| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 219 | | tagDensity | 0.064 | | leniency | 0.128 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 98.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2906 | | totalAiIsmAdverbs | 1 | | 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) | |
| 79.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2906 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "gloom" | | 2 | "charged" | | 3 | "flickered" | | 4 | "silence" | | 5 | "traced" | | 6 | "perfect" | | 7 | "familiar" | | 8 | "tenderness" | | 9 | "quivered" |
| |
| 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 | 1 | | narrationSentences | 222 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 222 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 426 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2906 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 100 | | wordCount | 1838 | | uniqueNames | 13 | | maxNameDensity | 1.74 | | worstName | "Evan" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Evan" | | discoveredNames | | Soho | 1 | | Tsingtao | 1 | | Evan | 32 | | London | 1 | | Raven | 1 | | Nest | 2 | | Europe | 1 | | Rory | 25 | | Silas | 30 | | Piccadilly | 1 | | Blitz | 1 | | Cardiff | 3 | | Relief | 1 |
| | persons | | 0 | "Tsingtao" | | 1 | "Evan" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Rory" | | 5 | "Silas" | | 6 | "Relief" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Europe" | | 3 | "Cardiff" |
| | globalScore | 0.629 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 144 | | glossingSentenceCount | 1 | | matches | | 0 | "as if measuring what had replaced him" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2906 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 426 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 308 | | mean | 9.44 | | std | 10.42 | | cv | 1.104 | | sampleLengths | | 0 | 27 | | 1 | 22 | | 2 | 59 | | 3 | 3 | | 4 | 10 | | 5 | 32 | | 6 | 1 | | 7 | 7 | | 8 | 12 | | 9 | 2 | | 10 | 8 | | 11 | 62 | | 12 | 24 | | 13 | 3 | | 14 | 15 | | 15 | 9 | | 16 | 5 | | 17 | 5 | | 18 | 23 | | 19 | 6 | | 20 | 9 | | 21 | 19 | | 22 | 5 | | 23 | 4 | | 24 | 3 | | 25 | 23 | | 26 | 4 | | 27 | 3 | | 28 | 9 | | 29 | 4 | | 30 | 9 | | 31 | 10 | | 32 | 4 | | 33 | 22 | | 34 | 1 | | 35 | 7 | | 36 | 1 | | 37 | 4 | | 38 | 60 | | 39 | 8 | | 40 | 15 | | 41 | 7 | | 42 | 4 | | 43 | 8 | | 44 | 31 | | 45 | 4 | | 46 | 3 | | 47 | 13 | | 48 | 2 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 222 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 320 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 426 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1841 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.022813688212927757 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0010863661053775121 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 426 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 426 | | mean | 6.82 | | std | 5.06 | | cv | 0.742 | | sampleLengths | | 0 | 8 | | 1 | 19 | | 2 | 22 | | 3 | 17 | | 4 | 16 | | 5 | 6 | | 6 | 6 | | 7 | 14 | | 8 | 3 | | 9 | 6 | | 10 | 4 | | 11 | 13 | | 12 | 19 | | 13 | 1 | | 14 | 7 | | 15 | 12 | | 16 | 2 | | 17 | 4 | | 18 | 4 | | 19 | 20 | | 20 | 6 | | 21 | 13 | | 22 | 10 | | 23 | 13 | | 24 | 6 | | 25 | 18 | | 26 | 3 | | 27 | 8 | | 28 | 7 | | 29 | 9 | | 30 | 5 | | 31 | 5 | | 32 | 10 | | 33 | 13 | | 34 | 6 | | 35 | 7 | | 36 | 2 | | 37 | 5 | | 38 | 14 | | 39 | 5 | | 40 | 4 | | 41 | 3 | | 42 | 4 | | 43 | 6 | | 44 | 13 | | 45 | 4 | | 46 | 3 | | 47 | 9 | | 48 | 4 | | 49 | 6 |
| |
| 46.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.23708920187793428 | | totalSentences | 426 | | uniqueOpeners | 101 | |
| 49.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 201 | | matches | | 0 | "Then Evan smiled." | | 1 | "Then he looked at her," | | 2 | "Then darkness returned and restored" |
| | ratio | 0.015 | |
| 74.73% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 73 | | totalSentences | 201 | | matches | | 0 | "He wore a camel coat" | | 1 | "His hair, once thick enough" | | 2 | "He crossed between a taxi" | | 3 | "She carried the crate through" | | 4 | "His hazel gaze passed from" | | 5 | "She pulled the folded paper" | | 6 | "His eyes settled on the" | | 7 | "It showed three minutes to" | | 8 | "He turned the lock on" | | 9 | "Her fingers missed the tab" | | 10 | "He removed his coat with" | | 11 | "His ring brushed the crescent" | | 12 | "She met his gaze." | | 13 | "His thumb moved around the" | | 14 | "She moved towards the door." | | 15 | "He rose before she reached" | | 16 | "His body still found the" | | 17 | "she told him" | | 18 | "He returned to his stool." | | 19 | "He turned the lock in" |
| | ratio | 0.363 | |
| 9.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 181 | | totalSentences | 201 | | matches | | 0 | "Rain had polished Soho into" | | 1 | "The green neon raven above" | | 2 | "Aurora stood outside with a" | | 3 | "He wore a camel coat" | | 4 | "His hair, once thick enough" | | 5 | "The years had sharpened his" | | 6 | "A pale line crossed one" | | 7 | "The crate shifted against her" | | 8 | "Glass knocked against glass." | | 9 | "He crossed between a taxi" | | 10 | "She carried the crate through" | | 11 | "Evan followed her inside." | | 12 | "Rory set the beer beneath" | | 13 | "The Nest had not opened" | | 14 | "Chairs stood upside down on" | | 15 | "Rain crawled down the front" | | 16 | "Silas looked up from the" | | 17 | "His hazel gaze passed from" | | 18 | "She pulled the folded paper" | | 19 | "Evan remained by the door," |
| | ratio | 0.9 | |
| 24.88% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 201 | | matches | | 0 | "By the time he reached" |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 219 | | tagDensity | 0.046 | | leniency | 0.091 | | rawRatio | 0.1 | | effectiveRatio | 0.009 | |