| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1517 | | totalAiIsmAdverbs | 2 | | 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) | |
| 60.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1517 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "flickered" | | 1 | "silence" | | 2 | "weight" | | 3 | "standard" | | 4 | "scanned" | | 5 | "could feel" | | 6 | "whisper" | | 7 | "vibrated" | | 8 | "magnetic" | | 9 | "etched" | | 10 | "echoing" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 148 | | matches | | 0 | "was afraid" | | 1 | "was afraid" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 148 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 148 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1517 | | ratio | 0 | | matches | (empty) | |
| 11.36% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 5 | | matches | | 0 | "It was a standard mugging gone wrong, he told her." | | 1 | "It was a bruise mark, he corrected." | | 2 | "Not ash, she murmured." | | 3 | "Show me the body, Quinn said, turning away from the wall." | | 4 | "The knife wound was made by a blade that vibrates, she said." |
| |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1517 | | uniqueNames | 13 | | maxNameDensity | 1.05 | | worstName | "Rourke" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rourke" | | discoveredNames | | Quinn | 12 | | South | 1 | | Camden | 2 | | Inspector | 1 | | Harlow | 4 | | Rourke | 16 | | Chief | 2 | | Morris | 3 | | Veil | 2 | | Compass | 1 | | British | 1 | | Museum | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Harlow" | | 2 | "Rourke" | | 3 | "Morris" | | 4 | "Market" |
| | places | | 0 | "Camden" | | 1 | "Chief" | | 2 | "British" |
| | globalScore | 0.973 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 118 | | glossingSentenceCount | 2 | | matches | | 0 | "tasted like wet copper and old pennies" | | 1 | "felt like the clock was running down th" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1517 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 148 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 38.9 | | std | 30.22 | | cv | 0.777 | | sampleLengths | | 0 | 97 | | 1 | 49 | | 2 | 21 | | 3 | 33 | | 4 | 88 | | 5 | 29 | | 6 | 19 | | 7 | 10 | | 8 | 13 | | 9 | 29 | | 10 | 63 | | 11 | 83 | | 12 | 68 | | 13 | 4 | | 14 | 20 | | 15 | 26 | | 16 | 88 | | 17 | 11 | | 18 | 17 | | 19 | 93 | | 20 | 15 | | 21 | 5 | | 22 | 25 | | 23 | 107 | | 24 | 26 | | 25 | 6 | | 26 | 30 | | 27 | 4 | | 28 | 25 | | 29 | 70 | | 30 | 34 | | 31 | 25 | | 32 | 43 | | 33 | 63 | | 34 | 94 | | 35 | 14 | | 36 | 24 | | 37 | 30 | | 38 | 16 |
| |
| 91.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 148 | | matches | | 0 | "was marked" | | 1 | "was gone" | | 2 | "was etched" | | 3 | "were buried" | | 4 | "was made" | | 5 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 268 | | matches | | 0 | "was running" | | 1 | "wasn't pointing" | | 2 | "was getting" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 148 | | ratio | 0.007 | | matches | | 0 | "She noticed the dust wasn't disturbed by the footprints; the dust had settled differently." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1522 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.020367936925098553 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005256241787122208 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 148 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 148 | | mean | 10.25 | | std | 5.42 | | cv | 0.529 | | sampleLengths | | 0 | 11 | | 1 | 19 | | 2 | 24 | | 3 | 16 | | 4 | 27 | | 5 | 14 | | 6 | 23 | | 7 | 12 | | 8 | 8 | | 9 | 13 | | 10 | 17 | | 11 | 16 | | 12 | 10 | | 13 | 5 | | 14 | 15 | | 15 | 9 | | 16 | 15 | | 17 | 8 | | 18 | 24 | | 19 | 2 | | 20 | 17 | | 21 | 12 | | 22 | 7 | | 23 | 12 | | 24 | 5 | | 25 | 5 | | 26 | 13 | | 27 | 8 | | 28 | 9 | | 29 | 5 | | 30 | 7 | | 31 | 28 | | 32 | 7 | | 33 | 5 | | 34 | 6 | | 35 | 17 | | 36 | 10 | | 37 | 12 | | 38 | 5 | | 39 | 13 | | 40 | 6 | | 41 | 8 | | 42 | 10 | | 43 | 4 | | 44 | 9 | | 45 | 6 | | 46 | 9 | | 47 | 12 | | 48 | 14 | | 49 | 7 |
| |
| 40.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.28378378378378377 | | totalSentences | 148 | | uniqueOpeners | 42 | |
| 68.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 146 | | matches | | 0 | "Just a cut, and an" | | 1 | "Maybe a cardiac event, maybe" | | 2 | "Just don't bring it up" |
| | ratio | 0.021 | |
| 30.96% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 69 | | totalSentences | 146 | | matches | | 0 | "She paused on the edge" | | 1 | "It was marked on the" | | 2 | "She believed in patterns, and" | | 3 | "He was thirty-five, fresh-faced, and" | | 4 | "It was a standard mugging" | | 5 | "He pointed with his chin." | | 6 | "His expression was tight with" | | 7 | "You’re staring too hard, Harlow," | | 8 | "It's the blood, she said." | | 9 | "Her voice was flat, low-pressured." | | 10 | "It was a bruise mark," | | 11 | "They were dead before the" | | 12 | "Her eyes scanned the platform" | | 13 | "She looked at the tracks." | | 14 | "She stepped closer to the" | | 15 | "Her gaze dropped to the" | | 16 | "They didn't come from the" | | 17 | "They came from the wall" | | 18 | "She crouched, bringing her face" | | 19 | "She noticed the dust wasn't" |
| | ratio | 0.473 | |
| 45.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 121 | | totalSentences | 146 | | matches | | 0 | "The air down here tasted" | | 1 | "Harlow Quinn pulled her collar" | | 2 | "She paused on the edge" | | 3 | "Military precision meant silence, a" | | 4 | "The abandoned South Camden line" | | 5 | "It was marked on the" | | 6 | "Detective Inspector Harlow Quinn didn't" | | 7 | "She believed in patterns, and" | | 8 | "DS Rourke stood near the" | | 9 | "He was thirty-five, fresh-faced, and" | | 10 | "It was a standard mugging" | | 11 | "He pointed with his chin." | | 12 | "The victim lay in the" | | 13 | "A knife wound to the" | | 14 | "The local coppers from the" | | 15 | "Rourke pushed off the pillar," | | 16 | "His expression was tight with" | | 17 | "You’re staring too hard, Harlow," | | 18 | "It's the blood, she said." | | 19 | "Her voice was flat, low-pressured." |
| | ratio | 0.829 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 146 | | matches | | 0 | "If you slice the carotid," | | 1 | "If the market is here," | | 2 | "If you start investigating the" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 69 | | technicalSentenceCount | 4 | | matches | | 0 | "Harlow Quinn pulled her collar higher against the draft that slipped through the cracks of the discarded concrete tiles." | | 1 | "It was marked on the city plans as a decommissioned infrastructure hazard, a hole in the underground map that didn't belong to anyone." | | 2 | "I have witnesses who said they saw the victim arguing at the station exit." | | 3 | "They were all people who worked at the archives near the British Museum." |
| |
| 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 | |