| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 491 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 38.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 491 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "scanned" | | 1 | "tracing" | | 2 | "vibrated" | | 3 | "jaw clenched" | | 4 | "weight" | | 5 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 51 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 51 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 51 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 491 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.90% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 491 | | uniqueNames | 17 | | maxNameDensity | 1.22 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Brick | 1 | | Lane | 1 | | Tuesday | 1 | | Rory | 2 | | Lucien | 6 | | Marseille | 1 | | Southwark | 1 | | Thames | 1 | | Evan | 1 | | Cardiff | 1 | | Frenchman | 1 | | Laila | 1 | | Channel | 1 | | Ptolemy | 2 | | Sumerian | 1 | | Aurora | 5 | | You | 5 |
| | persons | | 0 | "Rory" | | 1 | "Lucien" | | 2 | "Evan" | | 3 | "Frenchman" | | 4 | "Laila" | | 5 | "Ptolemy" | | 6 | "Aurora" | | 7 | "You" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "Southwark" | | 4 | "Thames" | | 5 | "Cardiff" | | 6 | "Channel" |
| | globalScore | 0.889 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | 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 | 491 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 51 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 14.88 | | std | 11.2 | | cv | 0.753 | | sampleLengths | | 0 | 47 | | 1 | 12 | | 2 | 8 | | 3 | 15 | | 4 | 15 | | 5 | 27 | | 6 | 4 | | 7 | 17 | | 8 | 7 | | 9 | 39 | | 10 | 17 | | 11 | 8 | | 12 | 14 | | 13 | 15 | | 14 | 6 | | 15 | 9 | | 16 | 18 | | 17 | 7 | | 18 | 6 | | 19 | 26 | | 20 | 8 | | 21 | 8 | | 22 | 12 | | 23 | 15 | | 24 | 16 | | 25 | 14 | | 26 | 9 | | 27 | 49 | | 28 | 7 | | 29 | 16 | | 30 | 2 | | 31 | 16 | | 32 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 51 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 84 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 494 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.024291497975708502 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.010121457489878543 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 51 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 51 | | mean | 9.63 | | std | 5.37 | | cv | 0.558 | | sampleLengths | | 0 | 13 | | 1 | 13 | | 2 | 10 | | 3 | 11 | | 4 | 12 | | 5 | 8 | | 6 | 15 | | 7 | 6 | | 8 | 9 | | 9 | 15 | | 10 | 12 | | 11 | 4 | | 12 | 17 | | 13 | 2 | | 14 | 5 | | 15 | 17 | | 16 | 11 | | 17 | 11 | | 18 | 11 | | 19 | 6 | | 20 | 8 | | 21 | 4 | | 22 | 10 | | 23 | 15 | | 24 | 6 | | 25 | 9 | | 26 | 7 | | 27 | 11 | | 28 | 7 | | 29 | 6 | | 30 | 26 | | 31 | 8 | | 32 | 8 | | 33 | 6 | | 34 | 2 | | 35 | 4 | | 36 | 15 | | 37 | 7 | | 38 | 9 | | 39 | 14 | | 40 | 3 | | 41 | 6 | | 42 | 22 | | 43 | 22 | | 44 | 5 | | 45 | 7 | | 46 | 16 | | 47 | 2 | | 48 | 4 | | 49 | 12 |
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| 71.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.45098039215686275 | | totalSentences | 51 | | uniqueOpeners | 23 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 24.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 47 | | matches | | 0 | "You missed our Tuesday meeting" | | 1 | "He scanned the stacks of" | | 2 | "You look exhausted, Rory." | | 3 | "She dropped her gaze to" | | 4 | "He closed the distance between" | | 5 | "They assumed you kept the" | | 6 | "They assume too much." | | 7 | "I dumped the damn book" | | 8 | "His heterochromatic gaze locked onto" | | 9 | "She stepped closer, chest nearly" | | 10 | "I survived Evan, and I" | | 11 | "I handle my own mistakes" | | 12 | "You always did bite when" | | 13 | "His hand lifted, long fingers" | | 14 | "We left things unresolved beside" | | 15 | "You walked away toward the" | | 16 | "I secured passage for both" | | 17 | "You chose the curry house" | | 18 | "She tilted her chin up," | | 19 | "I chose independence." |
| | ratio | 0.489 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 47 | | matches | | 0 | "Aurora pulled the timber slab" | | 1 | "Platinum hair caught the dim" | | 2 | "Lucien filled the doorframe, charcoal" | | 3 | "Ptolemy wound between those polished" | | 4 | "You missed our Tuesday meeting" | | 5 | "Aurora leaned her shoulder against" | | 6 | "Work ran late at the" | | 7 | "Yu-Fei needed deliveries cleared across" | | 8 | "Lucien stepped past uninvited, the" | | 9 | "He scanned the stacks of" | | 10 | "You look exhausted, Rory." | | 11 | "She dropped her gaze to" | | 12 | "A scarred wrist brushed the" | | 13 | "He closed the distance between" | | 14 | "The scent of rain and" | | 15 | "The council in Marseille sent" | | 16 | "They assumed you kept the" | | 17 | "Aurora spun around, her bright" | | 18 | "They assume too much." | | 19 | "I dumped the damn book" |
| | ratio | 0.957 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "Lucien closed the remaining space between them, his hand cupping the side of her neck, thumb brushing the edge of her jawline." |
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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 | |