| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1190 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 32.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1190 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "traced" | | 1 | "flicker" | | 2 | "thundered" | | 3 | "echoed" | | 4 | "fluttered" | | 5 | "scanned" | | 6 | "marble" | | 7 | "gleaming" | | 8 | "weight" | | 9 | "pulse" | | 10 | "flickered" | | 11 | "echo" | | 12 | "silence" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "lump in throat" | | count | 1 |
|
| | highlights | | 0 | "A lump formed in her throat" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 152 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1177 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 1174 | | uniqueNames | 6 | | maxNameDensity | 0.94 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Morris | 2 | | Veil | 2 | | Market | 2 | | Quinn | 11 |
| | persons | | 0 | "Raven" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Quinn" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | 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 | 1177 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 152 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 33.63 | | std | 16.88 | | cv | 0.502 | | sampleLengths | | 0 | 28 | | 1 | 2 | | 2 | 30 | | 3 | 34 | | 4 | 59 | | 5 | 40 | | 6 | 57 | | 7 | 19 | | 8 | 44 | | 9 | 46 | | 10 | 57 | | 11 | 58 | | 12 | 50 | | 13 | 40 | | 14 | 52 | | 15 | 4 | | 16 | 56 | | 17 | 23 | | 18 | 8 | | 19 | 39 | | 20 | 47 | | 21 | 42 | | 22 | 9 | | 23 | 6 | | 24 | 26 | | 25 | 16 | | 26 | 12 | | 27 | 38 | | 28 | 55 | | 29 | 29 | | 30 | 39 | | 31 | 20 | | 32 | 35 | | 33 | 33 | | 34 | 24 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 237 | | matches | (empty) | |
| 11.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 152 | | ratio | 0.046 | | matches | | 0 | "She traced the suspect’s footprints—ducking out every few paces to check his trail—footprints pressed deep into puddles." | | 1 | "She unhooked her leather watch, twisted the strap, and checked the time—1:17 a.m." | | 2 | "She followed the suspect’s scent—cold sweat and cheap cologne." | | 3 | "In one hand, he held a bone token—smooth, ivory crescent carved into a talon." | | 4 | "Every second, the market’s pulse grew louder—voices, shuffling feet, the scrape of metal." | | 5 | "The memory of DS Morris flashed before her—his pale face in the fog of some midnight ritual." | | 6 | "She glanced at the forest of bone tokens nailed to a wooden post beside her—each carved differently: raven’s wing, wolf tooth, crescent moon." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1187 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.018534119629317607 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002527379949452401 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 7.74 | | std | 4.33 | | cv | 0.559 | | sampleLengths | | 0 | 14 | | 1 | 7 | | 2 | 7 | | 3 | 2 | | 4 | 17 | | 5 | 4 | | 6 | 5 | | 7 | 4 | | 8 | 13 | | 9 | 5 | | 10 | 9 | | 11 | 5 | | 12 | 2 | | 13 | 8 | | 14 | 5 | | 15 | 22 | | 16 | 10 | | 17 | 6 | | 18 | 2 | | 19 | 6 | | 20 | 13 | | 21 | 3 | | 22 | 10 | | 23 | 4 | | 24 | 4 | | 25 | 6 | | 26 | 9 | | 27 | 5 | | 28 | 4 | | 29 | 14 | | 30 | 6 | | 31 | 14 | | 32 | 5 | | 33 | 3 | | 34 | 4 | | 35 | 6 | | 36 | 6 | | 37 | 9 | | 38 | 14 | | 39 | 7 | | 40 | 5 | | 41 | 9 | | 42 | 8 | | 43 | 5 | | 44 | 7 | | 45 | 4 | | 46 | 12 | | 47 | 5 | | 48 | 5 | | 49 | 17 |
| |
| 55.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3684210526315789 | | totalSentences | 152 | | uniqueOpeners | 56 | |
| 22.99% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 145 | | matches | | 0 | "Somewhere past a makeshift archway," |
| | ratio | 0.007 | |
| 87.59% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 145 | | matches | | 0 | "She pressed a fingertip to" | | 1 | "She traced the suspect’s footprints—ducking" | | 2 | "Her calf muscles burned." | | 3 | "She’d lost him once tonight." | | 4 | "He spun at her approach." | | 5 | "He paused, glanced back, and" | | 6 | "His eyes glowed with savage" | | 7 | "He lunged forward, sprinted across" | | 8 | "She thundered after him, heart" | | 9 | "She descended two at a" | | 10 | "Her lunges echoed." | | 11 | "She reached a landing and" | | 12 | "She could circle, trap him." | | 13 | "Her partner, Morris, would have" | | 14 | "She shook off the doubt." | | 15 | "She slid the panel aside" | | 16 | "She edged forward, every sense" | | 17 | "She unhooked her leather watch," | | 18 | "She stood alone in a" | | 19 | "She followed the suspect’s scent—cold" |
| | ratio | 0.331 | |
| 49.66% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 145 | | matches | | 0 | "Quinn crouched behind a rusted" | | 1 | "Streetlamps bled yellow pools on" | | 2 | "She pressed a fingertip to" | | 3 | "She traced the suspect’s footprints—ducking" | | 4 | "Her calf muscles burned." | | 5 | "She’d lost him once tonight." | | 6 | "A figure materialised at the" | | 7 | "He spun at her approach." | | 8 | "Rain dripped from the brim." | | 9 | "Quinn vaulted over the curb," | | 10 | "Water splashed around her boots." | | 11 | "The suspect darted between dumpsters," | | 12 | "Flicker of green glow painted" | | 13 | "He paused, glanced back, and" | | 14 | "His eyes glowed with savage" | | 15 | "He lunged forward, sprinted across" | | 16 | "Quinn didn’t hesitate." | | 17 | "She thundered after him, heart" | | 18 | "Engines idled inside garages." | | 19 | "Cats dashed behind crates." |
| | ratio | 0.821 | |
| 68.97% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 145 | | matches | | 0 | "Now she wondered how many" | | 1 | "By the time they climbed" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "the merchant hissed (hiss)" |
| | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |