| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2219 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "completely" | | 1 | "precisely" | | 2 | "very" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 68.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2219 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "weight" | | 1 | "navigated" | | 2 | "sanctuary" | | 3 | "velvet" | | 4 | "ensure" | | 5 | "silence" | | 6 | "intensity" | | 7 | "unspoken" | | 8 | "warmth" | | 9 | "traced" | | 10 | "standard" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 127 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 127 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 257 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2219 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 950 | | uniqueNames | 6 | | maxNameDensity | 2.63 | | worstName | "Lucien" | | maxWindowNameDensity | 5 | | worstWindowName | "Rory" | | discoveredNames | | Brick | 1 | | Lane | 1 | | Blackwood | 1 | | Lucien | 25 | | Rory | 24 | | Ptolemy | 4 |
| | persons | | 0 | "Lucien" | | 1 | "Rory" | | 2 | "Ptolemy" |
| | places | | | globalScore | 0.184 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | 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 | 2219 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 257 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 185 | | mean | 11.99 | | std | 8.6 | | cv | 0.717 | | sampleLengths | | 0 | 23 | | 1 | 28 | | 2 | 15 | | 3 | 3 | | 4 | 12 | | 5 | 7 | | 6 | 1 | | 7 | 9 | | 8 | 2 | | 9 | 21 | | 10 | 21 | | 11 | 7 | | 12 | 34 | | 13 | 1 | | 14 | 10 | | 15 | 4 | | 16 | 19 | | 17 | 4 | | 18 | 17 | | 19 | 15 | | 20 | 16 | | 21 | 7 | | 22 | 4 | | 23 | 30 | | 24 | 18 | | 25 | 20 | | 26 | 9 | | 27 | 20 | | 28 | 14 | | 29 | 21 | | 30 | 7 | | 31 | 16 | | 32 | 6 | | 33 | 3 | | 34 | 15 | | 35 | 9 | | 36 | 31 | | 37 | 11 | | 38 | 9 | | 39 | 26 | | 40 | 7 | | 41 | 32 | | 42 | 12 | | 43 | 7 | | 44 | 25 | | 45 | 7 | | 46 | 12 | | 47 | 21 | | 48 | 8 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 174 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 257 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 951 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.016824395373291272 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0010515247108307045 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 257 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 257 | | mean | 8.63 | | std | 6.36 | | cv | 0.736 | | sampleLengths | | 0 | 8 | | 1 | 8 | | 2 | 7 | | 3 | 5 | | 4 | 12 | | 5 | 11 | | 6 | 15 | | 7 | 3 | | 8 | 8 | | 9 | 4 | | 10 | 7 | | 11 | 1 | | 12 | 9 | | 13 | 2 | | 14 | 8 | | 15 | 13 | | 16 | 21 | | 17 | 7 | | 18 | 4 | | 19 | 9 | | 20 | 8 | | 21 | 13 | | 22 | 1 | | 23 | 10 | | 24 | 4 | | 25 | 6 | | 26 | 13 | | 27 | 4 | | 28 | 5 | | 29 | 12 | | 30 | 15 | | 31 | 16 | | 32 | 7 | | 33 | 4 | | 34 | 7 | | 35 | 23 | | 36 | 18 | | 37 | 20 | | 38 | 9 | | 39 | 20 | | 40 | 3 | | 41 | 11 | | 42 | 21 | | 43 | 7 | | 44 | 16 | | 45 | 6 | | 46 | 3 | | 47 | 15 | | 48 | 9 | | 49 | 31 |
| |
| 42.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.22957198443579765 | | totalSentences | 257 | | uniqueOpeners | 59 | |
| 54.20% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 123 | | matches | | 0 | "Then the second." | | 1 | "Then the third." |
| | ratio | 0.016 | |
| 37.89% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 123 | | matches | | 0 | "She pulled the heavy wooden" | | 1 | "His tailored charcoal suit absorbed" | | 2 | "He leaned his weight on" | | 3 | "Her knuckles turned white." | | 4 | "He kept his gaze fixed" | | 5 | "His amber eye caught the" | | 6 | "He crossed the threshold." | | 7 | "He sidestepped a tower of" | | 8 | "She crossed her arms, hiding" | | 9 | "He withdrew a thick manila" | | 10 | "He tapped the ivory handle" | | 11 | "His long fingers moved with" | | 12 | "Her hands dropped to her" | | 13 | "He stopped a foot away." | | 14 | "He stopped an inch from" | | 15 | "His fingers hovered in the" | | 16 | "She looked at his hand," | | 17 | "He looked down at his" | | 18 | "He placed it by the" | | 19 | "He repeated the process with" |
| | ratio | 0.455 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 121 | | totalSentences | 123 | | matches | | 0 | "The brass thumb-turn of the" | | 1 | "Rory twisted the second lock," | | 2 | "She pulled the heavy wooden" | | 3 | "Lucien filled the narrow hallway." | | 4 | "His tailored charcoal suit absorbed" | | 5 | "He leaned his weight on" | | 6 | "Rory kept her grip on" | | 7 | "Her knuckles turned white." | | 8 | "Rory let the word hang" | | 9 | "He kept his gaze fixed" | | 10 | "His amber eye caught the" | | 11 | "Rory stepped back and released" | | 12 | "He crossed the threshold." | | 13 | "The tip of his cane" | | 14 | "Ptolemy emerged from a fortress" | | 15 | "The tabby cat wound around" | | 16 | "Rory pushed the door shut" | | 17 | "Lucien navigated the cramped living" | | 18 | "He sidestepped a tower of" | | 19 | "Rory leaned against the door." |
| | ratio | 0.984 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |