| 78.79% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "He nodded slowly [slowly]" | | 1 | "he agreed quietly [quietly]" |
| | dialogueSentences | 33 | | tagDensity | 0.485 | | leniency | 0.97 | | rawRatio | 0.125 | | effectiveRatio | 0.121 | |
| 70.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1000 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slowly" | | 1 | "very" | | 2 | "quickly" | | 3 | "slightly" |
| |
| 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) | |
| 75.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1000 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "measured" | | 1 | "stomach" | | 2 | "silence" | | 3 | "footsteps" | | 4 | "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 | 48 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 48 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 65 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 7 | | totalWords | 1000 | | ratio | 0.007 | | matches | | 0 | "Don't go back to the bar alone" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 10 | | wordCount | 587 | | uniqueNames | 6 | | maxNameDensity | 0.85 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | London | 1 | | Moreau | 1 | | Ptolemy | 1 | | Eva | 1 | | Lucien | 5 | | Books | 1 |
| | persons | | 0 | "Moreau" | | 1 | "Eva" | | 2 | "Lucien" | | 3 | "Books" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | 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 | 1000 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 65 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 31.25 | | std | 24.56 | | cv | 0.786 | | sampleLengths | | 0 | 67 | | 1 | 5 | | 2 | 75 | | 3 | 3 | | 4 | 30 | | 5 | 8 | | 6 | 38 | | 7 | 1 | | 8 | 37 | | 9 | 76 | | 10 | 6 | | 11 | 62 | | 12 | 45 | | 13 | 19 | | 14 | 29 | | 15 | 16 | | 16 | 69 | | 17 | 32 | | 18 | 50 | | 19 | 5 | | 20 | 6 | | 21 | 37 | | 22 | 78 | | 23 | 9 | | 24 | 14 | | 25 | 23 | | 26 | 15 | | 27 | 61 | | 28 | 50 | | 29 | 17 | | 30 | 9 | | 31 | 8 |
| |
| 90.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 48 | | matches | | 0 | "was slicked" | | 1 | "being lifted" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 104 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 587 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.03747870528109029 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.008517887563884156 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 65 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 65 | | mean | 15.38 | | std | 12.78 | | cv | 0.831 | | sampleLengths | | 0 | 8 | | 1 | 30 | | 2 | 5 | | 3 | 24 | | 4 | 5 | | 5 | 21 | | 6 | 28 | | 7 | 4 | | 8 | 22 | | 9 | 3 | | 10 | 17 | | 11 | 13 | | 12 | 8 | | 13 | 14 | | 14 | 24 | | 15 | 1 | | 16 | 24 | | 17 | 13 | | 18 | 34 | | 19 | 42 | | 20 | 6 | | 21 | 30 | | 22 | 4 | | 23 | 7 | | 24 | 10 | | 25 | 11 | | 26 | 27 | | 27 | 13 | | 28 | 5 | | 29 | 5 | | 30 | 14 | | 31 | 29 | | 32 | 3 | | 33 | 13 | | 34 | 40 | | 35 | 29 | | 36 | 26 | | 37 | 6 | | 38 | 4 | | 39 | 46 | | 40 | 5 | | 41 | 6 | | 42 | 8 | | 43 | 10 | | 44 | 19 | | 45 | 13 | | 46 | 65 | | 47 | 9 | | 48 | 4 | | 49 | 10 |
| |
| 85.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5384615384615384 | | totalSentences | 65 | | uniqueOpeners | 35 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 43 | | matches | | 0 | "Somewhere below, the curry house" | | 1 | "Instead she heard herself say," |
| | ratio | 0.047 | |
| 33.95% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 43 | | matches | | 0 | "She knew who it was." | | 1 | "They regarded her with the" | | 2 | "He tilted his head toward" | | 3 | "He nodded slowly, as though" | | 4 | "He stepped past her before" | | 5 | "She shut the door and," | | 6 | "He turned to face her." | | 7 | "Her stomach tightened." | | 8 | "His voice softened, which was" | | 9 | "She folded her arms, aware" | | 10 | "She made herself drop her" | | 11 | "he said at last" | | 12 | "he agreed quietly" | | 13 | "She should have told him" | | 14 | "She had the words ready," | | 15 | "His mouth twitched, almost a" | | 16 | "She crossed to the kitchen," | | 17 | "He stopped at the counter," | | 18 | "he said, and this time" | | 19 | "She closed her eyes." |
| | ratio | 0.465 | |
| 18.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 43 | | matches | | 0 | "The first deadbolt slid back" | | 1 | "The second took two tries," | | 2 | "She knew who it was." | | 3 | "Nobody else in London knocked" | | 4 | "Rory pulled the door open." | | 5 | "Lucien Moreau stood on the" | | 6 | "The charcoal suit was immaculate," | | 7 | "Amber eye, black eye." | | 8 | "They regarded her with the" | | 9 | "Nobody called her that anymore" | | 10 | "Lucien said it like a" | | 11 | "He tilted his head toward" | | 12 | "He nodded slowly, as though" | | 13 | "He stepped past her before" | | 14 | "Rory muttered to the cat" | | 15 | "Lucien set his cane against" | | 16 | "Books stood in towers." | | 17 | "Scrolls leaned in corners like" | | 18 | "Paper covered the coffee table," | | 19 | "She shut the door and," |
| | ratio | 0.884 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 17 | | technicalSentenceCount | 4 | | matches | | 0 | "Nobody else in London knocked with that particular patience, three measured raps spaced like a metronome, as if the door owed him an answer." | | 1 | "The charcoal suit was immaculate, cut close across the shoulders, and his platinum hair was slicked back as if he had walked through a storm without acknowledgi…" | | 2 | "Behind her, Ptolemy the tabby abandoned his post on the bookshelf, dropped onto a stack of Eva's research notes with a thud, and fixed Lucien with the suspiciou…" | | 3 | "Behind her, she heard the faint tap of the cane being lifted, then the soft footsteps of a man who had not walked into a room without knowing exactly where to s…" |
| |
| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "He nodded slowly, as though she had given a reasonable answer to a question he had not expected her to answer at all" |
| |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 3 | | fancyTags | | 0 | "Rory muttered (mutter)" | | 1 | "he agreed quietly (agree)" | | 2 | "she heard (hear)" |
| | dialogueSentences | 33 | | tagDensity | 0.303 | | leniency | 0.606 | | rawRatio | 0.3 | | effectiveRatio | 0.182 | |