| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 157 | | tagDensity | 0.076 | | leniency | 0.153 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2382 | | 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) | |
| 95.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2382 | | totalAiIsms | 2 | | found | | | highlights | | |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 192 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 192 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 337 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2384 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 96 | | wordCount | 1349 | | uniqueNames | 13 | | maxNameDensity | 2.67 | | worstName | "Quinn" | | maxWindowNameDensity | 7 | | worstWindowName | "Eva" | | discoveredNames | | High | 1 | | Barnet | 1 | | Harlow | 1 | | Quinn | 36 | | Camden | 1 | | Tube | 1 | | Sergeant | 1 | | Imran | 1 | | Shah | 26 | | Eva | 20 | | Kowalski | 1 | | White | 2 | | One | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Sergeant" | | 4 | "Shah" | | 5 | "Eva" | | 6 | "Kowalski" | | 7 | "One" |
| | places | | | globalScore | 0.166 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | 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 | 2384 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 337 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 228 | | mean | 10.46 | | std | 10.72 | | cv | 1.025 | | sampleLengths | | 0 | 19 | | 1 | 69 | | 2 | 33 | | 3 | 5 | | 4 | 3 | | 5 | 22 | | 6 | 16 | | 7 | 5 | | 8 | 3 | | 9 | 6 | | 10 | 11 | | 11 | 23 | | 12 | 28 | | 13 | 5 | | 14 | 42 | | 15 | 2 | | 16 | 21 | | 17 | 12 | | 18 | 32 | | 19 | 2 | | 20 | 8 | | 21 | 6 | | 22 | 3 | | 23 | 30 | | 24 | 7 | | 25 | 4 | | 26 | 1 | | 27 | 4 | | 28 | 29 | | 29 | 1 | | 30 | 4 | | 31 | 30 | | 32 | 5 | | 33 | 4 | | 34 | 3 | | 35 | 4 | | 36 | 29 | | 37 | 7 | | 38 | 3 | | 39 | 6 | | 40 | 31 | | 41 | 6 | | 42 | 5 | | 43 | 1 | | 44 | 43 | | 45 | 2 | | 46 | 3 | | 47 | 2 | | 48 | 2 | | 49 | 3 |
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| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 192 | | matches | | 0 | "been knotted" | | 1 | "been disturbed" | | 2 | "been swapped" | | 3 | "been wiped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 216 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 337 | | ratio | 0.006 | | matches | | 0 | "The departure board still displayed HIGH BARNET — MINUS 4 MIN." | | 1 | "ARRIVAL — 2:17." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1353 | | adjectiveStacks | 1 | | stackExamples | | 0 | "sharp against bloodless cheeks." |
| | adverbCount | 24 | | adverbRatio | 0.017738359201773836 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0014781966001478197 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 337 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 337 | | mean | 7.07 | | std | 6.16 | | cv | 0.871 | | sampleLengths | | 0 | 19 | | 1 | 14 | | 2 | 12 | | 3 | 4 | | 4 | 11 | | 5 | 28 | | 6 | 6 | | 7 | 10 | | 8 | 12 | | 9 | 5 | | 10 | 2 | | 11 | 3 | | 12 | 3 | | 13 | 12 | | 14 | 10 | | 15 | 9 | | 16 | 7 | | 17 | 5 | | 18 | 3 | | 19 | 6 | | 20 | 11 | | 21 | 9 | | 22 | 14 | | 23 | 28 | | 24 | 5 | | 25 | 9 | | 26 | 2 | | 27 | 6 | | 28 | 9 | | 29 | 16 | | 30 | 2 | | 31 | 6 | | 32 | 15 | | 33 | 6 | | 34 | 6 | | 35 | 10 | | 36 | 4 | | 37 | 2 | | 38 | 2 | | 39 | 5 | | 40 | 9 | | 41 | 2 | | 42 | 8 | | 43 | 6 | | 44 | 3 | | 45 | 4 | | 46 | 26 | | 47 | 7 | | 48 | 4 | | 49 | 1 |
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| 58.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.36795252225519287 | | totalSentences | 337 | | uniqueOpeners | 124 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 164 | | matches | | 0 | "Instead of HIGH BARNET, it" | | 1 | "Then she faced Shah." |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 164 | | matches | | 0 | "His blue overshoes crinkled against" | | 1 | "He rested on his back," | | 2 | "His skin carried the waxy" | | 3 | "Her knees gave a small" | | 4 | "Its label read WIDOW’S SALT." | | 5 | "She had spent eighteen years" | | 6 | "They also gave murderers somewhere" | | 7 | "They crossed the platform." | | 8 | "She looked back at the" | | 9 | "She looked at Quinn’s warrant" | | 10 | "They returned to the body." | | 11 | "It had not been swapped" | | 12 | "She shifted her focus to" | | 13 | "She scrolled through the display." | | 14 | "She lowered herself beside the" | | 15 | "His nails carried black residue." | | 16 | "She looked towards the empty" | | 17 | "She touched neither." | | 18 | "She aimed the beam down." | | 19 | "He aimed his own torch." |
| | ratio | 0.152 | |
| 39.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 138 | | totalSentences | 164 | | matches | | 0 | "The dead man lay beneath" | | 1 | "Detective Harlow Quinn stopped at" | | 2 | "The cracked leather strap pressed" | | 3 | "Canvas awnings sagged over narrow" | | 4 | "Glass jars held teeth, feathers" | | 5 | "Another carried a handwritten sign:" | | 6 | "CHILDHOOD RATES POOR." | | 7 | "Detective Sergeant Imran Shah ducked" | | 8 | "His blue overshoes crinkled against" | | 9 | "Shah pointed towards a wooden" | | 10 | "A string of small bones" | | 11 | "Quinn studied the dead man." | | 12 | "He rested on his back," | | 13 | "His skin carried the waxy" | | 14 | "A narrow wound split his" | | 15 | "Shah led her closer" | | 16 | "Quinn crouched without touching the" | | 17 | "Her knees gave a small" | | 18 | "The man’s black shoes gleamed" | | 19 | "Both soles faced her." |
| | ratio | 0.841 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 164 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 2 | | matches | | 0 | "The dead man lay beneath a departure board that promised a train to High Barnet in minus four minutes." | | 1 | "Quinn glanced at the clean cut through the rails, the water that reflected too much, and the single wiped patch at the platform edge." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn continued (continue)" | | 1 | "she murmured (murmur)" | | 2 | "Shah muttered (mutter)" |
| | dialogueSentences | 157 | | tagDensity | 0.038 | | leniency | 0.076 | | rawRatio | 0.5 | | effectiveRatio | 0.038 | |