| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1518 | | 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) | |
| 80.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1518 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "pulsed" | | 1 | "stark" | | 2 | "traced" | | 3 | "flicked" | | 4 | "trembled" | | 5 | "etched" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 74 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1518 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 56.28% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 907 | | uniqueNames | 10 | | maxNameDensity | 1.87 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 17 | | Tube | 1 | | Camden | 1 | | November | 1 | | Veil | 2 | | Market | 1 | | Morris | 1 | | Kowalski | 1 | | Compass | 1 | | Eva | 13 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Morris" | | 3 | "Kowalski" | | 4 | "Compass" | | 5 | "Eva" |
| | places | | | globalScore | 0.563 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1518 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 21.69 | | std | 19.57 | | cv | 0.903 | | sampleLengths | | 0 | 22 | | 1 | 77 | | 2 | 8 | | 3 | 11 | | 4 | 39 | | 5 | 3 | | 6 | 8 | | 7 | 18 | | 8 | 40 | | 9 | 37 | | 10 | 48 | | 11 | 2 | | 12 | 6 | | 13 | 26 | | 14 | 57 | | 15 | 6 | | 16 | 4 | | 17 | 3 | | 18 | 45 | | 19 | 4 | | 20 | 4 | | 21 | 5 | | 22 | 24 | | 23 | 22 | | 24 | 4 | | 25 | 2 | | 26 | 1 | | 27 | 10 | | 28 | 2 | | 29 | 50 | | 30 | 23 | | 31 | 4 | | 32 | 5 | | 33 | 39 | | 34 | 8 | | 35 | 51 | | 36 | 14 | | 37 | 4 | | 38 | 11 | | 39 | 24 | | 40 | 40 | | 41 | 11 | | 42 | 12 | | 43 | 3 | | 44 | 3 | | 45 | 61 | | 46 | 34 | | 47 | 39 | | 48 | 14 | | 49 | 1 |
| |
| 95.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 106 | | matches | | 0 | "been pressed" | | 1 | "been kicked" | | 2 | "been razored" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 141 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 138 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 911 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.01756311745334797 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 11 | | std | 11.45 | | cv | 1.041 | | sampleLengths | | 0 | 22 | | 1 | 11 | | 2 | 18 | | 3 | 9 | | 4 | 7 | | 5 | 19 | | 6 | 9 | | 7 | 4 | | 8 | 8 | | 9 | 11 | | 10 | 7 | | 11 | 8 | | 12 | 24 | | 13 | 3 | | 14 | 8 | | 15 | 18 | | 16 | 7 | | 17 | 5 | | 18 | 12 | | 19 | 3 | | 20 | 3 | | 21 | 10 | | 22 | 9 | | 23 | 6 | | 24 | 2 | | 25 | 2 | | 26 | 5 | | 27 | 7 | | 28 | 6 | | 29 | 16 | | 30 | 8 | | 31 | 5 | | 32 | 8 | | 33 | 11 | | 34 | 2 | | 35 | 6 | | 36 | 26 | | 37 | 12 | | 38 | 10 | | 39 | 11 | | 40 | 6 | | 41 | 18 | | 42 | 2 | | 43 | 4 | | 44 | 4 | | 45 | 3 | | 46 | 45 | | 47 | 4 | | 48 | 4 | | 49 | 5 |
| |
| 52.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.34782608695652173 | | totalSentences | 138 | | uniqueOpeners | 48 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 97 | | matches | | 0 | "It was not the cold" | | 1 | "It cut with a clean" | | 2 | "It held its breath." | | 3 | "She had earned it three" | | 4 | "Her worn leather watch on" | | 5 | "She tucked hair behind her" | | 6 | "It spun, jerked, spun again," | | 7 | "She did not touch." | | 8 | "Her gaze traced the shoulders" | | 9 | "She lifted the right hand" | | 10 | "She turned each hand over." | | 11 | "Her knees cracked." | | 12 | "She stared at the compass." | | 13 | "She eased the flap of" | | 14 | "She worked it free." | | 15 | "She held it under the" | | 16 | "She checked the shoes again." | | 17 | "Her fingers drummed against her" | | 18 | "She set the compass back" | | 19 | "Her breath fogged." |
| | ratio | 0.237 | |
| 32.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 97 | | matches | | 0 | "Harlow Quinn ducked under the" | | 1 | "It was not the cold" | | 2 | "It cut with a clean" | | 3 | "Incense smoke hung in layers" | | 4 | "The Veil Market did not" | | 5 | "It held its breath." | | 6 | "A uniform at the stairhead" | | 7 | "Quinn kept her token in" | | 8 | "The shard of bone warmed" | | 9 | "She had earned it three" | | 10 | "The uniform tipped his chin" | | 11 | "Quinn stepped down onto the" | | 12 | "Gravel crunched under her boots." | | 13 | "Her worn leather watch on" | | 14 | "Brown eyes narrowed." | | 15 | "Salt-and-pepper crop caught the low" | | 16 | "The body lay between the" | | 17 | "Hands open at his sides." | | 18 | "Eyes wide, fixed on the" | | 19 | "Pupils blown to pinpricks of" |
| | ratio | 0.856 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |