| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 86.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1124 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 55.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1124 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "gloom" | | 1 | "silence" | | 2 | "echoing" | | 3 | "synthetic" | | 4 | "weight" | | 5 | "etched" | | 6 | "shattered" | | 7 | "pristine" | | 8 | "tapestry" |
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| 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 | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 134 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1124 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 9 | | matches | | 0 | "It is not an overdose, Detective, Eva said, her voice echoing slightly against the curved ceiling." | | 1 | "Or someone pushed him, Quinn said, her voice low." | | 2 | "Exactly, Quinn said." | | 3 | "He did not die from the ritual, Quinn said, standing up and brushing off her knees." | | 4 | "Are residual, Quinn confirmed." | | 5 | "So it was murder, Eva said, her voice quiet." | | 6 | "Bag the chalk, Quinn ordered Evans." | | 7 | "Every full moon, Eva replied." | | 8 | "Let is go, Quinn said." |
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| 38.79% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 1124 | | uniqueNames | 14 | | maxNameDensity | 2.22 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 2 | | Quinn | 25 | | Tube | 1 | | Veil | 2 | | Market | 1 | | Kowalski | 1 | | Detective | 1 | | Eva | 13 | | Morris | 2 | | Evans | 6 | | Guv | 1 | | Compass | 1 | | Shade | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Morris" | | 6 | "Evans" |
| | places | | | globalScore | 0.388 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 1 | | matches | | 0 | "sigils that seemed to shift slightly in the torchlight" |
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| 22.06% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.779 | | wordCount | 1124 | | matches | | 0 | "Not faded, not smudged by foot traffic, but intentionally severed on the northern arc" | | 1 | "not smudged by foot traffic, but intentionally severed on the northern arc" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 37.47 | | std | 23.47 | | cv | 0.627 | | sampleLengths | | 0 | 105 | | 1 | 92 | | 2 | 39 | | 3 | 59 | | 4 | 30 | | 5 | 47 | | 6 | 32 | | 7 | 48 | | 8 | 27 | | 9 | 49 | | 10 | 21 | | 11 | 46 | | 12 | 14 | | 13 | 56 | | 14 | 11 | | 15 | 39 | | 16 | 28 | | 17 | 14 | | 18 | 63 | | 19 | 26 | | 20 | 12 | | 21 | 65 | | 22 | 34 | | 23 | 27 | | 24 | 8 | | 25 | 22 | | 26 | 5 | | 27 | 57 | | 28 | 14 | | 29 | 34 |
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| 84.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 134 | | matches | | 0 | "were gone" | | 1 | "were tucked" | | 2 | "was involved" | | 3 | "was etched" | | 4 | "was locked" | | 5 | "was killed" | | 6 | "was placed" | | 7 | "was staged" | | 8 | "was locked" |
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| 76.54% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 216 | | matches | | 0 | "was not spinning" | | 1 | "was trying" | | 2 | "was sniffing" | | 3 | "were using" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1126 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.020426287744227355 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009769094138543518 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 8.39 | | std | 5.47 | | cv | 0.652 | | sampleLengths | | 0 | 21 | | 1 | 16 | | 2 | 31 | | 3 | 21 | | 4 | 16 | | 5 | 16 | | 6 | 9 | | 7 | 21 | | 8 | 9 | | 9 | 15 | | 10 | 22 | | 11 | 16 | | 12 | 11 | | 13 | 7 | | 14 | 5 | | 15 | 5 | | 16 | 9 | | 17 | 36 | | 18 | 9 | | 19 | 10 | | 20 | 8 | | 21 | 6 | | 22 | 6 | | 23 | 8 | | 24 | 3 | | 25 | 3 | | 26 | 16 | | 27 | 11 | | 28 | 6 | | 29 | 11 | | 30 | 5 | | 31 | 7 | | 32 | 5 | | 33 | 4 | | 34 | 9 | | 35 | 7 | | 36 | 14 | | 37 | 15 | | 38 | 3 | | 39 | 7 | | 40 | 5 | | 41 | 4 | | 42 | 11 | | 43 | 5 | | 44 | 5 | | 45 | 11 | | 46 | 9 | | 47 | 16 | | 48 | 3 | | 49 | 11 |
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| 52.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.35074626865671643 | | totalSentences | 134 | | uniqueOpeners | 47 | |
| 76.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 130 | | matches | | 0 | "Exactly, Quinn said." | | 1 | "Maybe they floated, Evans suggested," | | 2 | "Then we have until then" |
| | ratio | 0.023 | |
| 96.92% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 130 | | matches | | 0 | "She clicked her torch on," | | 1 | "She was forty-one, and her" | | 2 | "She glanced at the woman" | | 3 | "It is not an overdose," | | 4 | "She pushed her round glasses" | | 5 | "She did not trust the" | | 6 | "His skin was pale, almost" | | 7 | "She reached out, gloved fingers" | | 8 | "She pulled the object from" | | 9 | "It was a small brass" | | 10 | "It points to rifts." | | 11 | "It was locked, pointing rigidly" | | 12 | "He stepped inside it." | | 13 | "She walked the perimeter, her" | | 14 | "She stopped at the chalk" | | 15 | "It was broken." | | 16 | "It would allow energy to" | | 17 | "She pointed her torch at" | | 18 | "She looked at the wall" | | 19 | "He did not die from" |
| | ratio | 0.308 | |
| 83.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 130 | | matches | | 0 | "The air beneath Camden tasted" | | 1 | "She clicked her torch on," | | 2 | "This was the third location" | | 3 | "Quinn adjusted the worn leather" | | 4 | "A body lay sprawled near" | | 5 | "Quinn crouched, her knees cracking" | | 6 | "She was forty-one, and her" | | 7 | "She glanced at the woman" | | 8 | "Eva Kowalski was not police," | | 9 | "The red curls were tucked" | | 10 | "It is not an overdose," | | 11 | "She pushed her round glasses" | | 12 | "The sigils on the floor" | | 13 | "This was a ritual breach." | | 14 | "Quinn kept her expression neutral." | | 15 | "She did not trust the" | | 16 | "Quinn suspected the clique Eva" | | 17 | "PC Evans, the uniformed officer" | | 18 | "Looks like a bad trip" | | 19 | "Quinn ignored him and focused" |
| | ratio | 0.754 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 130 | | matches | | 0 | "If the needle is spinning," | | 1 | "If the portal was unstable," | | 2 | "If the man had died" | | 3 | "If the man broke the" | | 4 | "If someone else broke it," |
| | ratio | 0.038 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "The air beneath Camden tasted of rust and wet iron, a metallic tang that coated the back of Harlow Quinn's throat." | | 1 | "She thought of Morris, of the files she kept locked in her desk drawer that made no sense." |
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| 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 | |