| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.14 | | leniency | 0.28 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1647 | | 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) | |
| 93.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1647 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 123 | | matches | (empty) | |
| 96.40% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 123 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 166 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1647 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1280 | | uniqueNames | 8 | | maxNameDensity | 1.64 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Camden | 1 | | Veil | 2 | | Market | 12 | | Kowalski | 1 | | Eva | 18 | | Compass | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Compass" |
| | places | (empty) | | globalScore | 0.68 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 93 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a Market verdict" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1647 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 166 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 71 | | mean | 23.2 | | std | 24.6 | | cv | 1.06 | | sampleLengths | | 0 | 95 | | 1 | 40 | | 2 | 7 | | 3 | 5 | | 4 | 27 | | 5 | 76 | | 6 | 9 | | 7 | 5 | | 8 | 2 | | 9 | 6 | | 10 | 66 | | 11 | 8 | | 12 | 45 | | 13 | 3 | | 14 | 9 | | 15 | 4 | | 16 | 2 | | 17 | 10 | | 18 | 3 | | 19 | 7 | | 20 | 3 | | 21 | 11 | | 22 | 8 | | 23 | 1 | | 24 | 63 | | 25 | 7 | | 26 | 24 | | 27 | 8 | | 28 | 11 | | 29 | 92 | | 30 | 62 | | 31 | 8 | | 32 | 9 | | 33 | 23 | | 34 | 7 | | 35 | 4 | | 36 | 8 | | 37 | 13 | | 38 | 23 | | 39 | 18 | | 40 | 65 | | 41 | 17 | | 42 | 5 | | 43 | 2 | | 44 | 25 | | 45 | 34 | | 46 | 6 | | 47 | 15 | | 48 | 47 | | 49 | 4 |
| |
| 76.74% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 10 | | totalSentences | 123 | | matches | | 0 | "was etched" | | 1 | "been smeared" | | 2 | "were bitten" | | 3 | "been pulled" | | 4 | "been placed" | | 5 | "was painted" | | 6 | "been gone" | | 7 | "been scraped" | | 8 | "been pressed" | | 9 | "been opened" | | 10 | "been opened" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 188 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 166 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1283 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.01636788776305534 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002338269680436477 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 166 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 166 | | mean | 9.92 | | std | 6.74 | | cv | 0.68 | | sampleLengths | | 0 | 20 | | 1 | 14 | | 2 | 6 | | 3 | 13 | | 4 | 27 | | 5 | 15 | | 6 | 6 | | 7 | 17 | | 8 | 17 | | 9 | 7 | | 10 | 3 | | 11 | 2 | | 12 | 7 | | 13 | 20 | | 14 | 6 | | 15 | 16 | | 16 | 9 | | 17 | 14 | | 18 | 14 | | 19 | 17 | | 20 | 2 | | 21 | 3 | | 22 | 4 | | 23 | 5 | | 24 | 2 | | 25 | 3 | | 26 | 3 | | 27 | 5 | | 28 | 16 | | 29 | 8 | | 30 | 22 | | 31 | 8 | | 32 | 7 | | 33 | 8 | | 34 | 6 | | 35 | 16 | | 36 | 5 | | 37 | 18 | | 38 | 3 | | 39 | 3 | | 40 | 6 | | 41 | 4 | | 42 | 2 | | 43 | 7 | | 44 | 3 | | 45 | 3 | | 46 | 7 | | 47 | 3 | | 48 | 5 | | 49 | 6 |
| |
| 37.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.2289156626506024 | | totalSentences | 166 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 121 | | matches | | 0 | "Her worn leather watch, strapped" | | 1 | "Her round glasses caught the" | | 2 | "She tucked a red curl" | | 3 | "His coat had been split" | | 4 | "His left hand held two" | | 5 | "Her knees cracked." | | 6 | "She moved to the bone" | | 7 | "She lifted her torch above" | | 8 | "She returned to the body" | | 9 | "He had not held the" | | 10 | "She pushed them up." | | 11 | "Her leather watch ticked against" | | 12 | "She pressed her knuckles to" | | 13 | "It had not." | | 14 | "Their wax fell toward the" | | 15 | "She stepped closer to the" | | 16 | "She did not lift it." | | 17 | "She angled her torch through" | | 18 | "He had not entered through" | | 19 | "He had come through a" |
| | ratio | 0.223 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 115 | | totalSentences | 121 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "The abandoned tube station opened" | | 2 | "A ruined roundel over a" | | 3 | "The full moon had pulled" | | 4 | "Her worn leather watch, strapped" | | 5 | "Eva Kowalski waited beside the" | | 6 | "Her round glasses caught the" | | 7 | "She tucked a red curl" | | 8 | "Quinn stepped closer." | | 9 | "Eva pointed with a pencil" | | 10 | "The man lay on his" | | 11 | "His coat had been split" | | 12 | "A brass compass rested on" | | 13 | "The compass casing wore a" | | 14 | "His left hand held two" | | 15 | "Candles stood at four points" | | 16 | "Her knees cracked." | | 17 | "Eva's jaw tightened." | | 18 | "Quinn studied the chalk first." | | 19 | "The line around the man" |
| | ratio | 0.95 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |