| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 57 | | tagDensity | 0.351 | | leniency | 0.702 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1698 | | 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) | |
| 73.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1698 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "silence" | | 2 | "scanned" | | 3 | "weight" | | 4 | "measured" | | 5 | "traced" | | 6 | "perfect" | | 7 | "flicked" |
| |
| 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 | 97 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 97 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1698 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1140 | | uniqueNames | 8 | | maxNameDensity | 2.11 | | worstName | "Eva" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 22 | | Nest | 1 | | Interwar | 1 | | Europe | 1 | | Silas | 8 | | High | 1 | | Street | 1 | | Eva | 24 |
| | persons | | | places | | 0 | "Europe" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.447 | | windowScore | 0.167 | |
| 81.51% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like it was about to say something" | | 1 | "as if pricing it" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1698 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 22.34 | | std | 20.89 | | cv | 0.935 | | sampleLengths | | 0 | 80 | | 1 | 58 | | 2 | 8 | | 3 | 24 | | 4 | 62 | | 5 | 11 | | 6 | 49 | | 7 | 6 | | 8 | 7 | | 9 | 12 | | 10 | 1 | | 11 | 98 | | 12 | 7 | | 13 | 14 | | 14 | 4 | | 15 | 27 | | 16 | 16 | | 17 | 42 | | 18 | 19 | | 19 | 3 | | 20 | 38 | | 21 | 7 | | 22 | 5 | | 23 | 17 | | 24 | 3 | | 25 | 55 | | 26 | 5 | | 27 | 2 | | 28 | 6 | | 29 | 70 | | 30 | 11 | | 31 | 43 | | 32 | 6 | | 33 | 32 | | 34 | 19 | | 35 | 4 | | 36 | 3 | | 37 | 10 | | 38 | 25 | | 39 | 9 | | 40 | 11 | | 41 | 56 | | 42 | 2 | | 43 | 57 | | 44 | 13 | | 45 | 5 | | 46 | 36 | | 47 | 12 | | 48 | 10 | | 49 | 23 |
| |
| 98.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 97 | | matches | | 0 | "been rebuilt" | | 1 | "being carried" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 210 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1150 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.023478260869565216 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 12.67 | | std | 10.06 | | cv | 0.794 | | sampleLengths | | 0 | 36 | | 1 | 8 | | 2 | 36 | | 3 | 11 | | 4 | 7 | | 5 | 40 | | 6 | 8 | | 7 | 20 | | 8 | 4 | | 9 | 5 | | 10 | 14 | | 11 | 5 | | 12 | 11 | | 13 | 27 | | 14 | 11 | | 15 | 11 | | 16 | 19 | | 17 | 7 | | 18 | 12 | | 19 | 6 | | 20 | 7 | | 21 | 12 | | 22 | 1 | | 23 | 26 | | 24 | 6 | | 25 | 30 | | 26 | 17 | | 27 | 16 | | 28 | 1 | | 29 | 1 | | 30 | 1 | | 31 | 7 | | 32 | 14 | | 33 | 4 | | 34 | 21 | | 35 | 6 | | 36 | 13 | | 37 | 3 | | 38 | 32 | | 39 | 3 | | 40 | 7 | | 41 | 19 | | 42 | 3 | | 43 | 27 | | 44 | 2 | | 45 | 9 | | 46 | 7 | | 47 | 5 | | 48 | 13 | | 49 | 4 |
| |
| 41.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.29850746268656714 | | totalSentences | 134 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 88.18% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 88 | | matches | | 0 | "She unslung the bag and" | | 1 | "He reached beneath the counter" | | 2 | "He slid the glass forward" | | 3 | "She wrapped her hand around" | | 4 | "He returned to his glass." | | 5 | "He let the walls do" | | 6 | "She wore a long grey" | | 7 | "Her boots had no scuffs" | | 8 | "She scanned the bar the" | | 9 | "Her hair, once a wild" | | 10 | "She crossed the bar with" | | 11 | "He didn't move to pour" | | 12 | "His voice came out flat," | | 13 | "He made it and set" | | 14 | "He was giving them the" | | 15 | "She breathed through it." | | 16 | "She glanced toward Silas, who" | | 17 | "She picked at the edge" | | 18 | "She raised the whisky and" | | 19 | "She smoothed her coat sleeve," |
| | ratio | 0.33 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 88 | | matches | | 0 | "The green neon sign threw" | | 1 | "The bar hummed at its" | | 2 | "She unslung the bag and" | | 3 | "Silas raised an eyebrow but" | | 4 | "He reached beneath the counter" | | 5 | "He slid the glass forward" | | 6 | "She wrapped her hand around" | | 7 | "He returned to his glass." | | 8 | "The silence between them had" | | 9 | "Rory liked that about him." | | 10 | "Silas didn't fill a room" | | 11 | "He let the walls do" | | 12 | "The door opened and let" | | 13 | "A woman stood in the" | | 14 | "She wore a long grey" | | 15 | "Her boots had no scuffs" | | 16 | "She scanned the bar the" | | 17 | "Rory's hand froze around the" | | 18 | "The woman's gaze found her" | | 19 | "Silence moved through Rory's chest" |
| | ratio | 0.943 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 35.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 6 | | matches | | 0 | "Two regulars nursed pints near the map of Interwar Europe, and Silas stood behind the counter polishing a glass that didn't need it, his silver signet ring catc…" | | 1 | "He let the walls do the talking, all those old black-and-white photographs and pencil-shaded frontiers, the maps that showed countries that no longer existed in…" | | 2 | "No one else held her mouth that way, that half-smile that looked like it was about to say something cruel and then thought better of it." | | 3 | "The Eva she remembered had worn jumpers with frayed cuffs and chewed her nails to the quick and laughed at things that weren't funny just because the sound felt…" | | 4 | "He made it and set it down without ceremony and drifted to the far end of the bar where he began rearranging bottles that didn't need rearranging." | | 5 | "Rory stood and took the folded photograph off the counter and put it in her pocket without looking at Eva's face and walked toward the staircase behind the book…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 57 | | tagDensity | 0.07 | | leniency | 0.14 | | rawRatio | 0 | | effectiveRatio | 0 | |