| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 756 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "barely above a whisper" | | count | 1 |
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| | highlights | | 0 | "carefully" | | 1 | "barely above a whisper" |
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| 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) | |
| 27.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 756 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "echoed" | | 1 | "gloom" | | 2 | "whisper" | | 3 | "etched" | | 4 | "tracing" | | 5 | "eyebrow" | | 6 | "racing" | | 7 | "scanning" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
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| | highlights | | 0 | "eyes widened" | | 1 | "eyes narrowed" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 54 | | matches | | |
| 89.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 54 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 69 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 754 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 557 | | uniqueNames | 8 | | maxNameDensity | 2.51 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 2 | | Harlow | 1 | | Quinn | 14 | | Detective | 2 | | Sergeant | 1 | | Morris | 1 | | Eva | 12 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Kowalski" |
| | places | (empty) | | globalScore | 0.243 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | 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 | 754 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 69 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 26 | | std | 17.5 | | cv | 0.673 | | sampleLengths | | 0 | 66 | | 1 | 55 | | 2 | 54 | | 3 | 39 | | 4 | 49 | | 5 | 8 | | 6 | 24 | | 7 | 8 | | 8 | 56 | | 9 | 11 | | 10 | 17 | | 11 | 14 | | 12 | 22 | | 13 | 38 | | 14 | 6 | | 15 | 22 | | 16 | 5 | | 17 | 12 | | 18 | 17 | | 19 | 20 | | 20 | 42 | | 21 | 34 | | 22 | 16 | | 23 | 5 | | 24 | 24 | | 25 | 12 | | 26 | 39 | | 27 | 32 | | 28 | 7 |
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| 98.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 54 | | matches | | |
| 58.16% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 94 | | matches | | 0 | "was stepping" | | 1 | "was walking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 69 | | ratio | 0.014 | | matches | | 0 | "The ground was damp, the air thick with the scent of mildew and something else—something metallic and sharp." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 559 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.017889087656529516 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.007155635062611807 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 69 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 69 | | mean | 10.93 | | std | 6.28 | | cv | 0.575 | | sampleLengths | | 0 | 22 | | 1 | 19 | | 2 | 25 | | 3 | 9 | | 4 | 19 | | 5 | 16 | | 6 | 11 | | 7 | 11 | | 8 | 24 | | 9 | 19 | | 10 | 12 | | 11 | 18 | | 12 | 9 | | 13 | 14 | | 14 | 10 | | 15 | 11 | | 16 | 14 | | 17 | 8 | | 18 | 4 | | 19 | 20 | | 20 | 2 | | 21 | 6 | | 22 | 12 | | 23 | 18 | | 24 | 15 | | 25 | 11 | | 26 | 11 | | 27 | 6 | | 28 | 11 | | 29 | 4 | | 30 | 10 | | 31 | 4 | | 32 | 18 | | 33 | 7 | | 34 | 18 | | 35 | 13 | | 36 | 6 | | 37 | 8 | | 38 | 14 | | 39 | 3 | | 40 | 2 | | 41 | 2 | | 42 | 10 | | 43 | 8 | | 44 | 9 | | 45 | 8 | | 46 | 12 | | 47 | 12 | | 48 | 5 | | 49 | 12 |
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| 57.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.391304347826087 | | totalSentences | 69 | | uniqueOpeners | 27 | |
| 68.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 49 | | matches | | | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 49 | | matches | | 0 | "She was no cop, but" | | 1 | "His hands were clawed, as" | | 2 | "She tucked a curl of" | | 3 | "She examined the body, her" | | 4 | "She stood up, her torch" | | 5 | "She spotted a small brass" | | 6 | "She picked it up, her" | | 7 | "she asked, holding it up" | | 8 | "She walked over to the" | | 9 | "It was a circle with" | | 10 | "She turned to Eva, her" | | 11 | "She turned back to the" | | 12 | "She would find out what" | | 13 | "She would find the truth," |
| | ratio | 0.286 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 49 | | matches | | 0 | "The rain drummed against the" | | 1 | "Detective Harlow Quinn stepped carefully" | | 2 | "The beam of her torch" | | 3 | "Detective Sergeant Morris's replacement, Eva" | | 4 | "She was no cop, but" | | 5 | "Quinn trusted her instincts, even" | | 6 | "Quinn joined her, her sharp" | | 7 | "The beam of her torch" | | 8 | "His hands were clawed, as" | | 9 | "Eva said, her voice barely" | | 10 | "She tucked a curl of" | | 11 | "Quinn crouched down, her leather" | | 12 | "She examined the body, her" | | 13 | "The man's clothes were expensive," | | 14 | "Quinn asked, her voice steady" | | 15 | "Eva consulted her notepad." | | 16 | "She stood up, her torch" | | 17 | "The ground was damp, the" | | 18 | "She spotted a small brass" | | 19 | "She picked it up, her" |
| | ratio | 0.857 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 49 | | matches | | 0 | "Because that was her job." |
| | ratio | 0.02 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 2 | | matches | | 0 | "The rain drummed against the rusted metal roof of the abandoned Tube station, a steady rhythm that echoed through the cavernous space." | | 1 | "The beam of her torch cut through the gloom, illuminating the eerie graffiti that covered the walls, symbols and sigils that made her skin prickle." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva said, her voice barely above a whisper" | | 1 | "Quinn asked, her voice steady" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |