| 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 | 1480 | | 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) | |
| 89.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1480 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "scanned" | | 1 | "etched" | | 2 | "weight" |
| |
| 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 | 114 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 167 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1480 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1005 | | uniqueNames | 11 | | maxNameDensity | 1.99 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 20 | | Veil | 1 | | Market | 1 | | Kowalski | 1 | | Sergeant | 1 | | Miles | 1 | | Brody | 10 | | Eva | 11 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Kowalski" | | 4 | "Sergeant" | | 5 | "Brody" | | 6 | "Eva" |
| | places | (empty) | | globalScore | 0.505 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | 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 | 1480 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 167 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 97 | | mean | 15.26 | | std | 16.94 | | cv | 1.11 | | sampleLengths | | 0 | 80 | | 1 | 7 | | 2 | 9 | | 3 | 66 | | 4 | 4 | | 5 | 8 | | 6 | 46 | | 7 | 47 | | 8 | 5 | | 9 | 2 | | 10 | 5 | | 11 | 24 | | 12 | 12 | | 13 | 36 | | 14 | 19 | | 15 | 25 | | 16 | 38 | | 17 | 7 | | 18 | 2 | | 19 | 4 | | 20 | 12 | | 21 | 5 | | 22 | 58 | | 23 | 2 | | 24 | 1 | | 25 | 8 | | 26 | 4 | | 27 | 10 | | 28 | 3 | | 29 | 20 | | 30 | 5 | | 31 | 3 | | 32 | 4 | | 33 | 56 | | 34 | 6 | | 35 | 6 | | 36 | 23 | | 37 | 3 | | 38 | 5 | | 39 | 6 | | 40 | 42 | | 41 | 7 | | 42 | 2 | | 43 | 17 | | 44 | 57 | | 45 | 6 | | 46 | 3 | | 47 | 3 | | 48 | 35 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 160 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 167 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1009 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.005946481665014866 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0019821605550049554 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 167 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 167 | | mean | 8.86 | | std | 5.81 | | cv | 0.656 | | sampleLengths | | 0 | 16 | | 1 | 16 | | 2 | 13 | | 3 | 13 | | 4 | 18 | | 5 | 4 | | 6 | 7 | | 7 | 9 | | 8 | 4 | | 9 | 12 | | 10 | 18 | | 11 | 11 | | 12 | 21 | | 13 | 4 | | 14 | 8 | | 15 | 9 | | 16 | 10 | | 17 | 7 | | 18 | 7 | | 19 | 13 | | 20 | 14 | | 21 | 14 | | 22 | 6 | | 23 | 13 | | 24 | 5 | | 25 | 2 | | 26 | 5 | | 27 | 12 | | 28 | 12 | | 29 | 12 | | 30 | 9 | | 31 | 5 | | 32 | 22 | | 33 | 12 | | 34 | 7 | | 35 | 25 | | 36 | 4 | | 37 | 11 | | 38 | 8 | | 39 | 15 | | 40 | 7 | | 41 | 2 | | 42 | 4 | | 43 | 12 | | 44 | 5 | | 45 | 6 | | 46 | 15 | | 47 | 12 | | 48 | 9 | | 49 | 4 |
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| 46.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.32335329341317365 | | totalSentences | 167 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 109 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 109 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She checked the worn leather" | | 2 | "His head leaned against the" | | 3 | "She tucked a strand behind" | | 4 | "Its face bore etched sigils," | | 5 | "He held a notebook against" | | 6 | "His hands rested in his" | | 7 | "Their soles were clean." | | 8 | "Her fingers hovered over the" | | 9 | "She leaned closer." | | 10 | "She touched it with a" | | 11 | "She held the bag at" | | 12 | "She returned to the table" | | 13 | "It kept its line toward" | | 14 | "Its surface carried the same" | | 15 | "She compared the angle to" | | 16 | "It carried a second smell:" | | 17 | "She tucked the same curl" |
| | ratio | 0.165 | |
| 10.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 109 | | matches | | 0 | "Rain had ceased, yet water" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The stairwell carried the smell" | | 3 | "Her closely cropped salt-and-pepper hair" | | 4 | "She checked the worn leather" | | 5 | "Rust dust coated it." | | 6 | "A uniformed constable waited at" | | 7 | "Quinn scanned the platform." | | 8 | "Stalls of black timber and" | | 9 | "Glass jars, bundles of dried" | | 10 | "Charms stitched into canvas flaps" | | 11 | "The Veil Market moved with" | | 12 | "Quinn followed the constable's gaze" | | 13 | "A man sat behind a" | | 14 | "His head leaned against the" | | 15 | "A black mark spread across" | | 16 | "The air above him carried" | | 17 | "Eva Kowalski crouched beside the" | | 18 | "Freckles stood out on her" | | 19 | "She tucked a strand behind" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 109 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | 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 | |