| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.556 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1364 | | 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) | |
| 52.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1364 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "silence" | | 1 | "profound" | | 2 | "perfect" | | 3 | "chaotic" | | 4 | "gloom" | | 5 | "familiar" | | 6 | "tracing" | | 7 | "scanning" | | 8 | "glint" | | 9 | "etched" | | 10 | "intricate" | | 11 | "unwavering" |
| |
| 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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1357 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1032 | | uniqueNames | 14 | | maxNameDensity | 1.26 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Camden | 1 | | Town | 1 | | Tube | 1 | | Harlow | 13 | | Quinn | 3 | | John | 1 | | Doe | 1 | | Rorschach | 1 | | London | 1 | | Kowalski | 1 | | Eva | 11 | | Aurora | 1 | | Morris | 3 | | Detective | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "John" | | 3 | "Doe" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Aurora" | | 7 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Tube" | | 3 | "London" |
| | globalScore | 0.87 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 52.62% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.474 | | wordCount | 1357 | | matches | | 0 | "not to a how, but to a what" | | 1 | "no choice but" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 105 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 48.46 | | std | 24.83 | | cv | 0.512 | | sampleLengths | | 0 | 66 | | 1 | 82 | | 2 | 25 | | 3 | 71 | | 4 | 35 | | 5 | 2 | | 6 | 56 | | 7 | 41 | | 8 | 45 | | 9 | 23 | | 10 | 27 | | 11 | 74 | | 12 | 49 | | 13 | 68 | | 14 | 56 | | 15 | 86 | | 16 | 10 | | 17 | 49 | | 18 | 9 | | 19 | 72 | | 20 | 54 | | 21 | 75 | | 22 | 51 | | 23 | 20 | | 24 | 9 | | 25 | 92 | | 26 | 52 | | 27 | 58 |
| |
| 94.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 95 | | matches | | 0 | "was sprayed" | | 1 | "was etched" | | 2 | "been *pulled" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 158 | | matches | | 0 | "was already losing" | | 1 | "was tucking" | | 2 | "wasn’t pointing" | | 3 | "was pointing" | | 4 | "was getting" | | 5 | "was pointing" | | 6 | "was pointing" |
| |
| 34.01% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 105 | | ratio | 0.038 | | matches | | 0 | "The blood wasn’t pooled; it was sprayed." | | 1 | "She liked Eva well enough—Aurora’s bookish, earnest friend—but her presence at a crime scene was wrong." | | 2 | "The mundane world of forensics—of fibres, fingerprints, and time of death—felt suddenly, absurdly thin." | | 3 | "The lack of signs of entry for the victim—because he hadn’t walked here." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 690 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.021739130434782608 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004347826086956522 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 12.92 | | std | 9.64 | | cv | 0.746 | | sampleLengths | | 0 | 28 | | 1 | 19 | | 2 | 19 | | 3 | 18 | | 4 | 7 | | 5 | 15 | | 6 | 5 | | 7 | 27 | | 8 | 10 | | 9 | 18 | | 10 | 7 | | 11 | 5 | | 12 | 4 | | 13 | 7 | | 14 | 18 | | 15 | 12 | | 16 | 8 | | 17 | 10 | | 18 | 7 | | 19 | 6 | | 20 | 10 | | 21 | 2 | | 22 | 4 | | 23 | 13 | | 24 | 2 | | 25 | 11 | | 26 | 20 | | 27 | 25 | | 28 | 12 | | 29 | 16 | | 30 | 13 | | 31 | 29 | | 32 | 16 | | 33 | 3 | | 34 | 20 | | 35 | 17 | | 36 | 6 | | 37 | 4 | | 38 | 10 | | 39 | 10 | | 40 | 15 | | 41 | 13 | | 42 | 9 | | 43 | 17 | | 44 | 4 | | 45 | 27 | | 46 | 18 | | 47 | 3 | | 48 | 8 | | 49 | 5 |
| |
| 46.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3238095238095238 | | totalSentences | 105 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 90 | | matches | | 0 | "She’d already catalogued it." | | 1 | "she muttered, more to herself" | | 2 | "It was the forensic equivalent" | | 3 | "Her red curls were a" | | 4 | "She liked Eva well enough—Aurora’s" | | 5 | "She hesitated, then lifted her" | | 6 | "She didn’t look at the" | | 7 | "She looked at the clean" | | 8 | "She looked at the pillars," | | 9 | "She pointed a trembling finger" | | 10 | "It reminded her of Morris." | | 11 | "Her gaze snagged on a" | | 12 | "She knelt, the worn leather" | | 13 | "Its face was etched with" | | 14 | "They were sigils." | | 15 | "It was pointing, with quiet," | | 16 | "She could see a faint," | | 17 | "She placed it carefully into" | | 18 | "It kept pointing at the" | | 19 | "she asked, her voice dangerously" |
| | ratio | 0.4 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 90 | | matches | | 0 | "The air in the abandoned" | | 1 | "Harlow ducked under the tape," | | 2 | "The concrete was gritty under" | | 3 | "This one was different, though." | | 4 | "The victim, a John Doe," | | 5 | "Harlow didn’t need to look." | | 6 | "She’d already catalogued it." | | 7 | "The blood wasn’t pooled; it" | | 8 | "A violent Rorschach test fanning" | | 9 | "There, directly beneath the victim’s" | | 10 | "A perfect, one-foot circle of" | | 11 | "she muttered, more to herself" | | 12 | "It was the forensic equivalent" | | 13 | "The voice, laced with a" | | 14 | "Eva Kowalski stood at the" | | 15 | "Her red curls were a" | | 16 | "Harlow’s tone was flat, a" | | 17 | "She liked Eva well enough—Aurora’s" | | 18 | "A detail that did not" | | 19 | "Eva’s green eyes, magnified by" |
| | ratio | 0.833 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 1 | | matches | | 0 | "Its face was etched with intricate, spiralling lines that weren’t numbers or cardinal directions." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 4 | | matches | | 0 | "she muttered, more to herself" | | 1 | "Harlow said, her eyes scanning the periphery" | | 2 | "she asked, her voice dangerously calm" | | 3 | "She swallowed, her freckled complexion ashen" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 7 | | fancyTags | | 0 | "said a uniformed constable (say uniform)" | | 1 | "she muttered (mutter)" | | 2 | "a scholar correcting (correct)" | | 3 | "Eva whispered (whisper)" | | 4 | "Harlow repeated (repeat)" | | 5 | "Eva breathed (breathe)" | | 6 | "Harlow interrupted (interrupt)" |
| | dialogueSentences | 27 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.778 | | effectiveRatio | 0.519 | |