| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva drummed perfectly [perfectly]" |
| | dialogueSentences | 98 | | tagDensity | 0.173 | | leniency | 0.347 | | rawRatio | 0.059 | | effectiveRatio | 0.02 | |
| 95.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2136 | | totalAiIsmAdverbs | 2 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | |
| | highlights | | 0 | "deliberately" | | 1 | "perfectly" |
| |
| 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) | |
| 71.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2136 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "traced" | | 1 | "scanned" | | 2 | "silk" | | 3 | "trembled" | | 4 | "throbbed" | | 5 | "potential" | | 6 | "facade" | | 7 | "shattered" | | 8 | "navigating" | | 9 | "flickered" | | 10 | "chaotic" |
| |
| 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 | 122 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 122 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 203 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2119 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 75 | | wordCount | 1071 | | uniqueNames | 9 | | maxNameDensity | 2.43 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Rory | 23 | | Berlin | 1 | | Eva | 26 | | Cardiff | 2 | | Paddington | 1 | | Silas | 19 | | Ellis | 1 |
| | persons | | | places | | 0 | "Raven" | | 1 | "Berlin" | | 2 | "Cardiff" | | 3 | "Paddington" | | 4 | "Ellis" |
| | globalScore | 0.286 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | 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 | 2119 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 203 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 124 | | mean | 17.09 | | std | 13.39 | | cv | 0.784 | | sampleLengths | | 0 | 59 | | 1 | 31 | | 2 | 22 | | 3 | 17 | | 4 | 4 | | 5 | 21 | | 6 | 61 | | 7 | 5 | | 8 | 58 | | 9 | 35 | | 10 | 1 | | 11 | 1 | | 12 | 17 | | 13 | 29 | | 14 | 14 | | 15 | 5 | | 16 | 16 | | 17 | 47 | | 18 | 9 | | 19 | 4 | | 20 | 18 | | 21 | 11 | | 22 | 5 | | 23 | 8 | | 24 | 24 | | 25 | 19 | | 26 | 13 | | 27 | 29 | | 28 | 17 | | 29 | 6 | | 30 | 7 | | 31 | 29 | | 32 | 14 | | 33 | 28 | | 34 | 4 | | 35 | 4 | | 36 | 19 | | 37 | 30 | | 38 | 10 | | 39 | 46 | | 40 | 8 | | 41 | 5 | | 42 | 7 | | 43 | 22 | | 44 | 6 | | 45 | 12 | | 46 | 8 | | 47 | 2 | | 48 | 36 | | 49 | 17 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 122 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 179 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 203 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1085 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.009216589861751152 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003686635944700461 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 203 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 203 | | mean | 10.44 | | std | 6.97 | | cv | 0.668 | | sampleLengths | | 0 | 8 | | 1 | 15 | | 2 | 10 | | 3 | 9 | | 4 | 17 | | 5 | 11 | | 6 | 10 | | 7 | 10 | | 8 | 11 | | 9 | 11 | | 10 | 17 | | 11 | 4 | | 12 | 9 | | 13 | 10 | | 14 | 2 | | 15 | 6 | | 16 | 7 | | 17 | 11 | | 18 | 9 | | 19 | 9 | | 20 | 19 | | 21 | 5 | | 22 | 7 | | 23 | 26 | | 24 | 25 | | 25 | 14 | | 26 | 8 | | 27 | 13 | | 28 | 1 | | 29 | 1 | | 30 | 5 | | 31 | 12 | | 32 | 4 | | 33 | 16 | | 34 | 9 | | 35 | 14 | | 36 | 5 | | 37 | 2 | | 38 | 6 | | 39 | 8 | | 40 | 25 | | 41 | 22 | | 42 | 9 | | 43 | 4 | | 44 | 2 | | 45 | 6 | | 46 | 10 | | 47 | 11 | | 48 | 5 | | 49 | 8 |
| |
| 43.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.24630541871921183 | | totalSentences | 203 | | uniqueOpeners | 50 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 117 | | matches | (empty) | | ratio | 0 | |
| 79.83% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 117 | | matches | | 0 | "She sat at the mahogany" | | 1 | "His silver signet ring clacked" | | 2 | "He set down a dark" | | 3 | "He leaned forward, elbows planted" | | 4 | "His grey-streaked auburn beard brushed" | | 5 | "She shook out a beige" | | 6 | "She pushed wet blonde hair" | | 7 | "Her boots clicked against the" | | 8 | "She scanned the room, gaze" | | 9 | "Her eyes locked onto Rory." | | 10 | "Her posture held none of" | | 11 | "He wiped down the beer" | | 12 | "She carried a structured leather" | | 13 | "She smelled of expensive bergamot" | | 14 | "She slid onto the stool," | | 15 | "His hazel eyes fixed on" | | 16 | "She swiped it away with" | | 17 | "He retreated to the far" | | 18 | "Her hand trembled, a microscopic" | | 19 | "She took a long, sharp" |
| | ratio | 0.35 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 117 | | matches | | 0 | "Rain lashed the pavement outside" | | 1 | "The distinctive green neon sign" | | 2 | "Rory traced the crescent-shaped scar" | | 3 | "She sat at the mahogany" | | 4 | "Silas dragged his bad leg" | | 5 | "His silver signet ring clacked" | | 6 | "He set down a dark" | | 7 | "He leaned forward, elbows planted" | | 8 | "His grey-streaked auburn beard brushed" | | 9 | "The heavy oak door swung" | | 10 | "A gust of wind scattered" | | 11 | "A woman stood in the" | | 12 | "She shook out a beige" | | 13 | "Rainwater slid off the water-repellent" | | 14 | "She pushed wet blonde hair" | | 15 | "Her boots clicked against the" | | 16 | "She scanned the room, gaze" | | 17 | "Her eyes locked onto Rory." | | 18 | "Rory gripped the edge of" | | 19 | "The last time she saw" |
| | ratio | 0.991 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 117 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 1 | | matches | | 0 | "Eva had worn a torn band t-shirt and smeared eyeliner, shoving cash into Rory’s hands, begging her to get on the midnight train to Paddington." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 98 | | tagDensity | 0.01 | | leniency | 0.02 | | rawRatio | 1 | | effectiveRatio | 0.02 | |