| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.91% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1650 | | totalAiIsmAdverbs | 3 | | 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) | |
| 57.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1650 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "flickered" | | 1 | "silence" | | 2 | "weight" | | 3 | "standard" | | 4 | "traced" | | 5 | "scanned" | | 6 | "etched" | | 7 | "echoed" | | 8 | "aligned" | | 9 | "gloom" |
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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 | 295 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 295 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 295 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 20 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1650 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 11 | | matches | | 0 | "Gang execution, Miller said." | | 1 | "Pull the blade, Harlow said." | | 2 | "Leave it, Harlow said." | | 3 | "The needle does not move, Miller said." | | 4 | "Check the scuff marks, Harlow said." | | 5 | "Bone dust, Harlow said." | | 6 | "Check the door, Harlow said." | | 7 | "Morris never found the source, she said quietly." | | 8 | "I am bringing up facts, Harlow replied." | | 9 | "The Veil Market operates underground, she said." | | 10 | "I want you to drill the door, Harlow said." |
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| 71.21% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1650 | | uniqueNames | 9 | | maxNameDensity | 1.58 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 26 | | Quinn | 2 | | Camden | 1 | | Miller | 22 | | Shade | 1 | | Morris | 2 | | Veil | 1 | | Market | 1 | | You | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Morris" | | 4 | "Market" | | 5 | "You" |
| | places | (empty) | | globalScore | 0.712 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 133 | | 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 | 1650 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 295 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 33.67 | | std | 18.86 | | cv | 0.56 | | sampleLengths | | 0 | 80 | | 1 | 32 | | 2 | 11 | | 3 | 43 | | 4 | 41 | | 5 | 51 | | 6 | 17 | | 7 | 31 | | 8 | 73 | | 9 | 9 | | 10 | 11 | | 11 | 31 | | 12 | 17 | | 13 | 57 | | 14 | 29 | | 15 | 23 | | 16 | 28 | | 17 | 14 | | 18 | 21 | | 19 | 63 | | 20 | 25 | | 21 | 5 | | 22 | 38 | | 23 | 22 | | 24 | 51 | | 25 | 15 | | 26 | 13 | | 27 | 52 | | 28 | 23 | | 29 | 57 | | 30 | 31 | | 31 | 12 | | 32 | 49 | | 33 | 9 | | 34 | 38 | | 35 | 67 | | 36 | 20 | | 37 | 47 | | 38 | 21 | | 39 | 52 | | 40 | 33 | | 41 | 12 | | 42 | 47 | | 43 | 61 | | 44 | 41 | | 45 | 13 | | 46 | 23 | | 47 | 57 | | 48 | 34 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 295 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 315 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 295 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1650 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.025454545454545455 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.009696969696969697 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 295 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 295 | | mean | 5.59 | | std | 3.24 | | cv | 0.579 | | sampleLengths | | 0 | 20 | | 1 | 8 | | 2 | 11 | | 3 | 7 | | 4 | 16 | | 5 | 6 | | 6 | 12 | | 7 | 13 | | 8 | 10 | | 9 | 9 | | 10 | 4 | | 11 | 7 | | 12 | 6 | | 13 | 13 | | 14 | 6 | | 15 | 12 | | 16 | 6 | | 17 | 4 | | 18 | 4 | | 19 | 2 | | 20 | 2 | | 21 | 6 | | 22 | 7 | | 23 | 9 | | 24 | 4 | | 25 | 3 | | 26 | 9 | | 27 | 4 | | 28 | 10 | | 29 | 7 | | 30 | 5 | | 31 | 5 | | 32 | 8 | | 33 | 2 | | 34 | 1 | | 35 | 5 | | 36 | 7 | | 37 | 5 | | 38 | 2 | | 39 | 8 | | 40 | 6 | | 41 | 4 | | 42 | 3 | | 43 | 3 | | 44 | 2 | | 45 | 3 | | 46 | 7 | | 47 | 12 | | 48 | 1 | | 49 | 2 |
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| 39.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 46 | | diversityRatio | 0.33220338983050846 | | totalSentences | 295 | | uniqueOpeners | 98 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 9 | | totalSentences | 246 | | matches | | 0 | "Only the distant drip of" | | 1 | "Probably a debt collection gone" | | 2 | "Maybe a silencer." | | 3 | "Probably dropped by the perp." | | 4 | "Maybe he sat, then fell" | | 5 | "Then she looked at the" | | 6 | "Then they vanished through the" | | 7 | "Only the body in." | | 8 | "Only the truth left behind." |
| | ratio | 0.037 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 64 | | totalSentences | 246 | | matches | | 0 | "He shifted his weight when" | | 1 | "His arms hung at unnatural" | | 2 | "He tapped his pen against" | | 3 | "We found a silver blade" | | 4 | "Her sharp jaw tightened." | | 5 | "She traced the line of" | | 6 | "It stopped exactly at the" | | 7 | "Her voice cut through the" | | 8 | "I want a full trace." | | 9 | "I already logged the entry" | | 10 | "She crouched beside the head" | | 11 | "Her brown eyes caught a" | | 12 | "It looks like junk." | | 13 | "It sits on a crime" | | 14 | "She noted the placement of" | | 15 | "I want to know if" | | 16 | "You think the body moved" | | 17 | "He died upright." | | 18 | "He never fell." | | 19 | "Her boots echoed sharply against" |
| | ratio | 0.26 | |
| 65.69% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 194 | | totalSentences | 246 | | matches | | 0 | "Detective Harlow Quinn stepped through" | | 1 | "The air tasted of damp" | | 2 | "Harlow adjusted the worn leather" | | 3 | "The silence pressed against her" | | 4 | "A uniformed officer stood near" | | 5 | "Harlow approached and noted the" | | 6 | "He shifted his weight when" | | 7 | "DS Miller, she thought." | | 8 | "Forensics called him in twenty" | | 9 | "Miller pointed toward the third" | | 10 | "A man lay sprawled across" | | 11 | "His arms hung at unnatural" | | 12 | "Blood pooled around his torso" | | 13 | "The platform tiles above remained" | | 14 | "Gang execution, Miller said." | | 15 | "Points at the body." | | 16 | "He tapped his pen against" | | 17 | "We found a silver blade" | | 18 | "Matches the wound channels." | | 19 | "Harlow knelt beside the tape" |
| | ratio | 0.789 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 246 | | matches | | 0 | "To suggest a struggle." | | 1 | "To misdirect the initial sweep." | | 2 | "To tear in the fabric." | | 3 | "To places where the world" | | 4 | "If we open that door" | | 5 | "If you are wrong, Quinn," |
| | ratio | 0.024 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | 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 | |