| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1575 | | 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) | |
| 80.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1575 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "silence" | | 1 | "flicked" | | 2 | "trembled" | | 3 | "pulsed" | | 4 | "footsteps" | | 5 | "echoed" |
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| 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 | 162 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 162 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 201 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1575 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1372 | | uniqueNames | 8 | | maxNameDensity | 2.62 | | worstName | "Quinn" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 36 | | Raven | 1 | | Nest | 1 | | Metropolitan | 1 | | Police | 1 | | Underground | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Nest" | | 3 | "Police" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Metropolitan" |
| | globalScore | 0.188 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | 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 | 1575 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 201 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 135 | | mean | 11.67 | | std | 13.1 | | cv | 1.123 | | sampleLengths | | 0 | 12 | | 1 | 53 | | 2 | 5 | | 3 | 2 | | 4 | 4 | | 5 | 35 | | 6 | 23 | | 7 | 4 | | 8 | 57 | | 9 | 5 | | 10 | 28 | | 11 | 3 | | 12 | 7 | | 13 | 25 | | 14 | 4 | | 15 | 5 | | 16 | 3 | | 17 | 41 | | 18 | 10 | | 19 | 27 | | 20 | 20 | | 21 | 4 | | 22 | 5 | | 23 | 2 | | 24 | 5 | | 25 | 3 | | 26 | 43 | | 27 | 7 | | 28 | 18 | | 29 | 4 | | 30 | 6 | | 31 | 13 | | 32 | 8 | | 33 | 3 | | 34 | 2 | | 35 | 7 | | 36 | 6 | | 37 | 10 | | 38 | 15 | | 39 | 7 | | 40 | 44 | | 41 | 6 | | 42 | 5 | | 43 | 7 | | 44 | 3 | | 45 | 4 | | 46 | 8 | | 47 | 6 | | 48 | 1 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 162 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 233 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 201 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1373 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.014566642388929352 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0007283321194464676 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 201 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 201 | | mean | 7.84 | | std | 4.99 | | cv | 0.637 | | sampleLengths | | 0 | 12 | | 1 | 16 | | 2 | 23 | | 3 | 14 | | 4 | 5 | | 5 | 2 | | 6 | 4 | | 7 | 23 | | 8 | 12 | | 9 | 8 | | 10 | 4 | | 11 | 11 | | 12 | 4 | | 13 | 10 | | 14 | 17 | | 15 | 13 | | 16 | 5 | | 17 | 12 | | 18 | 5 | | 19 | 5 | | 20 | 5 | | 21 | 18 | | 22 | 3 | | 23 | 7 | | 24 | 6 | | 25 | 3 | | 26 | 16 | | 27 | 4 | | 28 | 5 | | 29 | 3 | | 30 | 17 | | 31 | 10 | | 32 | 6 | | 33 | 8 | | 34 | 10 | | 35 | 7 | | 36 | 13 | | 37 | 7 | | 38 | 20 | | 39 | 4 | | 40 | 5 | | 41 | 2 | | 42 | 5 | | 43 | 3 | | 44 | 7 | | 45 | 14 | | 46 | 7 | | 47 | 15 | | 48 | 7 | | 49 | 5 |
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| 51.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.34328358208955223 | | totalSentences | 201 | | uniqueOpeners | 69 | |
| 63.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 158 | | matches | | 0 | "Then the suspect vaulted a" | | 1 | "Only a low crackle answered." | | 2 | "Then the market unfolded before" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 158 | | matches | | 0 | "Its tyres threw a sheet" | | 1 | "She did not slow." | | 2 | "Her leather watch knocked against" | | 3 | "She raised her torch, caught" | | 4 | "It stood ajar." | | 5 | "She drew her pistol." | | 6 | "She descended with her weapon" | | 7 | "She pressed the button again." | | 8 | "Their coat hung open now." | | 9 | "She turned her head by" | | 10 | "Their faces remained hidden beneath" | | 11 | "She fired into the ceiling." | | 12 | "They descended again." | | 13 | "It occupied the abandoned station’s" | | 14 | "He wore a suit stitched" | | 15 | "His gaze flicked to her" | | 16 | "He rubbed his thumb against" | | 17 | "She had cash, a warrant" | | 18 | "It looked like a knucklebone." | | 19 | "His smile faltered." |
| | ratio | 0.177 | |
| 29.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 136 | | totalSentences | 158 | | matches | | 0 | "Detective Harlow Quinn kept the" | | 1 | "A narrow figure in a" | | 2 | "Quinn opened the van door." | | 3 | "The figure glanced back." | | 4 | "That single movement gave Quinn" | | 5 | "Quinn crossed the road without" | | 6 | "A taxi horn blared." | | 7 | "Its tyres threw a sheet" | | 8 | "She did not slow." | | 9 | "Her leather watch knocked against" | | 10 | "The rain had soaked through" | | 11 | "Panic ran in straight lines." | | 12 | "This person ran like they" | | 13 | "Quinn cut through an alley." | | 14 | "A bin toppled behind her." | | 15 | "Glass scattered over the pavement." | | 16 | "She raised her torch, caught" | | 17 | "The suspect ducked into a" | | 18 | "Quinn reached the door seconds" | | 19 | "It stood ajar." |
| | ratio | 0.861 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 158 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 1 | | matches | | 0 | "The ticket hall stretched in every direction, packed with strangers who had witnessed her arrival and decided not to react." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "he murmured (murmur)" | | 1 | "she demanded (demand)" |
| | dialogueSentences | 42 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0.667 | | effectiveRatio | 0.095 | |