| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1061 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 95.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1061 | | 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 | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 8 | | totalWords | 1061 | | ratio | 0.008 | | matches | | 0 | "Mate's kid drew it," | | 1 | "Reckons it's a spell." |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 742 | | uniqueNames | 7 | | maxNameDensity | 0.94 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 7 | | Patel | 7 | | Tube | 1 | | TfL | 1 | | Londoners | 1 | | Morris | 1 | | Ford | 6 |
| | persons | | 0 | "Quinn" | | 1 | "Patel" | | 2 | "Tube" | | 3 | "Londoners" | | 4 | "Morris" | | 5 | "Ford" |
| | places | | | 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 | 1061 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 112 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 19.29 | | std | 22.61 | | cv | 1.172 | | sampleLengths | | 0 | 23 | | 1 | 16 | | 2 | 39 | | 3 | 2 | | 4 | 18 | | 5 | 1 | | 6 | 80 | | 7 | 22 | | 8 | 1 | | 9 | 23 | | 10 | 2 | | 11 | 70 | | 12 | 7 | | 13 | 8 | | 14 | 4 | | 15 | 7 | | 16 | 22 | | 17 | 3 | | 18 | 9 | | 19 | 44 | | 20 | 61 | | 21 | 4 | | 22 | 76 | | 23 | 4 | | 24 | 4 | | 25 | 4 | | 26 | 75 | | 27 | 5 | | 28 | 76 | | 29 | 1 | | 30 | 4 | | 31 | 2 | | 32 | 2 | | 33 | 33 | | 34 | 1 | | 35 | 12 | | 36 | 4 | | 37 | 24 | | 38 | 6 | | 39 | 61 | | 40 | 11 | | 41 | 8 | | 42 | 17 | | 43 | 4 | | 44 | 5 | | 45 | 32 | | 46 | 9 | | 47 | 2 | | 48 | 9 | | 49 | 28 |
| |
| 82.48% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 77 | | matches | | 0 | "been arranged" | | 1 | "were furred" | | 2 | "been carried" | | 3 | "was scorched" | | 4 | "been held" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 133 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 749 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.03204272363150868 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0053404539385847796 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 9.47 | | std | 8.47 | | cv | 0.894 | | sampleLengths | | 0 | 5 | | 1 | 18 | | 2 | 16 | | 3 | 14 | | 4 | 21 | | 5 | 4 | | 6 | 2 | | 7 | 18 | | 8 | 1 | | 9 | 13 | | 10 | 16 | | 11 | 2 | | 12 | 22 | | 13 | 19 | | 14 | 8 | | 15 | 5 | | 16 | 17 | | 17 | 1 | | 18 | 23 | | 19 | 2 | | 20 | 15 | | 21 | 4 | | 22 | 5 | | 23 | 27 | | 24 | 2 | | 25 | 17 | | 26 | 7 | | 27 | 8 | | 28 | 4 | | 29 | 2 | | 30 | 3 | | 31 | 2 | | 32 | 8 | | 33 | 14 | | 34 | 3 | | 35 | 9 | | 36 | 13 | | 37 | 28 | | 38 | 3 | | 39 | 5 | | 40 | 5 | | 41 | 3 | | 42 | 28 | | 43 | 18 | | 44 | 2 | | 45 | 4 | | 46 | 10 | | 47 | 15 | | 48 | 4 | | 49 | 4 |
| |
| 83.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5357142857142857 | | totalSentences | 112 | | uniqueOpeners | 60 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 67 | | matches | | 0 | "His left hand rested palm-up" | | 1 | "His right hand lay flat" | | 2 | "She heard Patel relay it" | | 3 | "He'd been carried down." | | 4 | "Her leather watch strap creaked" | | 5 | "She adjusted it without thinking," | | 6 | "She lifted his right hand," | | 7 | "She'd seen that mark before." | | 8 | "He'd laughed when she'd asked" | | 9 | "She'd believed him less when" | | 10 | "He stopped two steps above" | | 11 | "She stood, knees protesting" | | 12 | "She looked down at the" |
| | ratio | 0.194 | |
| 86.87% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 67 | | matches | | 0 | "The body had been arranged." | | 1 | "That was Quinn's first thought," | | 2 | "DC Patel held the tape" | | 3 | "The stairwell smelled of damp" | | 4 | "Quinn let her eyes travel" | | 5 | "A man, mid-thirties, dressed in" | | 6 | "A watch that had stopped" | | 7 | "His left hand rested palm-up" | | 8 | "His right hand lay flat" | | 9 | "Patel flipped open her notebook." | | 10 | "Quinn pulled a pair of" | | 11 | "The stairwell led nowhere." | | 12 | "That was the first thing." | | 13 | "A decommissioned access point to" | | 14 | "Quinn leaned closer to the" | | 15 | "She heard Patel relay it" | | 16 | "Ford's voice carried down, brittle" | | 17 | "Quinn ignored him." | | 18 | "The man's shoes were clean." | | 19 | "That was the second thing." |
| | ratio | 0.746 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 27.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 5 | | matches | | 0 | "His left hand rested palm-up on the tiles, fingers curled like he'd been holding something that wasn't there anymore." | | 1 | "Ford's voice carried down, brittle with the particular pitch of a senior officer being second-guessed by someone he outranked on paper and under-ranked in every…" | | 2 | "The stairwell tiles were furred with a decade's worth of cigarette ash, pigeon lime, and the dark grease that accumulated anywhere Londoners congregated to do t…" | | 3 | "She'd believed him less when she'd found the same mark scratched into the underside of his desk after he'd gone, in the tight deliberate hand of a man who knew …" | | 4 | "Ford descended with the heaviness of a man who wanted the entire borough to hear his displeasure." |
| |
| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "She stood, knees protesting" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |