| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 904 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 66.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 904 | | totalAiIsms | 6 | | found | | 0 | | word | "down her spine" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "down her spine" | | 1 | "familiar" | | 2 | "flickered" | | 3 | "electric" | | 4 | "pulsed" | | 5 | "silence" |
| |
| 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 | 60 | | matches | (empty) | |
| 95.24% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 60 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 63 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 904 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 845 | | uniqueNames | 21 | | maxNameDensity | 0.71 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 6 | | Tomás | 2 | | Herrera | 5 | | Soho | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Raven | 1 | | Nest | 1 | | Georgian | 1 | | Euston | 2 | | Road | 2 | | London | 1 | | Hackney | 1 | | Morris | 3 | | Bethnal | 1 | | Green | 1 | | Victorian | 1 | | Tube | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Shaftesbury" | | 2 | "Avenue" | | 3 | "Euston" | | 4 | "Road" | | 5 | "London" | | 6 | "Bethnal" |
| | globalScore | 1 | | windowScore | 1 | |
| 91.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 904 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 63 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 45.2 | | std | 27.79 | | cv | 0.615 | | sampleLengths | | 0 | 82 | | 1 | 68 | | 2 | 82 | | 3 | 92 | | 4 | 53 | | 5 | 38 | | 6 | 59 | | 7 | 76 | | 8 | 13 | | 9 | 54 | | 10 | 16 | | 11 | 8 | | 12 | 61 | | 13 | 7 | | 14 | 8 | | 15 | 75 | | 16 | 31 | | 17 | 25 | | 18 | 39 | | 19 | 17 |
| |
| 93.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 60 | | matches | | 0 | "been told" | | 1 | "been bricked" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 141 | | matches | | |
| 97.51% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 63 | | ratio | 0.016 | | matches | | 0 | "She had not bothered to call it in; the signal had cut out somewhere around the Euston Road, and she had not wanted to waste a second on a sergeant who would ask for a grid reference she couldn't give." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 846 | | adjectiveStacks | 1 | | stackExamples | | 0 | "occasional fire-damaged corpse." |
| | adverbCount | 27 | | adverbRatio | 0.031914893617021274 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0070921985815602835 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 63 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 63 | | mean | 14.35 | | std | 9.52 | | cv | 0.663 | | sampleLengths | | 0 | 26 | | 1 | 25 | | 2 | 4 | | 3 | 4 | | 4 | 23 | | 5 | 8 | | 6 | 21 | | 7 | 23 | | 8 | 16 | | 9 | 4 | | 10 | 16 | | 11 | 14 | | 12 | 27 | | 13 | 21 | | 14 | 5 | | 15 | 25 | | 16 | 5 | | 17 | 13 | | 18 | 19 | | 19 | 25 | | 20 | 10 | | 21 | 15 | | 22 | 19 | | 23 | 9 | | 24 | 8 | | 25 | 10 | | 26 | 20 | | 27 | 10 | | 28 | 30 | | 29 | 4 | | 30 | 15 | | 31 | 1 | | 32 | 8 | | 33 | 23 | | 34 | 5 | | 35 | 23 | | 36 | 7 | | 37 | 9 | | 38 | 13 | | 39 | 15 | | 40 | 9 | | 41 | 30 | | 42 | 7 | | 43 | 5 | | 44 | 4 | | 45 | 8 | | 46 | 28 | | 47 | 33 | | 48 | 7 | | 49 | 3 |
| |
| 69.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.47619047619047616 | | totalSentences | 63 | | uniqueOpeners | 30 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 54 | | matches | | 0 | "Somewhere north of Euston Road" | | 1 | "Somewhere in that dark was" |
| | ratio | 0.037 | |
| 57.04% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 54 | | matches | | 0 | "He didn't look back." | | 1 | "He didn't need to." | | 2 | "He had been running from" | | 3 | "They had left Soho behind" | | 4 | "Her left wrist ached." | | 5 | "She ignored it, the way" | | 6 | "He cut left, hard, through" | | 7 | "She caught herself on a" | | 8 | "She climbed the wall and" | | 9 | "It was not electric." | | 10 | "It was the colour of" | | 11 | "She drew her breath and" | | 12 | "He was breathing hard, but" | | 13 | "His right hand rested against" | | 14 | "His accent softened the words." | | 15 | "He glanced down into the" | | 16 | "His silence was answer enough." | | 17 | "Her radio was dead." | | 18 | "She had not bothered to" | | 19 | "Her warrant card felt absurdly" |
| | ratio | 0.407 | |
| 61.85% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 54 | | matches | | 0 | "The rain had found the" | | 1 | "He didn't look back." | | 2 | "He didn't need to." | | 3 | "He had been running from" | | 4 | "They had left Soho behind" | | 5 | "The Raven's Nest, its green" | | 6 | "Her left wrist ached." | | 7 | "She ignored it, the way" | | 8 | "The leather strap of her" | | 9 | "Morris had taught her that" | | 10 | "The coroner's report said *unexplained*." | | 11 | "The forensic team had said" | | 12 | "Quinn had said plenty, most" | | 13 | "Tomás Herrera's name had come" | | 14 | "He cut left, hard, through" | | 15 | "Quinn followed, her shoulder scraping" | | 16 | "She caught herself on a" | | 17 | "Something about the way he" | | 18 | "The rain seemed to fall" | | 19 | "She climbed the wall and" |
| | ratio | 0.796 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "She ignored it, the way she ignored everything that wasn't the man in front of her." | | 1 | "Quinn had said plenty, most of it to people who stopped listening the moment she mentioned the word *wrong*." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |