3 | | matches | | 0 | "On the walls, old maps and black-and-white photographs watched like ghosts of men who hadn't made it home." | | 1 | "Bright blue eyes, straight shoulder-length black hair, the stubborn set of a jaw that had learned to take a hit and keep talking." | | 2 | "His coat was open, his knuckles split and raw, his gaze fixed on her with the flat hunger of a man who had finally cornered what he was hunting." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 61 | | tagDensity | 0.016 | | leniency | 0.033 | | rawRatio | 0 | | effectiveRatio | 0 | |