| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 1 | | adverbTags | | 0 | "Miller read aloud [aloud]" |
| | dialogueSentences | 61 | | tagDensity | 0.393 | | leniency | 0.787 | | rawRatio | 0.042 | | effectiveRatio | 0.033 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1467 | | 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) | |
| 65.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1467 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "chill" | | 1 | "standard" | | 2 | "echo" | | 3 | "pristine" | | 4 | "scanning" | | 5 | "echoed" | | 6 | "footsteps" | | 7 | "glint" | | 8 | "navigated" |
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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 | 92 | | matches | | |
| 96.27% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 92 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1467 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 1 | | matches | | 0 | "Then, it snapped ninety degrees to the left, pointing directly at the cleared circle of white tiles on the wall." |
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| 27.53% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 939 | | uniqueNames | 15 | | maxNameDensity | 2.45 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 23 | | Quinn | 1 | | Camden | 1 | | Tube | 1 | | Morris | 2 | | Constable | 1 | | Miller | 10 | | Victorian | 1 | | Thames | 1 | | Whitechapel | 1 | | Bovril | 1 | | West | 1 | | End | 1 | | Oxford | 1 | | Eva | 7 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Constable" | | 4 | "Miller" | | 5 | "Eva" |
| | places | | 0 | "Thames" | | 1 | "Whitechapel" | | 2 | "West" | | 3 | "End" | | 4 | "Oxford" |
| | globalScore | 0.275 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 1467 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 24.45 | | std | 18.24 | | cv | 0.746 | | sampleLengths | | 0 | 19 | | 1 | 77 | | 2 | 44 | | 3 | 4 | | 4 | 12 | | 5 | 30 | | 6 | 7 | | 7 | 36 | | 8 | 3 | | 9 | 57 | | 10 | 37 | | 11 | 21 | | 12 | 24 | | 13 | 53 | | 14 | 8 | | 15 | 4 | | 16 | 20 | | 17 | 12 | | 18 | 12 | | 19 | 49 | | 20 | 16 | | 21 | 38 | | 22 | 8 | | 23 | 22 | | 24 | 20 | | 25 | 18 | | 26 | 62 | | 27 | 22 | | 28 | 29 | | 29 | 49 | | 30 | 18 | | 31 | 17 | | 32 | 2 | | 33 | 4 | | 34 | 7 | | 35 | 22 | | 36 | 3 | | 37 | 4 | | 38 | 4 | | 39 | 32 | | 40 | 1 | | 41 | 15 | | 42 | 40 | | 43 | 48 | | 44 | 21 | | 45 | 36 | | 46 | 53 | | 47 | 10 | | 48 | 49 | | 49 | 21 |
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| 97.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 92 | | matches | | 0 | "were rusted" | | 1 | "been wiped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 166 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 129 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 946 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.0200845665961945 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004228329809725159 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 11.37 | | std | 7.41 | | cv | 0.651 | | sampleLengths | | 0 | 19 | | 1 | 20 | | 2 | 10 | | 3 | 21 | | 4 | 3 | | 5 | 23 | | 6 | 17 | | 7 | 27 | | 8 | 4 | | 9 | 12 | | 10 | 20 | | 11 | 10 | | 12 | 7 | | 13 | 3 | | 14 | 33 | | 15 | 3 | | 16 | 22 | | 17 | 10 | | 18 | 12 | | 19 | 3 | | 20 | 10 | | 21 | 7 | | 22 | 30 | | 23 | 3 | | 24 | 18 | | 25 | 24 | | 26 | 15 | | 27 | 15 | | 28 | 7 | | 29 | 1 | | 30 | 1 | | 31 | 2 | | 32 | 12 | | 33 | 8 | | 34 | 4 | | 35 | 20 | | 36 | 12 | | 37 | 12 | | 38 | 3 | | 39 | 14 | | 40 | 19 | | 41 | 13 | | 42 | 2 | | 43 | 14 | | 44 | 8 | | 45 | 6 | | 46 | 2 | | 47 | 7 | | 48 | 15 | | 49 | 8 |
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| 79.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5116279069767442 | | totalSentences | 129 | | uniqueOpeners | 66 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | 0 | "Then, it snapped ninety degrees" |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 78 | | matches | | 0 | "She shone her torch over" | | 1 | "She had seen this exact" | | 2 | "She checked the worn leather" | | 3 | "Her jaw tightened." | | 4 | "She paced the length of" | | 5 | "She ran her gloved fingers" | | 6 | "They clipped against the concrete," | | 7 | "She stood five-foot-four, her curly" | | 8 | "She pushed a pair of" | | 9 | "Her other hand dropped into" | | 10 | "Her voice carried the crisp" | | 11 | "Her left hand rose, tucking" | | 12 | "She tucked the same curl" | | 13 | "She stopped two feet away," | | 14 | "She looked past Harlow toward" | | 15 | "Her shoulders dropped an inch." | | 16 | "She reached into her wool" | | 17 | "It spun in frantic, erratic" | | 18 | "She tilted the small brass" | | 19 | "It pointed dead ahead at" |
| | ratio | 0.256 | |
| 11.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 78 | | matches | | 0 | "The victim's pocket watch had" | | 1 | "Detective Harlow Quinn crouched beside" | | 2 | "She shone her torch over" | | 3 | "A standard forensic team would" | | 4 | "Harlow knew better." | | 5 | "She had seen this exact" | | 6 | "Detective Constable Miller said, chewing" | | 7 | "Harlow tapped the dead man's" | | 8 | "Miller leaned in, his torch" | | 9 | "Harlow stood, her spine straightening" | | 10 | "She checked the worn leather" | | 11 | "The second hand ticked backward," | | 12 | "Her jaw tightened." | | 13 | "Miller swept his light across" | | 14 | "Harlow walked to the edge" | | 15 | "The tracks below were rusted" | | 16 | "The exact smell of the" | | 17 | "Harlow ignored him." | | 18 | "She paced the length of" | | 19 | "Vintage posters for Bovril and" |
| | ratio | 0.897 | |
| 64.10% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 78 | | matches | | 0 | "Yet, beneath the rot, another" |
| | ratio | 0.013 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 4 | | matches | | 0 | "Thick grey dust coated the concrete, undisturbed save for their own heavy police issue boots and the single, frantic path made by the urban explorer who had cal…" | | 1 | "Above them, Victorian brickwork wept black moisture that smelled of copper and stagnant Thames water." | | 2 | "Harlow drew her baton, thumbing her torch off, and stepped into the shadow of a rusted vending kiosk." | | 3 | "A worn leather satchel bulged against her hip, weighed down by thick volumes that knocked against her thigh as she walked." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow stood, her spine straightening with military precision" |
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| 68.03% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 5 | | fancyTags | | 0 | "Miller muttered (mutter)" | | 1 | "Miller barked (bark)" | | 2 | "Harlow ordered (order)" | | 3 | "Eva whispered (whisper)" | | 4 | "Harlow demanded (demand)" |
| | dialogueSentences | 61 | | tagDensity | 0.197 | | leniency | 0.393 | | rawRatio | 0.417 | | effectiveRatio | 0.164 | |