| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.379 | | leniency | 0.759 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1038 | | totalAiIsmAdverbs | 1 | | 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) | |
| 56.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1038 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "solitary" | | 1 | "oppressive" | | 2 | "gloom" | | 3 | "echoed" | | 4 | "scanning" | | 5 | "pulse" | | 6 | "pristine" | | 7 | "methodical" | | 8 | "echoing" |
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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 | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1033 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 770 | | uniqueNames | 11 | | maxNameDensity | 1.04 | | worstName | "Carter" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Carter" | | discoveredNames | | Tube | 1 | | Harlow | 6 | | Quinn | 1 | | Sergeant | 1 | | Liam | 1 | | Carter | 8 | | Veil | 1 | | Market | 3 | | Metropolitan | 1 | | Police | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Liam" | | 4 | "Carter" | | 5 | "Police" | | 6 | "Morris" |
| | places | | | globalScore | 0.981 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.968 | | wordCount | 1033 | | matches | | 0 | "Not burned by flame, but cauterized, as if by immense, instantaneous heat" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 35.62 | | std | 22.75 | | cv | 0.639 | | sampleLengths | | 0 | 86 | | 1 | 44 | | 2 | 24 | | 3 | 52 | | 4 | 14 | | 5 | 17 | | 6 | 48 | | 7 | 44 | | 8 | 13 | | 9 | 4 | | 10 | 33 | | 11 | 71 | | 12 | 7 | | 13 | 11 | | 14 | 64 | | 15 | 19 | | 16 | 23 | | 17 | 8 | | 18 | 66 | | 19 | 23 | | 20 | 49 | | 21 | 18 | | 22 | 35 | | 23 | 56 | | 24 | 47 | | 25 | 51 | | 26 | 31 | | 27 | 3 | | 28 | 72 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 74 | | matches | | 0 | "was singed" | | 1 | "been ordered" |
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| 80.95% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 112 | | matches | | 0 | "was wearing" | | 1 | "was cleaning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 90 | | ratio | 0.011 | | matches | | 0 | "She didn’t look at the token; she looked at the scene." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 774 | | adjectiveStacks | 1 | | stackExamples | | 0 | "slender, bone-white token," |
| | adverbCount | 20 | | adverbRatio | 0.025839793281653745 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.00904392764857881 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 11.48 | | std | 7.54 | | cv | 0.657 | | sampleLengths | | 0 | 24 | | 1 | 1 | | 2 | 29 | | 3 | 32 | | 4 | 13 | | 5 | 16 | | 6 | 15 | | 7 | 10 | | 8 | 12 | | 9 | 2 | | 10 | 12 | | 11 | 14 | | 12 | 14 | | 13 | 12 | | 14 | 14 | | 15 | 17 | | 16 | 8 | | 17 | 17 | | 18 | 5 | | 19 | 15 | | 20 | 3 | | 21 | 10 | | 22 | 34 | | 23 | 3 | | 24 | 4 | | 25 | 6 | | 26 | 4 | | 27 | 15 | | 28 | 18 | | 29 | 5 | | 30 | 14 | | 31 | 11 | | 32 | 5 | | 33 | 2 | | 34 | 18 | | 35 | 16 | | 36 | 5 | | 37 | 2 | | 38 | 11 | | 39 | 29 | | 40 | 20 | | 41 | 8 | | 42 | 7 | | 43 | 8 | | 44 | 11 | | 45 | 8 | | 46 | 4 | | 47 | 11 | | 48 | 8 | | 49 | 23 |
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| 64.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.43333333333333335 | | totalSentences | 90 | | uniqueOpeners | 39 | |
| 93.90% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 71 | | matches | | 0 | "Unofficially, it was her obsession." | | 1 | "Too precise for a frantic" |
| | ratio | 0.028 | |
| 45.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 71 | | matches | | 0 | "Her worn leather shoes crunched" | | 1 | "She found the scene cordoned" | | 2 | "He lay sprawled on his" | | 3 | "Her voice was flat, devoid" | | 4 | "She pulled on a pair" | | 5 | "He held up an evidence" | | 6 | "Her pulse gave a dull" | | 7 | "She moved with a military" | | 8 | "She didn’t look at the" | | 9 | "She crouched, her knees popping." | | 10 | "She didn’t look at Carter," | | 11 | "Her eyes continued their methodical" | | 12 | "Her beam halted on a" | | 13 | "It wasn’t dirt." | | 14 | "It was a residue, a" | | 15 | "She took a step closer." | | 16 | "She ignored him, pulling a" | | 17 | "She dabbed at the smear." | | 18 | "It came away black, shimmering" | | 19 | "It smelled of ozone and" |
| | ratio | 0.437 | |
| 16.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 71 | | matches | | 0 | "The air in the abandoned" | | 1 | "Her worn leather shoes crunched" | | 2 | "The platform should have been" | | 3 | "She found the scene cordoned" | | 4 | "The kneeling figure stood, brushing" | | 5 | "Sergeant Liam Carter, young, earnest," | | 6 | "Harlow stopped at the edge" | | 7 | "The body was a man," | | 8 | "He lay sprawled on his" | | 9 | "A silver-tipped cane lay a" | | 10 | "Her voice was flat, devoid" | | 11 | "Harlow repeated, her gaze drifting" | | 12 | "The station was a known" | | 13 | "She pulled on a pair" | | 14 | "Carter gestured with his chin" | | 15 | "He held up an evidence" | | 16 | "A bone token." | | 17 | "Entry to the Market." | | 18 | "Her pulse gave a dull" | | 19 | "Carter lowered his voice" |
| | ratio | 0.887 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 4 | | matches | | 0 | "Harlow stopped at the edge of the tape, her eyes scanning, cataloging." | | 1 | "A silver-tipped cane lay a few feet away, as if tossed aside." | | 2 | "She moved with a military precision that made the younger officers straighten their backs." | | 3 | "Not burned by flame, but cauterized, as if by immense, instantaneous heat." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 4 | | matches | | 0 | "Harlow repeated, her gaze drifting past Carter" | | 1 | "she said, her voice low" | | 2 | "she instructed, her tone leaving no room for argument" | | 3 | "She peeled off, the snap echoing in the dead air" |
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| 81.03% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Harlow repeated (repeat)" | | 1 | "Her eyes continued (continue)" |
| | dialogueSentences | 29 | | tagDensity | 0.103 | | leniency | 0.207 | | rawRatio | 0.667 | | effectiveRatio | 0.138 | |