| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 937 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 937 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "mosaic" | | 1 | "reminder" | | 2 | "glint" | | 3 | "scanned" | | 4 | "jaw clenched" | | 5 | "echo" | | 6 | "footsteps" | | 7 | "weight" | | 8 | "gloom" | | 9 | "echoing" | | 10 | "intricate" | | 11 | "silence" | | 12 | "whisper" | | 13 | "tension" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 73 | | matches | | |
| 64.58% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 73 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 932 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 856 | | uniqueNames | 11 | | maxNameDensity | 0.58 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 5 | | Raven | 2 | | Nest | 2 | | Morris | 2 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Morris" | | 5 | "Market" | | 6 | "Herrera" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like human skin" |
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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 | 932 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 24.53 | | std | 21.41 | | cv | 0.873 | | sampleLengths | | 0 | 58 | | 1 | 3 | | 2 | 57 | | 3 | 63 | | 4 | 9 | | 5 | 62 | | 6 | 3 | | 7 | 41 | | 8 | 51 | | 9 | 2 | | 10 | 71 | | 11 | 8 | | 12 | 44 | | 13 | 36 | | 14 | 48 | | 15 | 21 | | 16 | 1 | | 17 | 43 | | 18 | 4 | | 19 | 50 | | 20 | 16 | | 21 | 42 | | 22 | 27 | | 23 | 4 | | 24 | 14 | | 25 | 1 | | 26 | 35 | | 27 | 8 | | 28 | 9 | | 29 | 20 | | 30 | 3 | | 31 | 12 | | 32 | 21 | | 33 | 1 | | 34 | 9 | | 35 | 23 | | 36 | 7 | | 37 | 5 |
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| 95.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 73 | | matches | | 0 | "was gone" | | 1 | "were crammed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 153 | | matches | | |
| 46.55% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 89 | | ratio | 0.034 | | matches | | 0 | "She rounded the corner, her flashlight catching glimpses of the suspect’s movements—a flash of a hood, the glint of something metallic in their hand." | | 1 | "The memory of her partner, Morris, flashed in her mind—his face pale, his eyes wide with fear as he vanished into the unknown." | | 2 | "The stalls were crammed with strange artifacts—jars filled with glowing liquids, bones carved into intricate shapes, books bound in what looked like human skin." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 861 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.017421602787456445 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005807200929152149 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 10.47 | | std | 5.75 | | cv | 0.549 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 14 | | 3 | 13 | | 4 | 3 | | 5 | 4 | | 6 | 9 | | 7 | 13 | | 8 | 11 | | 9 | 20 | | 10 | 24 | | 11 | 10 | | 12 | 9 | | 13 | 20 | | 14 | 9 | | 15 | 12 | | 16 | 3 | | 17 | 12 | | 18 | 22 | | 19 | 13 | | 20 | 3 | | 21 | 3 | | 22 | 10 | | 23 | 4 | | 24 | 15 | | 25 | 3 | | 26 | 6 | | 27 | 14 | | 28 | 9 | | 29 | 14 | | 30 | 14 | | 31 | 2 | | 32 | 8 | | 33 | 11 | | 34 | 15 | | 35 | 14 | | 36 | 23 | | 37 | 3 | | 38 | 5 | | 39 | 18 | | 40 | 16 | | 41 | 10 | | 42 | 15 | | 43 | 18 | | 44 | 3 | | 45 | 10 | | 46 | 24 | | 47 | 14 | | 48 | 8 | | 49 | 13 |
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| 40.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3146067415730337 | | totalSentences | 89 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 47.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 72 | | matches | | 0 | "She adjusted her grip on" | | 1 | "They skidded around another corner," | | 2 | "Her sharp jaw tightened, her" | | 3 | "She rounded the corner, her" | | 4 | "She pushed harder, her military" | | 5 | "She barrelled into the door," | | 6 | "She scanned the room, her" | | 7 | "Her eyes darted to the" | | 8 | "Her jaw clenched." | | 9 | "She didn’t have time for" | | 10 | "She strode toward the bookshelf," | | 11 | "She shoved it aside, revealing" | | 12 | "She stepped in, her flashlight" | | 13 | "She crouched, her fingers finding" | | 14 | "She hesitated for a moment," | | 15 | "Her jaw tightened." | | 16 | "She wouldn’t lose another one." | | 17 | "She stepped into the fray," | | 18 | "She scanned the crowd, searching" | | 19 | "Her flashlight caught the edge" |
| | ratio | 0.431 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 72 | | matches | | 0 | "The rain hammered down, turning" | | 1 | "Detective Harlow Quinn sprinted down" | | 2 | "She adjusted her grip on" | | 3 | "The suspect didn’t slow." | | 4 | "They skidded around another corner," | | 5 | "Quinn swore under her breath," | | 6 | "Her sharp jaw tightened, her" | | 7 | "The worn leather watch on" | | 8 | "She rounded the corner, her" | | 9 | "The alley narrowed, the walls" | | 10 | "She pushed harder, her military" | | 11 | "The suspect was heading toward" | | 12 | "The suspect lunged through the" | | 13 | "Quinn didn’t hesitate." | | 14 | "She barrelled into the door," | | 15 | "The dimly lit interior of" | | 16 | "Patrons glanced up from their" | | 17 | "She scanned the room, her" | | 18 | "The suspect was gone." | | 19 | "Her eyes darted to the" |
| | ratio | 0.972 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 1 | | matches | | 0 | "She pushed past a vendor selling vials of shimmering powder, their curses trailing after her." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "Tommy said, his voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 0 | | effectiveRatio | 0 | |