| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 157 | | tagDensity | 0.083 | | leniency | 0.166 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2412 | | 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) | |
| 97.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2412 | | 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 | 144 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 144 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 288 | | 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 | 0 | | markdownWords | 0 | | totalWords | 2412 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 80 | | wordCount | 1293 | | uniqueNames | 7 | | maxNameDensity | 2.78 | | worstName | "Quinn" | | maxWindowNameDensity | 6 | | worstWindowName | "Grant" | | discoveredNames | | Harlow | 1 | | Quinn | 36 | | Daniel | 1 | | Malik | 13 | | Bell | 6 | | Grant | 18 | | Blood | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Daniel" | | 3 | "Malik" | | 4 | "Bell" | | 5 | "Grant" | | 6 | "Blood" |
| | places | (empty) | | globalScore | 0.108 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | 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 | 2412 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 288 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 213 | | mean | 11.32 | | std | 11.04 | | cv | 0.975 | | sampleLengths | | 0 | 14 | | 1 | 3 | | 2 | 37 | | 3 | 8 | | 4 | 15 | | 5 | 59 | | 6 | 19 | | 7 | 16 | | 8 | 7 | | 9 | 7 | | 10 | 5 | | 11 | 8 | | 12 | 18 | | 13 | 43 | | 14 | 13 | | 15 | 41 | | 16 | 3 | | 17 | 28 | | 18 | 5 | | 19 | 4 | | 20 | 27 | | 21 | 5 | | 22 | 10 | | 23 | 3 | | 24 | 13 | | 25 | 3 | | 26 | 8 | | 27 | 39 | | 28 | 5 | | 29 | 3 | | 30 | 6 | | 31 | 10 | | 32 | 47 | | 33 | 1 | | 34 | 13 | | 35 | 3 | | 36 | 8 | | 37 | 18 | | 38 | 5 | | 39 | 4 | | 40 | 34 | | 41 | 4 | | 42 | 21 | | 43 | 4 | | 44 | 17 | | 45 | 28 | | 46 | 2 | | 47 | 20 | | 48 | 5 | | 49 | 6 |
| |
| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 144 | | matches | | 0 | "been chalked" | | 1 | "been scratched" | | 2 | "been squared" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 201 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 288 | | ratio | 0.003 | | matches | | 0 | "One pointed towards the body; the other pointed back towards Grant’s table." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1297 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.013107170393215111 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0007710100231303007 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 288 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 288 | | mean | 8.38 | | std | 5.47 | | cv | 0.653 | | sampleLengths | | 0 | 14 | | 1 | 3 | | 2 | 12 | | 3 | 16 | | 4 | 9 | | 5 | 8 | | 6 | 15 | | 7 | 17 | | 8 | 17 | | 9 | 14 | | 10 | 11 | | 11 | 9 | | 12 | 10 | | 13 | 16 | | 14 | 7 | | 15 | 7 | | 16 | 5 | | 17 | 8 | | 18 | 10 | | 19 | 8 | | 20 | 12 | | 21 | 15 | | 22 | 16 | | 23 | 5 | | 24 | 8 | | 25 | 5 | | 26 | 8 | | 27 | 13 | | 28 | 15 | | 29 | 3 | | 30 | 28 | | 31 | 5 | | 32 | 4 | | 33 | 5 | | 34 | 13 | | 35 | 9 | | 36 | 5 | | 37 | 10 | | 38 | 3 | | 39 | 5 | | 40 | 8 | | 41 | 3 | | 42 | 8 | | 43 | 7 | | 44 | 11 | | 45 | 21 | | 46 | 5 | | 47 | 3 | | 48 | 6 | | 49 | 10 |
| |
| 46.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.2881944444444444 | | totalSentences | 288 | | uniqueOpeners | 83 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 132 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 132 | | matches | | 0 | "She placed it in the" | | 1 | "Their light fell across market" | | 2 | "He wore a paper suit" | | 3 | "Its bars showed rust beneath" | | 4 | "His palm lay clean against" | | 5 | "Its back faced the neighbouring" | | 6 | "Its largest piece lay beneath" | | 7 | "She beckoned to a scene" | | 8 | "Its round face rolled a" | | 9 | "She pointed to the wardrobe" | | 10 | "She left it where it" | | 11 | "He pointed to a square" | | 12 | "It began at the grille" | | 13 | "She held out her hand." | | 14 | "She had cropped black hair" | | 15 | "Her boots stood on the" | | 16 | "she said as Quinn approached" | | 17 | "Her coat lay folded on" | | 18 | "Her cut knuckle faced Quinn." | | 19 | "He called a scene officer" |
| | ratio | 0.167 | |
| 9.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 132 | | matches | | 0 | "The constable at the bottom" | | 1 | "Detective Harlow Quinn took a" | | 2 | "A hole pierced one end," | | 3 | "She placed it in the" | | 4 | "the constable asked" | | 5 | "The constable picked up the" | | 6 | "The gate behind him opened" | | 7 | "Their light fell across market" | | 8 | "Canvas roofs crowded the tiled" | | 9 | "VEIL MARKET had been chalked" | | 10 | "Someone had rubbed out the" | | 11 | "A woman in a silver" | | 12 | "The woman caught sight of" | | 13 | "Quinn passed her and ducked" | | 14 | "Sergeant Daniel Malik waited beside" | | 15 | "He wore a paper suit" | | 16 | "Quinn crouched beside the body." | | 17 | "Bell’s white shirt had opened" | | 18 | "Blood soaked the front of" | | 19 | "A narrow knife lay near" |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 132 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | 12 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 157 | | tagDensity | 0.076 | | leniency | 0.153 | | rawRatio | 0.083 | | effectiveRatio | 0.013 | |