| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 94 | | tagDensity | 0.181 | | leniency | 0.362 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1962 | | totalAiIsmAdverbs | 2 | | 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) | |
| 79.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1962 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "silk" | | 1 | "quivered" | | 2 | "silence" | | 3 | "weight" | | 4 | "shattered" | | 5 | "measured" | | 6 | "trembled" |
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| 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 | 171 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 171 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 249 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1962 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 92 | | wordCount | 1313 | | uniqueNames | 13 | | maxNameDensity | 2.36 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 31 | | Market | 1 | | Sergeant | 1 | | Leonard | 1 | | Pike | 23 | | Anika | 1 | | Vale | 9 | | Bell | 13 | | Morrow | 6 | | One | 3 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Market" | | 4 | "Sergeant" | | 5 | "Leonard" | | 6 | "Pike" | | 7 | "Anika" | | 8 | "Vale" | | 9 | "Bell" | | 10 | "Morrow" |
| | places | | | globalScore | 0.319 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | 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 | 1962 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 249 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 164 | | mean | 11.96 | | std | 12.77 | | cv | 1.067 | | sampleLengths | | 0 | 89 | | 1 | 15 | | 2 | 5 | | 3 | 34 | | 4 | 41 | | 5 | 6 | | 6 | 11 | | 7 | 17 | | 8 | 4 | | 9 | 2 | | 10 | 36 | | 11 | 6 | | 12 | 10 | | 13 | 28 | | 14 | 23 | | 15 | 3 | | 16 | 37 | | 17 | 1 | | 18 | 6 | | 19 | 2 | | 20 | 4 | | 21 | 6 | | 22 | 14 | | 23 | 27 | | 24 | 30 | | 25 | 4 | | 26 | 15 | | 27 | 9 | | 28 | 7 | | 29 | 45 | | 30 | 3 | | 31 | 13 | | 32 | 9 | | 33 | 3 | | 34 | 1 | | 35 | 5 | | 36 | 1 | | 37 | 7 | | 38 | 1 | | 39 | 33 | | 40 | 9 | | 41 | 3 | | 42 | 2 | | 43 | 19 | | 44 | 5 | | 45 | 6 | | 46 | 21 | | 47 | 50 | | 48 | 10 | | 49 | 35 |
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| 99.11% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 171 | | matches | | 0 | "been folded" | | 1 | "been pressed" | | 2 | "been scraped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 215 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 249 | | ratio | 0.004 | | matches | | 0 | "The station sign had lost most of its letters; only AMDE clung to the curve above the ticket hall." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 945 | | adjectiveStacks | 1 | | stackExamples | | 0 | "North lay behind Quinn’s" |
| | adverbCount | 17 | | adverbRatio | 0.01798941798941799 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0021164021164021165 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 249 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 249 | | mean | 7.88 | | std | 5.4 | | cv | 0.685 | | sampleLengths | | 0 | 15 | | 1 | 5 | | 2 | 19 | | 3 | 17 | | 4 | 3 | | 5 | 4 | | 6 | 26 | | 7 | 15 | | 8 | 1 | | 9 | 2 | | 10 | 2 | | 11 | 13 | | 12 | 4 | | 13 | 17 | | 14 | 18 | | 15 | 7 | | 16 | 16 | | 17 | 6 | | 18 | 11 | | 19 | 17 | | 20 | 4 | | 21 | 2 | | 22 | 21 | | 23 | 8 | | 24 | 7 | | 25 | 6 | | 26 | 10 | | 27 | 9 | | 28 | 7 | | 29 | 12 | | 30 | 15 | | 31 | 8 | | 32 | 3 | | 33 | 28 | | 34 | 9 | | 35 | 1 | | 36 | 6 | | 37 | 2 | | 38 | 4 | | 39 | 6 | | 40 | 14 | | 41 | 27 | | 42 | 4 | | 43 | 14 | | 44 | 12 | | 45 | 4 | | 46 | 4 | | 47 | 11 | | 48 | 9 | | 49 | 7 |
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| 54.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3534136546184739 | | totalSentences | 249 | | uniqueOpeners | 88 | |
| 22.83% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 146 | | matches | | 0 | "Then it settled towards the" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 146 | | matches | | 0 | "Her leather watch strap pressed" | | 1 | "Her team had reached the" | | 2 | "He looked too clean for" | | 3 | "His shoes had picked up" | | 4 | "His fingers clasped a small" | | 5 | "It had darkened to the" | | 6 | "His lips had shrunk from" | | 7 | "She kept her gloves clear" | | 8 | "She kept her knees clear" | | 9 | "It ringed the body in" | | 10 | "His pocket square had been" | | 11 | "She leaned closer." | | 12 | "It had sunk into it." | | 13 | "Its needle quivered beneath the" | | 14 | "It opened its beak when" | | 15 | "His eyes, round and gold," | | 16 | "She led Pike beyond the" | | 17 | "Its line passed beneath Bell’s" | | 18 | "She crouched again." | | 19 | "His back remained clean except" |
| | ratio | 0.178 | |
| 69.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 114 | | totalSentences | 146 | | matches | | 0 | "The old Tube station lay" | | 1 | "Tile walls sweated black water." | | 2 | "The station sign had lost" | | 3 | "Jars of teeth." | | 4 | "Bottles filled with smoke." | | 5 | "A woman with a fox’s" | | 6 | "Detective Harlow Quinn ducked beneath" | | 7 | "Her leather watch strap pressed" | | 8 | "Her team had reached the" | | 9 | "Detective Sergeant Leonard Pike waited" | | 10 | "He looked too clean for" | | 11 | "His shoes had picked up" | | 12 | "Quinn looked at the constable" | | 13 | "Pike’s mouth pulled sideways." | | 14 | "The dead man lay on" | | 15 | "Someone had arranged his hands" | | 16 | "His fingers clasped a small" | | 17 | "It had darkened to the" | | 18 | "His lips had shrunk from" | | 19 | "Dr Anika Vale knelt near" |
| | ratio | 0.781 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 146 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "The platform dust held, stallholders’ shoes, bare feet, hoof marks" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn murmured (murmur)" | | 1 | "he growled (growl)" | | 2 | "he muttered (mutter)" |
| | dialogueSentences | 94 | | tagDensity | 0.138 | | leniency | 0.277 | | rawRatio | 0.231 | | effectiveRatio | 0.064 | |