| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 34 | | tagDensity | 0.382 | | leniency | 0.765 | | rawRatio | 0.077 | | effectiveRatio | 0.059 | |
| 85.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1335 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "really" | | 2 | "carefully" |
| |
| 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) | |
| 73.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1335 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "magnetic" | | 1 | "familiar" | | 2 | "silence" | | 3 | "weight" | | 4 | "constructed" | | 5 | "unspoken" |
| |
| 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 | 80 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 80 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1335 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 5 | | wordCount | 1127 | | uniqueNames | 5 | | maxNameDensity | 0.09 | | worstName | "Soho" | | maxWindowNameDensity | 0 | | worstWindowName | (null) | | discoveredNames | | Soho | 1 | | British | 1 | | Empire | 1 | | Cardiff | 1 | | Don | 1 |
| | persons | (empty) | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 1335 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 29.67 | | std | 27.54 | | cv | 0.928 | | sampleLengths | | 0 | 92 | | 1 | 5 | | 2 | 104 | | 3 | 47 | | 4 | 11 | | 5 | 68 | | 6 | 4 | | 7 | 73 | | 8 | 1 | | 9 | 37 | | 10 | 1 | | 11 | 79 | | 12 | 5 | | 13 | 3 | | 14 | 20 | | 15 | 32 | | 16 | 5 | | 17 | 61 | | 18 | 4 | | 19 | 25 | | 20 | 3 | | 21 | 1 | | 22 | 1 | | 23 | 25 | | 24 | 39 | | 25 | 28 | | 26 | 12 | | 27 | 15 | | 28 | 80 | | 29 | 7 | | 30 | 37 | | 31 | 14 | | 32 | 5 | | 33 | 49 | | 34 | 5 | | 35 | 1 | | 36 | 7 | | 37 | 53 | | 38 | 61 | | 39 | 14 | | 40 | 37 | | 41 | 40 | | 42 | 45 | | 43 | 40 | | 44 | 39 |
| |
| 83.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 80 | | matches | | 0 | "being consumed" | | 1 | "was, fell" | | 2 | "was disappointed" | | 3 | "been invited" | | 4 | "was gone" |
| |
| 64.64% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 197 | | matches | | 0 | "was leaving" | | 1 | "was resting" | | 2 | "was actually asking" | | 3 | "was learning" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 101 | | ratio | 0 | | matches | (empty) | |
| 98.06% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1137 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 48 | | adverbRatio | 0.04221635883905013 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.013192612137203167 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 13.22 | | std | 10.76 | | cv | 0.814 | | sampleLengths | | 0 | 19 | | 1 | 38 | | 2 | 7 | | 3 | 28 | | 4 | 5 | | 5 | 5 | | 6 | 22 | | 7 | 3 | | 8 | 18 | | 9 | 20 | | 10 | 13 | | 11 | 13 | | 12 | 10 | | 13 | 27 | | 14 | 6 | | 15 | 8 | | 16 | 6 | | 17 | 11 | | 18 | 49 | | 19 | 19 | | 20 | 4 | | 21 | 6 | | 22 | 15 | | 23 | 28 | | 24 | 10 | | 25 | 14 | | 26 | 1 | | 27 | 12 | | 28 | 25 | | 29 | 1 | | 30 | 3 | | 31 | 48 | | 32 | 5 | | 33 | 6 | | 34 | 17 | | 35 | 5 | | 36 | 3 | | 37 | 16 | | 38 | 4 | | 39 | 10 | | 40 | 4 | | 41 | 18 | | 42 | 5 | | 43 | 14 | | 44 | 7 | | 45 | 3 | | 46 | 8 | | 47 | 14 | | 48 | 5 | | 49 | 10 |
| |
| 48.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.36633663366336633 | | totalSentences | 101 | | uniqueOpeners | 37 | |
| 95.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 70 | | matches | | 0 | "Even the bartender was the" | | 1 | "Then he was gone, swallowed" |
| | ratio | 0.029 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 70 | | matches | | 0 | "She pushed open the door." | | 1 | "He looked old now, but" | | 2 | "She took a seat at" | | 3 | "She ordered a gin and" | | 4 | "She was halfway through the" | | 5 | "It was a distinctive rhythm," | | 6 | "She didn't turn around." | | 7 | "He sat down two stools" | | 8 | "She watched in the bar" | | 9 | "His hair had more grey" | | 10 | "He used her surname the" | | 11 | "She turned then." | | 12 | "His hazel eyes held hers," | | 13 | "He'd given her a job." | | 14 | "He'd given her the flat" | | 15 | "He'd never asked questions, which" | | 16 | "He accepted his drink from" | | 17 | "She turned back to her" | | 18 | "She hadn't been drinking it," | | 19 | "She hadn't told him she" |
| | ratio | 0.6 | |
| 17.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 70 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Rory stood beneath it for" | | 2 | "She pushed open the door." | | 3 | "The smell hit her first." | | 4 | "Pipe tobacco and aged wood" | | 5 | "Nothing had changed." | | 6 | "The same maps on the" | | 7 | "The same black-and-white photographs in" | | 8 | "He looked old now, but" | | 9 | "Everyone looked old now when" | | 10 | "She took a seat at" | | 11 | "She ordered a gin and" | | 12 | "The bartender didn't recognize her," | | 13 | "She was halfway through the" | | 14 | "It was a distinctive rhythm," | | 15 | "The slight hitch in the" | | 16 | "She didn't turn around." | | 17 | "He sat down two stools" | | 18 | "She watched in the bar" | | 19 | "His hair had more grey" |
| | ratio | 0.886 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 4 | | matches | | 0 | "Rory stood beneath it for a long moment, rain spotting her jacket, telling herself she was only here because it was the closest shelter and her shift had ended …" | | 1 | "He used her surname the way he always had, the way he used everyone's surname, as if first names were a currency he spent sparingly." | | 2 | "He'd never asked questions, which was its own kind of kindness, though she hadn't understood that then." | | 3 | "Then he was gone, swallowed by the dark behind the bar, and she sat alone with her watered-down gin and tonic and the ghost of who she'd been, haunting her own …" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 34 | | tagDensity | 0.265 | | leniency | 0.529 | | rawRatio | 0.111 | | effectiveRatio | 0.059 | |