| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.04 | | leniency | 0.08 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1134 | | 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) | |
| 64.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1134 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "silk" | | 1 | "chill" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "trembled" | | 5 | "intricate" | | 6 | "silence" | | 7 | "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 | 0 | | narrationSentences | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | 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 | 1 | | markdownWords | 1 | | totalWords | 1134 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.78% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 949 | | uniqueNames | 9 | | maxNameDensity | 1.26 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Sergeant | 1 | | Miller | 6 | | Victorian | 1 | | Rotherhithe | 1 | | Morris | 1 | | Harlow | 12 | | Eva | 3 | | Thames | 1 |
| | persons | | 0 | "Sergeant" | | 1 | "Miller" | | 2 | "Morris" | | 3 | "Harlow" | | 4 | "Eva" |
| | places | | | globalScore | 0.868 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | 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 | 1134 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 106 | | matches | | 0 | "filled that Rotherhithe" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 21.81 | | std | 20.04 | | cv | 0.919 | | sampleLengths | | 0 | 32 | | 1 | 25 | | 2 | 3 | | 3 | 2 | | 4 | 12 | | 5 | 7 | | 6 | 6 | | 7 | 62 | | 8 | 5 | | 9 | 81 | | 10 | 64 | | 11 | 57 | | 12 | 6 | | 13 | 9 | | 14 | 9 | | 15 | 5 | | 16 | 23 | | 17 | 34 | | 18 | 33 | | 19 | 26 | | 20 | 25 | | 21 | 59 | | 22 | 11 | | 23 | 7 | | 24 | 9 | | 25 | 12 | | 26 | 8 | | 27 | 46 | | 28 | 4 | | 29 | 61 | | 30 | 12 | | 31 | 9 | | 32 | 5 | | 33 | 5 | | 34 | 1 | | 35 | 47 | | 36 | 13 | | 37 | 35 | | 38 | 9 | | 39 | 50 | | 40 | 29 | | 41 | 5 | | 42 | 11 | | 43 | 6 | | 44 | 5 | | 45 | 13 | | 46 | 37 | | 47 | 29 | | 48 | 6 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 153 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 106 | | ratio | 0.009 | | matches | | 0 | "The flesh beneath wasn't blistered red; it sat sunken, waxy, and blue-grey, hard as butcher's meat left in a deep freeze." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 959 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.03232533889468196 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009384775808133473 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 10.7 | | std | 7.12 | | cv | 0.665 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 13 | | 3 | 12 | | 4 | 3 | | 5 | 2 | | 6 | 12 | | 7 | 7 | | 8 | 6 | | 9 | 8 | | 10 | 15 | | 11 | 14 | | 12 | 2 | | 13 | 2 | | 14 | 21 | | 15 | 5 | | 16 | 13 | | 17 | 19 | | 18 | 19 | | 19 | 15 | | 20 | 3 | | 21 | 12 | | 22 | 9 | | 23 | 10 | | 24 | 45 | | 25 | 9 | | 26 | 22 | | 27 | 5 | | 28 | 21 | | 29 | 6 | | 30 | 9 | | 31 | 9 | | 32 | 5 | | 33 | 23 | | 34 | 3 | | 35 | 14 | | 36 | 17 | | 37 | 10 | | 38 | 10 | | 39 | 13 | | 40 | 1 | | 41 | 15 | | 42 | 3 | | 43 | 7 | | 44 | 10 | | 45 | 15 | | 46 | 15 | | 47 | 11 | | 48 | 33 | | 49 | 11 |
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| 94.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.6226415094339622 | | totalSentences | 106 | | uniqueOpeners | 66 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 74 | | matches | | 0 | "Further down the platform, strange" | | 1 | "Too dense, the marrow pores" | | 2 | "Instead of north, south, east," | | 3 | "Then, without warning, the needle" |
| | ratio | 0.054 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 74 | | matches | | 0 | "He gestured toward the dead" | | 1 | "His leather brogues gleamed, untouched" | | 2 | "She tilted her head, letting" | | 3 | "She checked the worn leather" | | 4 | "She walked the edge of" | | 5 | "He jammed his hands into" | | 6 | "He wanted a quick file," | | 7 | "She pulled tweezers from her" | | 8 | "She bagged the disc and" | | 9 | "She clutched a worn leather" | | 10 | "Her fingers trembled, nails bitten" | | 11 | "Her gaze dropped to the" | | 12 | "She gestured toward the body" | | 13 | "She watched Eva instead." | | 14 | "Her round glasses pointed straight" | | 15 | "Her leather satchel bumped against" | | 16 | "She tucked another curl behind" | | 17 | "It spun once, violent and" |
| | ratio | 0.243 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 74 | | matches | | 0 | "Harlow ducked beneath the rusted" | | 1 | "Detective Sergeant Miller stood over" | | 2 | "He gestured toward the dead" | | 3 | "Harlow ignored him and crouched" | | 4 | "His leather brogues gleamed, untouched" | | 5 | "Muggers didn't leave platinum behind." | | 6 | "She tilted her head, letting" | | 7 | "Dampness wept through the Victorian" | | 8 | "Harlow rose, her knees popping" | | 9 | "She checked the worn leather" | | 10 | "She walked the edge of" | | 11 | "Dust lay thick on the" | | 12 | "Stalls had stood here recently." | | 13 | "Miller stopped chewing." | | 14 | "He jammed his hands into" | | 15 | "He wanted a quick file," | | 16 | "Harlow crouched beside a drainage" | | 17 | "Something pale wedged between the" | | 18 | "She pulled tweezers from her" | | 19 | "She bagged the disc and" |
| | ratio | 0.716 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 4 | | matches | | 0 | "Male, mid-thirties, wearing a bespoke charcoal wool overcoat that cost more than her monthly rent." | | 1 | "Dampness wept through the Victorian brickwork, feeding patches of black mould that smelled of stagnant river water and copper." | | 2 | "Four minutes since she breached the perimeter, and the air already tasted wrong, metallic and sharp against her teeth, the exact same static tang that filled th…" | | 3 | "Miller shifted his weight, glancing between the detective and the museum girl, his hand drifting instinctively toward the baton on his belt." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |