| 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 | 910 | | 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) | |
| 23.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 910 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "fractured" | | 1 | "shattered" | | 2 | "wavering" | | 3 | "echoed" | | 4 | "traced" | | 5 | "footsteps" | | 6 | "thundered" | | 7 | "vibrated" | | 8 | "weight" | | 9 | "standard" | | 10 | "silence" | | 11 | "aligned" |
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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 | 153 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 153 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 153 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 910 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 910 | | uniqueNames | 7 | | maxNameDensity | 1.32 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 12 | | Metropolitan | 2 | | Police | 2 | | Road | 1 | | Station | 1 | | Morris | 1 | | You | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Morris" | | 3 | "You" |
| | places | | | globalScore | 0.841 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | 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 | 910 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 153 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 47.89 | | std | 36.36 | | cv | 0.759 | | sampleLengths | | 0 | 107 | | 1 | 134 | | 2 | 111 | | 3 | 6 | | 4 | 15 | | 5 | 28 | | 6 | 25 | | 7 | 26 | | 8 | 31 | | 9 | 41 | | 10 | 13 | | 11 | 4 | | 12 | 16 | | 13 | 63 | | 14 | 81 | | 15 | 47 | | 16 | 55 | | 17 | 48 | | 18 | 59 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 153 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 174 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 153 | | 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 | 17 | | adverbRatio | 0.018660812294182216 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007683863885839737 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 153 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 153 | | mean | 5.95 | | std | 3.17 | | cv | 0.534 | | sampleLengths | | 0 | 8 | | 1 | 9 | | 2 | 8 | | 3 | 8 | | 4 | 9 | | 5 | 12 | | 6 | 13 | | 7 | 8 | | 8 | 2 | | 9 | 10 | | 10 | 4 | | 11 | 2 | | 12 | 14 | | 13 | 4 | | 14 | 16 | | 15 | 6 | | 16 | 5 | | 17 | 6 | | 18 | 12 | | 19 | 3 | | 20 | 2 | | 21 | 11 | | 22 | 3 | | 23 | 4 | | 24 | 14 | | 25 | 7 | | 26 | 4 | | 27 | 7 | | 28 | 3 | | 29 | 8 | | 30 | 9 | | 31 | 3 | | 32 | 5 | | 33 | 2 | | 34 | 8 | | 35 | 3 | | 36 | 7 | | 37 | 3 | | 38 | 4 | | 39 | 2 | | 40 | 5 | | 41 | 5 | | 42 | 5 | | 43 | 5 | | 44 | 4 | | 45 | 4 | | 46 | 8 | | 47 | 7 | | 48 | 11 | | 49 | 6 |
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| 98.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.6993464052287581 | | totalSentences | 153 | | uniqueOpeners | 107 | |
| 98.77% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 135 | | matches | | 0 | "Officially decommissioned in ninety eight." | | 1 | "Only lantern glow and dim" | | 2 | "Somewhere a metal tray scraped" | | 3 | "Only wet rock and patient" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 135 | | matches | | 0 | "Her trench coat soaked heavy" | | 1 | "He dropped behind a concrete" | | 2 | "She hit the fence, pulled" | | 3 | "She spotted a rusted maintenance" | | 4 | "She killed her flashlight." | | 5 | "She switched on her beam." | | 6 | "You are standing outside the" | | 7 | "I am chasing a man." | | 8 | "You know the passage rules." | | 9 | "She tapped her belt buckle." | | 10 | "He bypassed your checkpoint." | | 11 | "You want the runner, you" | | 12 | "They keep what they trap." | | 13 | "Her watch ticked steadily against" | | 14 | "He paused near a vendor" | | 15 | "He pushed off a wooden" | | 16 | "She stepped onto the crumbling" | | 17 | "She ignored the frequency." | | 18 | "She made her choice." |
| | ratio | 0.141 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 135 | | matches | | 0 | "Harlow Quinn drove through the" | | 1 | "Her trench coat soaked heavy" | | 2 | "Salt and pepper hair stuck" | | 3 | "Tonight the city tried to" | | 4 | "Boots scraped galvanized steel." | | 5 | "He dropped behind a concrete" | | 6 | "Quinn did not hesitate." | | 7 | "She hit the fence, pulled" | | 8 | "Water splashed up past her" | | 9 | "Radio chatter crackled in her" | | 10 | "Static washed over the frequency" | | 11 | "She spotted a rusted maintenance" | | 12 | "Iron kicked inward." | | 13 | "The locking mechanism shattered." | | 14 | "She killed her flashlight." | | 15 | "Texture shifted from smooth cement" | | 16 | "Camden Road Station." | | 17 | "A heavy iron gate blocked" | | 18 | "The other hung loose on" | | 19 | "Quinn slid underneath." |
| | ratio | 0.659 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 135 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 19 | | 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 | |