| 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 | 1497 | | 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) | |
| 53.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1497 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "macabre" | | 1 | "chill" | | 2 | "traced" | | 3 | "weight" | | 4 | "scanned" | | 5 | "trembled" | | 6 | "silence" | | 7 | "shattered" | | 8 | "standard" | | 9 | "vibrated" | | 10 | "resonated" | | 11 | "predator" |
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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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 151 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1497 | | ratio | 0 | | matches | (empty) | |
| 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 | 30 | | wordCount | 1497 | | uniqueNames | 8 | | maxNameDensity | 0.73 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Camden | 1 | | Hale | 9 | | Kowalski | 1 | | Shade | 1 | | Morris | 2 | | Eva | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Hale" | | 3 | "Kowalski" | | 4 | "Morris" | | 5 | "Eva" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | 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 | 1497 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 151 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 39.39 | | std | 21.05 | | cv | 0.534 | | sampleLengths | | 0 | 98 | | 1 | 31 | | 2 | 29 | | 3 | 42 | | 4 | 71 | | 5 | 10 | | 6 | 27 | | 7 | 61 | | 8 | 31 | | 9 | 30 | | 10 | 39 | | 11 | 28 | | 12 | 9 | | 13 | 39 | | 14 | 50 | | 15 | 49 | | 16 | 22 | | 17 | 28 | | 18 | 37 | | 19 | 21 | | 20 | 44 | | 21 | 87 | | 22 | 44 | | 23 | 32 | | 24 | 12 | | 25 | 17 | | 26 | 59 | | 27 | 43 | | 28 | 14 | | 29 | 26 | | 30 | 72 | | 31 | 55 | | 32 | 29 | | 33 | 35 | | 34 | 25 | | 35 | 17 | | 36 | 71 | | 37 | 63 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 151 | | matches | | 0 | "been stored" | | 1 | "were asked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 265 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 151 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1502 | | adjectiveStacks | 1 | | stackExamples | | 0 | "steady behind round glasses." |
| | adverbCount | 34 | | adverbRatio | 0.022636484687083888 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006657789613848202 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 9.91 | | std | 6.36 | | cv | 0.641 | | sampleLengths | | 0 | 16 | | 1 | 13 | | 2 | 14 | | 3 | 40 | | 4 | 15 | | 5 | 8 | | 6 | 8 | | 7 | 4 | | 8 | 11 | | 9 | 9 | | 10 | 20 | | 11 | 9 | | 12 | 7 | | 13 | 16 | | 14 | 7 | | 15 | 3 | | 16 | 4 | | 17 | 16 | | 18 | 5 | | 19 | 5 | | 20 | 25 | | 21 | 5 | | 22 | 11 | | 23 | 10 | | 24 | 4 | | 25 | 2 | | 26 | 5 | | 27 | 10 | | 28 | 6 | | 29 | 5 | | 30 | 7 | | 31 | 12 | | 32 | 28 | | 33 | 9 | | 34 | 5 | | 35 | 6 | | 36 | 20 | | 37 | 6 | | 38 | 2 | | 39 | 2 | | 40 | 8 | | 41 | 12 | | 42 | 13 | | 43 | 5 | | 44 | 21 | | 45 | 4 | | 46 | 5 | | 47 | 6 | | 48 | 13 | | 49 | 9 |
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| 65.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4304635761589404 | | totalSentences | 151 | | uniqueOpeners | 65 | |
| 23.64% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 141 | | matches | | 0 | "Only this table lay in" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 141 | | matches | | 0 | "Her flashlight sliced through curtains" | | 1 | "She paused at the edge" | | 2 | "His uniform jacket hung open," | | 3 | "His face locked in a" | | 4 | "She lifted one limp hand." | | 5 | "He probably shorted the wrong" | | 6 | "She set the limb down" | | 7 | "She picked the instrument up." | | 8 | "Her satchel bulged with books." | | 9 | "Its needle seeks the closest" | | 10 | "My interpretation holds." | | 11 | "She ran her fingers across" | | 12 | "Her breath fogged." | | 13 | "She knelt again beside the" | | 14 | "She pulled a slim volume" | | 15 | "He sold information here." | | 16 | "You're both reaching." | | 17 | "She checked the dust layer" | | 18 | "She followed the trail to" | | 19 | "She slipped it free and" |
| | ratio | 0.234 | |
| 73.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 109 | | totalSentences | 141 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "Her flashlight sliced through curtains" | | 2 | "The platform opened before her" | | 3 | "Police floodlights hummed on their" | | 4 | "She paused at the edge" | | 5 | "The air tasted of ozone" | | 6 | "Sergeant Hale waved her over" | | 7 | "His uniform jacket hung open," | | 8 | "Looks like another turf war" | | 9 | "Victim took a blade to" | | 10 | "Killer dragged him a few" | | 11 | "Footprints head straight toward the" | | 12 | "Quinn crouched without answering." | | 13 | "The dead man wore layered" | | 14 | "His face locked in a" | | 15 | "She lifted one limp hand." | | 16 | "Hale shifted his weight from" | | 17 | "These market types sell everything" | | 18 | "He probably shorted the wrong" | | 19 | "Quinn turned the hand over." |
| | ratio | 0.773 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 141 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 4 | | matches | | 0 | "Makeshift stalls crowded the space between rusting rails, their surfaces heaped with stoppered vials of luminous fluid, stacks of parchment inscribed in inks th…" | | 1 | "His uniform jacket hung open, and sweat beaded along his hairline despite the chill that radiated from the tunnel mouth." | | 2 | "A small brass compass rested on the edge of the nearest table, its casing green with age, face crowded with sigils that looked older than the station itself." | | 3 | "She had until the next full moon to map the connections that had killed this guardian and, years earlier, her partner." |
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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 | |