| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 172 | | tagDensity | 0.134 | | leniency | 0.267 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2201 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "very" | | 1 | "gently" | | 2 | "slowly" | | 3 | "quickly" |
| |
| 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) | |
| 75.01% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2201 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "absolutely" | | 1 | "firmly" | | 2 | "pulse" | | 3 | "sense of" | | 4 | "warmth" | | 5 | "familiar" | | 6 | "stomach" | | 7 | "restrained" | | 8 | "could feel" |
| |
| 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 | 159 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 4 | | narrationSentences | 159 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 307 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2196 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 38 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1399 | | uniqueNames | 7 | | maxNameDensity | 1.29 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 18 | | Moreau | 1 | | Rory | 18 | | Ptolemy | 4 | | Eva | 2 | | Aurora | 1 | | Alive | 1 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Ptolemy" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0.857 | | windowScore | 0.833 | |
| 75.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | glossingSentenceCount | 3 | | matches | | 0 | "sounded like someone who owned a castle" | | 1 | "sounded like a hand reaching for something" | | 2 | "seemed unable to answer" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.455 | | wordCount | 2196 | | matches | | 0 | "not hard, but firmly enough to stop it" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 5 | | totalSentences | 307 | | matches | | 0 | "hated that she" | | 1 | "touching that strand" | | 2 | "hated that her" | | 3 | "hated that she" | | 4 | "hated that a" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 209 | | mean | 10.51 | | std | 13.43 | | cv | 1.278 | | sampleLengths | | 0 | 6 | | 1 | 67 | | 2 | 9 | | 3 | 3 | | 4 | 5 | | 5 | 2 | | 6 | 7 | | 7 | 6 | | 8 | 38 | | 9 | 16 | | 10 | 2 | | 11 | 7 | | 12 | 6 | | 13 | 5 | | 14 | 76 | | 15 | 26 | | 16 | 10 | | 17 | 7 | | 18 | 5 | | 19 | 4 | | 20 | 5 | | 21 | 8 | | 22 | 12 | | 23 | 12 | | 24 | 33 | | 25 | 7 | | 26 | 4 | | 27 | 58 | | 28 | 6 | | 29 | 5 | | 30 | 23 | | 31 | 6 | | 32 | 6 | | 33 | 8 | | 34 | 48 | | 35 | 6 | | 36 | 4 | | 37 | 14 | | 38 | 2 | | 39 | 5 | | 40 | 5 | | 41 | 34 | | 42 | 9 | | 43 | 4 | | 44 | 7 | | 45 | 14 | | 46 | 2 | | 47 | 5 | | 48 | 36 | | 49 | 19 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 159 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 270 | | matches | (empty) | |
| 96.32% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 5 | | totalSentences | 307 | | ratio | 0.016 | | matches | | 0 | "The other—black, black as a shut room—did not." | | 1 | "Lucien set the ivory handle of his cane against the threshold—not hard, but firmly enough to stop it." | | 2 | "His jaw tightened; his black eye seemed to swallow the light." | | 3 | "Their fingers brushed—brief, warm, devastatingly familiar." | | 4 | "The words sat between them, tender and dangerous, and she felt her anger shift—not vanish, not forgive, only make room for something she hadn’t been ready to name." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1405 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.034875444839857654 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.0071174377224199285 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 307 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 307 | | mean | 7.15 | | std | 5.58 | | cv | 0.78 | | sampleLengths | | 0 | 6 | | 1 | 18 | | 2 | 8 | | 3 | 26 | | 4 | 7 | | 5 | 8 | | 6 | 9 | | 7 | 3 | | 8 | 5 | | 9 | 2 | | 10 | 7 | | 11 | 6 | | 12 | 6 | | 13 | 18 | | 14 | 14 | | 15 | 6 | | 16 | 10 | | 17 | 2 | | 18 | 7 | | 19 | 6 | | 20 | 3 | | 21 | 2 | | 22 | 13 | | 23 | 12 | | 24 | 15 | | 25 | 19 | | 26 | 17 | | 27 | 4 | | 28 | 6 | | 29 | 16 | | 30 | 10 | | 31 | 7 | | 32 | 5 | | 33 | 4 | | 34 | 5 | | 35 | 4 | | 36 | 4 | | 37 | 5 | | 38 | 7 | | 39 | 12 | | 40 | 6 | | 41 | 3 | | 42 | 6 | | 43 | 18 | | 44 | 7 | | 45 | 4 | | 46 | 4 | | 47 | 11 | | 48 | 10 | | 49 | 18 |
| |
| 46.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.26058631921824105 | | totalSentences | 307 | | uniqueOpeners | 80 | |
| 98.77% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 135 | | matches | | 0 | "Instead, she found herself watching" | | 1 | "Too close for the flat," | | 2 | "Instead, she said," | | 3 | "Instead, she looked at the" |
| | ratio | 0.03 | |
| 39.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 135 | | matches | | 0 | "His platinum hair lay slicked" | | 1 | "She began to close the" | | 2 | "She looked at the cane," | | 3 | "She should call Eva." | | 4 | "She should shout for someone" | | 5 | "She should let the chain" | | 6 | "Her mouth went thin." | | 7 | "He lowered the cane from" | | 8 | "She shut the door." | | 9 | "She opened it again, just" | | 10 | "His gaze moved over the" | | 11 | "It paused on Ptolemy, who" | | 12 | "She shut the door and" | | 13 | "She laughed once." | | 14 | "She stepped past him into" | | 15 | "His eyes followed her." | | 16 | "His hands were steady." | | 17 | "She wondered if he’d rehearsed" | | 18 | "He set the glove on" | | 19 | "She rubbed it once with" |
| | ratio | 0.452 | |
| 41.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 113 | | totalSentences | 135 | | matches | | 0 | "The door opened to Lucien" | | 1 | "Rain jeweled the shoulders of" | | 2 | "His platinum hair lay slicked" | | 3 | "The other—black, black as a" | | 4 | "Rory’s hand tightened around the" | | 5 | "She began to close the" | | 6 | "Lucien set the ivory handle" | | 7 | "The blade concealed inside it" | | 8 | "Rory’s pulse gave one abrupt" | | 9 | "She looked at the cane," | | 10 | "The flat smelled of old" | | 11 | "Books crowded every surface, their" | | 12 | "Rory had spent the evening" | | 13 | "She should call Eva." | | 14 | "She should shout for someone" | | 15 | "She should let the chain" | | 16 | "Her mouth went thin." | | 17 | "Lucien glanced at the chain." | | 18 | "The briefest change crossed his" | | 19 | "Lucien was too practiced for" |
| | ratio | 0.837 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 135 | | matches | | 0 | "Now Lucien stood in her" | | 1 | "Even with his coat soaked" | | 2 | "Now it sounded like a" |
| | ratio | 0.022 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 2 | | matches | | 0 | "Lucien looked down as if the cat had presented him with an unexpected diplomatic treaty." | | 1 | "She searched his face for the familiar private amusement, the faint smile that used to make her want to argue just to see whether she could wipe it away." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 2 | | fancyTags | | 0 | "Rory muttered (mutter)" | | 1 | "he agreed (agree)" |
| | dialogueSentences | 172 | | tagDensity | 0.11 | | leniency | 0.221 | | rawRatio | 0.105 | | effectiveRatio | 0.023 | |