| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 78.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 946 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "carefully" | | 1 | "very" | | 2 | "quickly" |
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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) | |
| 15.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 946 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "silence" | | 1 | "oppressive" | | 2 | "shattered" | | 3 | "etched" | | 4 | "intricate" | | 5 | "racing" | | 6 | "tracing" | | 7 | "calculated" | | 8 | "quickened" | | 9 | "footsteps" | | 10 | "echoed" | | 11 | "eyebrow" | | 12 | "wavered" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 68 | | matches | (empty) | |
| 37.82% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 68 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 950 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 4.29% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 652 | | uniqueNames | 11 | | maxNameDensity | 2.91 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 19 | | Tube | 1 | | Constable | 1 | | Taylor | 7 | | Veil | 2 | | Market | 2 | | Morris | 1 | | Compass | 1 | | Kowalski | 1 | | Eva | 7 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Constable" | | 3 | "Taylor" | | 4 | "Market" | | 5 | "Morris" | | 6 | "Kowalski" | | 7 | "Eva" |
| | places | (empty) | | globalScore | 0.043 | | windowScore | 0.333 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like she was missing a crucial pie" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 950 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 83 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 30.65 | | std | 18.46 | | cv | 0.602 | | sampleLengths | | 0 | 81 | | 1 | 16 | | 2 | 69 | | 3 | 14 | | 4 | 35 | | 5 | 50 | | 6 | 8 | | 7 | 21 | | 8 | 39 | | 9 | 9 | | 10 | 18 | | 11 | 60 | | 12 | 45 | | 13 | 22 | | 14 | 10 | | 15 | 42 | | 16 | 48 | | 17 | 26 | | 18 | 18 | | 19 | 10 | | 20 | 47 | | 21 | 11 | | 22 | 34 | | 23 | 13 | | 24 | 47 | | 25 | 16 | | 26 | 27 | | 27 | 22 | | 28 | 19 | | 29 | 39 | | 30 | 34 |
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| 89.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 68 | | matches | | 0 | "was bound" | | 1 | "being pulled" | | 2 | "was etched" |
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| 79.88% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 111 | | matches | | 0 | "was missing" | | 1 | "was racing" |
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| 39.59% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 83 | | ratio | 0.036 | | matches | | 0 | "This wasn't the usual crime scene – no sirens, no curious onlookers, no brightly colored tape to cordon off the area." | | 1 | "The face was etched with protective sigils, the needle pointing toward the nearest supernatural rift or portal – a Veil Compass." | | 2 | "She had a feeling that this case was about to get a lot more complicated – and a lot more deadly." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 651 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.03840245775729647 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009216589861751152 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 11.45 | | std | 8.11 | | cv | 0.709 | | sampleLengths | | 0 | 29 | | 1 | 14 | | 2 | 21 | | 3 | 17 | | 4 | 10 | | 5 | 6 | | 6 | 29 | | 7 | 20 | | 8 | 18 | | 9 | 2 | | 10 | 14 | | 11 | 13 | | 12 | 22 | | 13 | 5 | | 14 | 18 | | 15 | 3 | | 16 | 5 | | 17 | 11 | | 18 | 7 | | 19 | 1 | | 20 | 8 | | 21 | 2 | | 22 | 19 | | 23 | 16 | | 24 | 12 | | 25 | 11 | | 26 | 9 | | 27 | 2 | | 28 | 16 | | 29 | 3 | | 30 | 8 | | 31 | 2 | | 32 | 1 | | 33 | 18 | | 34 | 10 | | 35 | 8 | | 36 | 9 | | 37 | 1 | | 38 | 23 | | 39 | 11 | | 40 | 11 | | 41 | 22 | | 42 | 7 | | 43 | 3 | | 44 | 9 | | 45 | 4 | | 46 | 10 | | 47 | 7 | | 48 | 12 | | 49 | 21 |
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| 60.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3855421686746988 | | totalSentences | 83 | | uniqueOpeners | 32 | |
| 56.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 59 | | matches | | 0 | "Just a lone police officer," |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 59 | | matches | | 0 | "She spotted a faint symbol" | | 1 | "He gestured to the body" | | 2 | "She knelt beside the body," | | 3 | "It felt warm to the" | | 4 | "She pulled out her notebook" | | 5 | "She pushed the memories aside," | | 6 | "it all seemed too deliberate," | | 7 | "She felt like she was" | | 8 | "She tucked the information away," | | 9 | "She had a feeling that" |
| | ratio | 0.169 | |
| 27.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 59 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The air was thick with" | | 2 | "This wasn't the usual crime" | | 3 | "the officer, Constable Taylor, said," | | 4 | "Quinn's gaze swept the platform," | | 5 | "The Veil Market, an underground" | | 6 | "She spotted a faint symbol" | | 7 | "Quinn asked, her voice crisp," | | 8 | "Taylor, a lanky man in" | | 9 | "He gestured to the body" | | 10 | "Quinn's eyes followed the gesture." | | 11 | "The woman's wrist was bound" | | 12 | "Quinn's gut twisted." | | 13 | "This was no ordinary murder." | | 14 | "She knelt beside the body," | | 15 | "It felt warm to the" | | 16 | "Quinn asked, her mind racing" | | 17 | "Quinn's gaze snapped to the" | | 18 | "The tears were small, scattered" | | 19 | "Quinn asked, her voice neutral" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "Quinn's gaze snapped to the body, her eyes tracing the jagged edges of the victim's clothing." | | 1 | "As she worked, Quinn's thoughts drifted back to her partner, DS Morris, and the case that had ended in tragedy three years ago." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 4 | | matches | | 0 | "Quinn asked, her voice crisp, as she approached the body" | | 1 | "Quinn asked, her mind racing" | | 2 | "Quinn asked, her voice neutral" | | 3 | "Quinn asked, her tone neutral" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.238 | | leniency | 0.476 | | rawRatio | 0 | | effectiveRatio | 0 | |