| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1010 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 95.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1010 | | totalAiIsms | 1 | | 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 | 68 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 68 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 87 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1010 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 805 | | uniqueNames | 11 | | maxNameDensity | 0.62 | | worstName | "Calloway" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 3 | | March | 1 | | Tube | 1 | | Camden | 1 | | Calloway | 5 | | Holt | 3 | | Constable | 1 | | Yates | 2 | | Borough | 1 | | Market | 1 | | Morris | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Calloway" | | 3 | "Holt" | | 4 | "Yates" | | 5 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | 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 | 1010 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 22.95 | | std | 22.18 | | cv | 0.966 | | sampleLengths | | 0 | 17 | | 1 | 84 | | 2 | 39 | | 3 | 60 | | 4 | 22 | | 5 | 3 | | 6 | 7 | | 7 | 12 | | 8 | 7 | | 9 | 57 | | 10 | 4 | | 11 | 6 | | 12 | 33 | | 13 | 4 | | 14 | 4 | | 15 | 63 | | 16 | 70 | | 17 | 15 | | 18 | 21 | | 19 | 23 | | 20 | 8 | | 21 | 22 | | 22 | 32 | | 23 | 28 | | 24 | 3 | | 25 | 57 | | 26 | 5 | | 27 | 1 | | 28 | 1 | | 29 | 12 | | 30 | 13 | | 31 | 32 | | 32 | 2 | | 33 | 7 | | 34 | 9 | | 35 | 70 | | 36 | 11 | | 37 | 38 | | 38 | 3 | | 39 | 52 | | 40 | 17 | | 41 | 5 | | 42 | 10 | | 43 | 21 |
| |
| 74.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 68 | | matches | | 0 | "been arranged" | | 1 | "been rigged" | | 2 | "been taught" | | 3 | "been swept" | | 4 | "been placed" | | 5 | "been photographed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 134 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 87 | | ratio | 0.011 | | matches | | 0 | "She stood at the threshold of what had once been a staffroom — two plastic chairs knocked over, a whiteboard still advertising a fire safety drill scheduled for March, a kettle with a cracked lid sitting on a formica counter." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 806 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.031017369727047148 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004962779156327543 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 87 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 87 | | mean | 11.61 | | std | 8.93 | | cv | 0.769 | | sampleLengths | | 0 | 5 | | 1 | 12 | | 2 | 40 | | 3 | 26 | | 4 | 18 | | 5 | 8 | | 6 | 5 | | 7 | 17 | | 8 | 2 | | 9 | 7 | | 10 | 16 | | 11 | 10 | | 12 | 34 | | 13 | 22 | | 14 | 3 | | 15 | 7 | | 16 | 7 | | 17 | 5 | | 18 | 7 | | 19 | 15 | | 20 | 8 | | 21 | 1 | | 22 | 3 | | 23 | 20 | | 24 | 10 | | 25 | 4 | | 26 | 6 | | 27 | 33 | | 28 | 4 | | 29 | 4 | | 30 | 27 | | 31 | 10 | | 32 | 26 | | 33 | 2 | | 34 | 12 | | 35 | 14 | | 36 | 24 | | 37 | 6 | | 38 | 12 | | 39 | 6 | | 40 | 9 | | 41 | 21 | | 42 | 21 | | 43 | 2 | | 44 | 8 | | 45 | 18 | | 46 | 4 | | 47 | 17 | | 48 | 15 | | 49 | 14 |
| |
| 90.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5747126436781609 | | totalSentences | 87 | | uniqueOpeners | 50 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 61 | | matches | | 0 | "She stood at the threshold" | | 1 | "He sat with his back" | | 2 | "He was young enough to" | | 3 | "She turned the phrase over" | | 4 | "She tilted her head and" | | 5 | "She stood, and let her" | | 6 | "He'd been placed after the" | | 7 | "She pointed to a scuff" | | 8 | "He pushed at the wall" | | 9 | "She was already looking elsewhere," | | 10 | "She pulled a pair of" | | 11 | "She kept her face blank." | | 12 | "She moved to the eastern" | | 13 | "She had taken photographs and" | | 14 | "She stepped into the passage." |
| | ratio | 0.246 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 61 | | matches | | 0 | "The body had been arranged." | | 1 | "That much was obvious the" | | 2 | "She stood at the threshold" | | 3 | "The abandoned Tube station beneath" | | 4 | "Arc lights had been rigged" | | 5 | "The victim was a man" | | 6 | "He sat with his back" | | 7 | "Nobody died like that on" | | 8 | "He was young enough to" | | 9 | "Quinn crouched beside the body" | | 10 | "She turned the phrase over" | | 11 | "Calloway had the grace to" | | 12 | "The thing that had snagged" | | 13 | "She tilted her head and" | | 14 | "Someone had wiped them." | | 15 | "She stood, and let her" | | 16 | "Morris had called it reading" | | 17 | "Dust disturbed in wide, deliberate" | | 18 | "He'd been placed after the" | | 19 | "She pointed to a scuff" |
| | ratio | 0.656 | |
| 81.97% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 61 | | matches | | 0 | "Either way, someone had been" |
| | ratio | 0.016 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "The abandoned Tube station beneath Camden smelled of compacted earth and old rubber, the kind of smell that soaked into your clothes and followed you home." | | 1 | "There was grime under his fingernails, the dark half-moons of a man who had been scrabbling at something earthy, recent." | | 2 | "Dust disturbed in wide, deliberate arcs, as though something had been swept." |
| |
| 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 | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 28 | | tagDensity | 0.107 | | leniency | 0.214 | | rawRatio | 0.333 | | effectiveRatio | 0.071 | |