| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.163 | | leniency | 0.327 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1168 | | 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) | |
| 87.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1168 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "anticipation" | | 1 | "perfect" | | 2 | "weight" |
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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 | 1 | | narrationSentences | 91 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1168 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 886 | | uniqueNames | 11 | | maxNameDensity | 0.9 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 8 | | Victorian | 1 | | Bethnal | 1 | | Green | 1 | | Nash | 8 | | Thatcher | 1 | | Number | 1 | | Ten | 1 | | Fincham | 3 | | Morris | 3 | | Yard | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Nash" | | 2 | "Thatcher" | | 3 | "Fincham" | | 4 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | 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 | 1168 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 132 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 18.25 | | std | 22.34 | | cv | 1.224 | | sampleLengths | | 0 | 51 | | 1 | 49 | | 2 | 5 | | 3 | 40 | | 4 | 4 | | 5 | 5 | | 6 | 47 | | 7 | 79 | | 8 | 12 | | 9 | 7 | | 10 | 33 | | 11 | 5 | | 12 | 21 | | 13 | 3 | | 14 | 2 | | 15 | 65 | | 16 | 3 | | 17 | 1 | | 18 | 2 | | 19 | 2 | | 20 | 2 | | 21 | 19 | | 22 | 1 | | 23 | 5 | | 24 | 7 | | 25 | 89 | | 26 | 1 | | 27 | 3 | | 28 | 7 | | 29 | 1 | | 30 | 1 | | 31 | 11 | | 32 | 3 | | 33 | 3 | | 34 | 24 | | 35 | 16 | | 36 | 71 | | 37 | 5 | | 38 | 4 | | 39 | 29 | | 40 | 8 | | 41 | 22 | | 42 | 6 | | 43 | 1 | | 44 | 8 | | 45 | 3 | | 46 | 62 | | 47 | 77 | | 48 | 5 | | 49 | 5 |
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| 89.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 91 | | matches | | 0 | "been opened" | | 1 | "been swept" | | 2 | "been found" | | 3 | "was scratched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 135 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 132 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 895 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.024581005586592177 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0033519553072625698 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 8.85 | | std | 8.25 | | cv | 0.933 | | sampleLengths | | 0 | 21 | | 1 | 2 | | 2 | 5 | | 3 | 23 | | 4 | 33 | | 5 | 16 | | 6 | 5 | | 7 | 5 | | 8 | 7 | | 9 | 6 | | 10 | 19 | | 11 | 3 | | 12 | 4 | | 13 | 5 | | 14 | 12 | | 15 | 12 | | 16 | 12 | | 17 | 11 | | 18 | 2 | | 19 | 3 | | 20 | 17 | | 21 | 18 | | 22 | 1 | | 23 | 7 | | 24 | 3 | | 25 | 3 | | 26 | 25 | | 27 | 12 | | 28 | 7 | | 29 | 4 | | 30 | 29 | | 31 | 5 | | 32 | 21 | | 33 | 3 | | 34 | 2 | | 35 | 7 | | 36 | 13 | | 37 | 6 | | 38 | 12 | | 39 | 5 | | 40 | 9 | | 41 | 13 | | 42 | 3 | | 43 | 1 | | 44 | 2 | | 45 | 2 | | 46 | 2 | | 47 | 8 | | 48 | 3 | | 49 | 5 |
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| 75.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.5 | | totalSentences | 132 | | uniqueOpeners | 66 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 77 | | matches | | 0 | "Then at the wall behind" | | 1 | "Then at the ceiling, which" | | 2 | "Then she tilted the torch" |
| | ratio | 0.039 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 77 | | matches | | 0 | "His pen slowed" | | 1 | "He looked at the wall" | | 2 | "She rolled the word around" | | 3 | "Her knees clicked." | | 4 | "She paced the perimeter of" | | 5 | "She crouched again, slower this" | | 6 | "His nostrils worked." | | 7 | "His eyes went to hers." | | 8 | "She lifted the dead man's" | | 9 | "She turned the palm up." | | 10 | "He peered closer." | | 11 | "She let him, this time." | | 12 | "She shifted the torch." | | 13 | "She shifted it back." | | 14 | "She watched his face do" | | 15 | "She didn't know, yet, what" | | 16 | "She just wanted the weight" | | 17 | "She'd kept the file in" | | 18 | "She'd read it, on bad" | | 19 | "She looked at Fincham's peaceful" |
| | ratio | 0.299 | |
| 96.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 77 | | matches | | 0 | "The body sat cross-legged in" | | 1 | "DS Nash crouched on the" | | 2 | "His pen slowed" | | 3 | "He looked at the wall" | | 4 | "She rolled the word around" | | 5 | "The bricked-up doorway had a" | | 6 | "The mortar between the bricks" | | 7 | "Nobody had touched that wall" | | 8 | "Her knees clicked." | | 9 | "She paced the perimeter of" | | 10 | "The hatch the landlord swore" | | 11 | "Nash flipped his notebook." | | 12 | "Quinn came back round to" | | 13 | "She crouched again, slower this" | | 14 | "That was the first wrong" | | 15 | "A corpse three days gone" | | 16 | "This one smelled of nothing." | | 17 | "Nothing at all, as if" | | 18 | "Nash leaned in, flinched in" | | 19 | "His nostrils worked." |
| | ratio | 0.727 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "She just wanted the weight of something professional in her hand, because her palms had started to sweat, and sweat in a sealed cellar with a dead man who didn'…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.061 | | leniency | 0.122 | | rawRatio | 0 | | effectiveRatio | 0 | |