| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1957 | | 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) | |
| 71.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1957 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "weight" | | 1 | "flickered" | | 2 | "tracing" | | 3 | "rhythmic" | | 4 | "tension" | | 5 | "echoed" | | 6 | "solitary" | | 7 | "vibrated" | | 8 | "silence" |
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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 | 292 | | matches | (empty) | |
| 89.04% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 11 | | hedgeCount | 0 | | narrationSentences | 292 | | filterMatches | | 0 | "look" | | 1 | "know" | | 2 | "think" | | 3 | "realize" |
| | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 292 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1952 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 103 | | wordCount | 1952 | | uniqueNames | 16 | | maxNameDensity | 1.69 | | worstName | "You" | | maxWindowNameDensity | 7 | | worstWindowName | "You" | | discoveredNames | | Soho | 1 | | Raven | 3 | | Nest | 3 | | Blackwood | 1 | | Aurora | 20 | | Cardiff | 3 | | Carter | 3 | | London | 5 | | Prague | 1 | | Cheung | 1 | | Golden | 2 | | Empress | 2 | | Silas | 20 | | Evan | 1 | | You | 33 | | Eva | 4 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Blackwood" | | 3 | "Aurora" | | 4 | "Carter" | | 5 | "Cheung" | | 6 | "Silas" | | 7 | "Evan" | | 8 | "You" | | 9 | "Eva" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" | | 3 | "Prague" | | 4 | "Golden" |
| | globalScore | 0.655 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 164 | | glossingSentenceCount | 3 | | matches | | 0 | "smelled like sandalwood and smoke" | | 1 | "seemed deeper than the rest of the room" | | 2 | "looked like ash in the dim light" |
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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 | 1952 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 292 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 115 | | mean | 16.97 | | std | 14.84 | | cv | 0.874 | | sampleLengths | | 0 | 79 | | 1 | 48 | | 2 | 46 | | 3 | 5 | | 4 | 25 | | 5 | 7 | | 6 | 12 | | 7 | 42 | | 8 | 6 | | 9 | 4 | | 10 | 5 | | 11 | 24 | | 12 | 13 | | 13 | 31 | | 14 | 3 | | 15 | 2 | | 16 | 3 | | 17 | 62 | | 18 | 23 | | 19 | 28 | | 20 | 2 | | 21 | 16 | | 22 | 27 | | 23 | 13 | | 24 | 10 | | 25 | 7 | | 26 | 9 | | 27 | 21 | | 28 | 15 | | 29 | 20 | | 30 | 4 | | 31 | 15 | | 32 | 2 | | 33 | 23 | | 34 | 14 | | 35 | 3 | | 36 | 19 | | 37 | 29 | | 38 | 17 | | 39 | 5 | | 40 | 7 | | 41 | 5 | | 42 | 25 | | 43 | 21 | | 44 | 11 | | 45 | 36 | | 46 | 3 | | 47 | 6 | | 48 | 3 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 292 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 375 | | matches | | 0 | "were arriving" | | 1 | "was opening" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 292 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1957 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 37 | | adverbRatio | 0.01890648952478283 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.004087889626980072 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 292 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 292 | | mean | 6.68 | | std | 3.96 | | cv | 0.592 | | sampleLengths | | 0 | 23 | | 1 | 21 | | 2 | 12 | | 3 | 23 | | 4 | 13 | | 5 | 19 | | 6 | 5 | | 7 | 7 | | 8 | 4 | | 9 | 9 | | 10 | 15 | | 11 | 10 | | 12 | 12 | | 13 | 5 | | 14 | 7 | | 15 | 11 | | 16 | 7 | | 17 | 7 | | 18 | 4 | | 19 | 4 | | 20 | 4 | | 21 | 14 | | 22 | 6 | | 23 | 8 | | 24 | 6 | | 25 | 8 | | 26 | 6 | | 27 | 4 | | 28 | 5 | | 29 | 10 | | 30 | 6 | | 31 | 8 | | 32 | 7 | | 33 | 6 | | 34 | 13 | | 35 | 18 | | 36 | 3 | | 37 | 2 | | 38 | 3 | | 39 | 8 | | 40 | 20 | | 41 | 7 | | 42 | 11 | | 43 | 16 | | 44 | 7 | | 45 | 7 | | 46 | 9 | | 47 | 2 | | 48 | 7 | | 49 | 19 |
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| 36.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 40 | | diversityRatio | 0.2191780821917808 | | totalSentences | 292 | | uniqueOpeners | 64 | |
| 12.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 264 | | matches | | | ratio | 0.004 | |
| 27.58% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 127 | | totalSentences | 264 | | matches | | 0 | "She moved toward the bar," | | 1 | "He paused when she approached." | | 2 | "He leaned forward, resting his" | | 3 | "You look like your father." | | 4 | "Her fingers brushed the small" | | 5 | "You have the stare." | | 6 | "He slid it across the" | | 7 | "She watched the condensation form" | | 8 | "She said you could help." | | 9 | "He set it down with" | | 10 | "She turned her wrist over," | | 11 | "I left Cardiff." | | 12 | "I left everything." | | 13 | "He moved with a slight" | | 14 | "He stopped a foot away" | | 15 | "He looked older than the" | | 16 | "You walked away from a" | | 17 | "You walked away from a" | | 18 | "You walked away from a" | | 19 | "You pay it every day" |
| | ratio | 0.481 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 240 | | totalSentences | 264 | | matches | | 0 | "The green neon sign above" | | 1 | "Aurora pushed through the heavy" | | 2 | "The air inside smelled of" | | 3 | "She moved toward the bar," | | 4 | "He paused when she approached." | | 5 | "Hazel eyes locked onto bright" | | 6 | "The rag stopped moving." | | 7 | "Silas Blackwood set the glass" | | 8 | "The movement caused the light" | | 9 | "He leaned forward, resting his" | | 10 | "The left one stayed straight," | | 11 | "You look like your father." | | 12 | "Aurora gripped the edge of" | | 13 | "Her fingers brushed the small" | | 14 | "The wood felt rough under" | | 15 | "People say I look like" | | 16 | "Jennifer had the smile." | | 17 | "Brendan had the stare." | | 18 | "You have the stare." | | 19 | "Silas reached for a bottle" |
| | ratio | 0.909 | |
| 75.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 264 | | matches | | 0 | "If you stay here, you" | | 1 | "If you break a rule," | | 2 | "If you bring trouble to" | | 3 | "If he comes here?" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 1 | | matches | | 0 | "A clock ticked somewhere in the back, near the bookshelf that looked slightly out of place against the brickwork." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |