| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.563 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 720 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 720 | | totalAiIsms | 15 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | word | "down her spine" | | count | 1 |
| | 11 | | | 12 | |
| | highlights | | 0 | "pulsed" | | 1 | "scanning" | | 2 | "stark" | | 3 | "gloom" | | 4 | "velvet" | | 5 | "measured" | | 6 | "rhythmic" | | 7 | "throb" | | 8 | "shimmered" | | 9 | "chill" | | 10 | "down her spine" | | 11 | "flicker" | | 12 | "potential" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | |
| 90.99% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 49 | | matches | | 0 | "felt a chill" | | 1 | "a flicker of fear" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 49 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 55 | | 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 | 720 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 489 | | uniqueNames | 13 | | maxNameDensity | 2.04 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 10 | | Quinn | 1 | | Camden | 1 | | Tube | 2 | | Veil | 3 | | Market | 4 | | Kowalski | 1 | | Sergeant | 1 | | Davies | 2 | | Eva | 5 | | Aether | 1 | | Ash | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Kowalski" | | 4 | "Sergeant" | | 5 | "Davies" | | 6 | "Eva" |
| | places | | | globalScore | 0.478 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 33 | | 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 | 720 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 55 | | matches | (empty) | |
| 51.74% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 51.43 | | std | 17.03 | | cv | 0.331 | | sampleLengths | | 0 | 68 | | 1 | 57 | | 2 | 22 | | 3 | 82 | | 4 | 54 | | 5 | 44 | | 6 | 39 | | 7 | 22 | | 8 | 60 | | 9 | 58 | | 10 | 56 | | 11 | 67 | | 12 | 31 | | 13 | 60 |
| |
| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 49 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 72 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 492 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.012195121951219513 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.008130081300813009 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 55 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 55 | | mean | 13.09 | | std | 8.12 | | cv | 0.62 | | sampleLengths | | 0 | 11 | | 1 | 23 | | 2 | 14 | | 3 | 12 | | 4 | 8 | | 5 | 18 | | 6 | 15 | | 7 | 17 | | 8 | 7 | | 9 | 22 | | 10 | 3 | | 11 | 22 | | 12 | 20 | | 13 | 19 | | 14 | 4 | | 15 | 1 | | 16 | 1 | | 17 | 3 | | 18 | 9 | | 19 | 14 | | 20 | 11 | | 21 | 13 | | 22 | 16 | | 23 | 12 | | 24 | 9 | | 25 | 7 | | 26 | 3 | | 27 | 13 | | 28 | 6 | | 29 | 21 | | 30 | 8 | | 31 | 2 | | 32 | 2 | | 33 | 6 | | 34 | 16 | | 35 | 20 | | 36 | 4 | | 37 | 36 | | 38 | 12 | | 39 | 28 | | 40 | 18 | | 41 | 17 | | 42 | 8 | | 43 | 7 | | 44 | 5 | | 45 | 4 | | 46 | 15 | | 47 | 7 | | 48 | 31 | | 49 | 29 |
| |
| 80.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5272727272727272 | | totalSentences | 55 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 45 | | matches | (empty) | | ratio | 0 | |
| 95.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 45 | | matches | | 0 | "Her salt-and-pepper crop bristled, eyes" | | 1 | "She tucked a strand of" | | 2 | "Her gaze snagged on the" | | 3 | "His throat bore a jagged" | | 4 | "they pulsed a faint, unnatural" | | 5 | "He held a forensic camera," | | 6 | "She crouched, ignoring the damp" | | 7 | "She brushed her thumb across" | | 8 | "Her voice dropped" | | 9 | "They were deep in the" | | 10 | "It was a statement." | | 11 | "she said, her voice hardening" | | 12 | "She looked at Eva" | | 13 | "She needed to find the" |
| | ratio | 0.311 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 45 | | matches | | 0 | "The bone token burned through" | | 1 | "The abandoned Camden Tube station" | | 2 | "The Veil Market pulsed with" | | 3 | "Harlow’s worn leather watch ticked," | | 4 | "Her salt-and-pepper crop bristled, eyes" | | 5 | "Eva Kowalski stood beside a" | | 6 | "She tucked a strand of" | | 7 | "Eva’s green eyes wide, peering" | | 8 | "The vendor, a pale figure" | | 9 | "Harlow ignored him." | | 10 | "Her gaze snagged on the" | | 11 | "A young man, late twenties," | | 12 | "His throat bore a jagged" | | 13 | "Copper stung Harlow’s nostrils." | | 14 | "they pulsed a faint, unnatural" | | 15 | "A gruff voice broke her" | | 16 | "Sergeant Davies stepped around her," | | 17 | "He held a forensic camera," | | 18 | "Harlow moved closer, her military" | | 19 | "She crouched, ignoring the damp" |
| | ratio | 0.933 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 45 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice hardening" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.063 | | leniency | 0.125 | | rawRatio | 0 | | effectiveRatio | 0 | |