| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "Carys stepped back [back]" |
| | dialogueSentences | 116 | | tagDensity | 0.164 | | leniency | 0.328 | | rawRatio | 0.053 | | effectiveRatio | 0.017 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1898 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 97.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1898 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 0 | | narrationSentences | 108 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 108 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 205 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1898 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 29 | | unquotedAttributions | 1 | | matches | | 0 | "Beyond the windows, Soho hissed with tyres and umbrellas." |
| |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 87 | | wordCount | 1036 | | uniqueNames | 10 | | maxNameDensity | 3.47 | | worstName | "Rory" | | maxWindowNameDensity | 6 | | worstWindowName | "Carys" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Berlin | 1 | | Rory | 36 | | Silas | 11 | | Morgan | 1 | | Carys | 33 | | London | 1 | | Christmas | 1 |
| | persons | | 0 | "Raven" | | 1 | "Rory" | | 2 | "Silas" | | 3 | "Morgan" | | 4 | "Carys" |
| | places | | 0 | "Soho" | | 1 | "Berlin" | | 2 | "London" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | 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 | 1898 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 205 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 146 | | mean | 13 | | std | 12.72 | | cv | 0.979 | | sampleLengths | | 0 | 60 | | 1 | 50 | | 2 | 14 | | 3 | 4 | | 4 | 4 | | 5 | 38 | | 6 | 14 | | 7 | 5 | | 8 | 12 | | 9 | 45 | | 10 | 1 | | 11 | 28 | | 12 | 50 | | 13 | 6 | | 14 | 6 | | 15 | 6 | | 16 | 41 | | 17 | 9 | | 18 | 9 | | 19 | 13 | | 20 | 5 | | 21 | 4 | | 22 | 3 | | 23 | 19 | | 24 | 23 | | 25 | 4 | | 26 | 16 | | 27 | 14 | | 28 | 9 | | 29 | 45 | | 30 | 6 | | 31 | 3 | | 32 | 19 | | 33 | 4 | | 34 | 4 | | 35 | 31 | | 36 | 1 | | 37 | 2 | | 38 | 15 | | 39 | 9 | | 40 | 12 | | 41 | 3 | | 42 | 33 | | 43 | 4 | | 44 | 2 | | 45 | 8 | | 46 | 5 | | 47 | 22 | | 48 | 27 | | 49 | 13 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 108 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 201 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 205 | | ratio | 0.01 | | matches | | 0 | "Two men argued over a crossword beneath a map of pre-war Berlin; near the bookshelf, a woman slept beside a gin she had never touched." | | 1 | "One looked over; Carys folded her cocktail napkin into a narrow strip and kept her eyes on Rory." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1037 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 29 | | adverbRatio | 0.027965284474445518 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009643201542912247 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 205 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 205 | | mean | 9.26 | | std | 6.85 | | cv | 0.74 | | sampleLengths | | 0 | 20 | | 1 | 19 | | 2 | 12 | | 3 | 9 | | 4 | 6 | | 5 | 25 | | 6 | 8 | | 7 | 11 | | 8 | 14 | | 9 | 4 | | 10 | 4 | | 11 | 29 | | 12 | 9 | | 13 | 4 | | 14 | 10 | | 15 | 5 | | 16 | 12 | | 17 | 14 | | 18 | 16 | | 19 | 15 | | 20 | 1 | | 21 | 9 | | 22 | 19 | | 23 | 15 | | 24 | 21 | | 25 | 9 | | 26 | 5 | | 27 | 6 | | 28 | 6 | | 29 | 6 | | 30 | 2 | | 31 | 15 | | 32 | 9 | | 33 | 15 | | 34 | 9 | | 35 | 9 | | 36 | 10 | | 37 | 3 | | 38 | 5 | | 39 | 4 | | 40 | 3 | | 41 | 3 | | 42 | 16 | | 43 | 23 | | 44 | 4 | | 45 | 16 | | 46 | 14 | | 47 | 9 | | 48 | 5 | | 49 | 16 |
| |
| 45.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3024390243902439 | | totalSentences | 205 | | uniqueOpeners | 62 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 92 | | matches | | 0 | "Then the woman slid down" | | 1 | "Somewhere beyond the wall, a" | | 2 | "Then she sat back." |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 92 | | matches | | 0 | "His silver signet ring tapped" | | 1 | "He took in Rory’s soaked" | | 2 | "She stowed the delivery bag" | | 3 | "He reached beneath the counter" | | 4 | "She had peeled one wet" | | 5 | "Her eyebrows remained the same:" | | 6 | "Their embrace caught at one" | | 7 | "She returned to her wine," | | 8 | "He set it before Rory" | | 9 | "She glanced at the photographs" | | 10 | "Her cuff slipped back, exposing" | | 11 | "She watched Silas at the" | | 12 | "He had found an invoice" | | 13 | "She unfolded the napkin." | | 14 | "Its creases had cut it" | | 15 | "She took her phone from" | | 16 | "It spread beneath her finger" | | 17 | "It moved through her face" | | 18 | "He set them down, collected" | | 19 | "His signet ring flashed as" |
| | ratio | 0.239 | |
| 8.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 92 | | matches | | 0 | "Rain had copied the green" | | 1 | "Rory stepped over the brightest" | | 2 | "Water darkened her black hair" | | 3 | "Tuesday had emptied the place" | | 4 | "Silas stood behind the bar," | | 5 | "His silver signet ring tapped" | | 6 | "He took in Rory’s soaked" | | 7 | "She stowed the delivery bag" | | 8 | "He reached beneath the counter" | | 9 | "Silas moved towards the back," | | 10 | "Rory pulled out a stool" | | 11 | "She had peeled one wet" | | 12 | "The name crossed the bar" | | 13 | "Carys Morgan had lost the" | | 14 | "Her eyebrows remained the same:" | | 15 | "A navy coat hung open" | | 16 | "Rain jewelled its shoulders too." | | 17 | "Rory drew off the second" | | 18 | "The sound loosened something in" | | 19 | "Rory shifted the supper carton" |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 116 | | tagDensity | 0.121 | | leniency | 0.241 | | rawRatio | 0 | | effectiveRatio | 0 | |