| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1042 | | totalAiIsmAdverbs | 1 | | 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) | |
| 61.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1042 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "porcelain" | | 1 | "flicker" | | 2 | "standard" | | 3 | "stomach" | | 4 | "etched" | | 5 | "eyebrow" | | 6 | "echoes" |
| |
| 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 | 40 | | matches | (empty) | |
| 71.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 40 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 47 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1039 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.98% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 833 | | uniqueNames | 13 | | maxNameDensity | 1.2 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Torres" | | discoveredNames | | Camden | 1 | | Tube | 2 | | Morris | 3 | | Mike | 1 | | Torres | 8 | | Sandhurst | 2 | | Harlow | 10 | | British | 1 | | Museum | 1 | | Soho | 1 | | Shade | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Morris" | | 1 | "Mike" | | 2 | "Torres" | | 3 | "Sandhurst" | | 4 | "Harlow" | | 5 | "Market" |
| | places | | | globalScore | 0.9 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | 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 | 1039 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 47 | | matches | (empty) | |
| 71.78% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 64.94 | | std | 26.05 | | cv | 0.401 | | sampleLengths | | 0 | 80 | | 1 | 68 | | 2 | 55 | | 3 | 92 | | 4 | 87 | | 5 | 78 | | 6 | 61 | | 7 | 3 | | 8 | 46 | | 9 | 49 | | 10 | 93 | | 11 | 21 | | 12 | 70 | | 13 | 58 | | 14 | 108 | | 15 | 70 |
| |
| 87.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 40 | | matches | | 0 | "are dusted" | | 1 | "are clenched" | | 2 | "was found" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 132 | | matches | (empty) | |
| 82.07% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 47 | | ratio | 0.021 | | matches | | 0 | "The fluid pooled under the victim’s ribs isn’t blood—it shifts iridescent in the flashlight beam, like oil on a puddle, and carries a sharp, sweet tang of burnt frankincense she hasn’t smelled since Morris’s funeral." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 842 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint, vivid purple, swirling" |
| | adverbCount | 12 | | adverbRatio | 0.014251781472684086 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004750593824228029 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 47 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 47 | | mean | 22.11 | | std | 10.95 | | cv | 0.495 | | sampleLengths | | 0 | 21 | | 1 | 24 | | 2 | 35 | | 3 | 52 | | 4 | 16 | | 5 | 55 | | 6 | 22 | | 7 | 20 | | 8 | 22 | | 9 | 28 | | 10 | 24 | | 11 | 42 | | 12 | 21 | | 13 | 14 | | 14 | 33 | | 15 | 31 | | 16 | 19 | | 17 | 23 | | 18 | 19 | | 19 | 3 | | 20 | 12 | | 21 | 19 | | 22 | 8 | | 23 | 7 | | 24 | 18 | | 25 | 31 | | 26 | 10 | | 27 | 17 | | 28 | 20 | | 29 | 28 | | 30 | 18 | | 31 | 14 | | 32 | 7 | | 33 | 14 | | 34 | 15 | | 35 | 41 | | 36 | 24 | | 37 | 17 | | 38 | 17 | | 39 | 3 | | 40 | 22 | | 41 | 23 | | 42 | 31 | | 43 | 29 | | 44 | 20 | | 45 | 16 | | 46 | 34 |
| |
| 45.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3617021276595745 | | totalSentences | 47 | | uniqueOpeners | 17 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 80.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 40 | | matches | | 0 | "She’s been here twenty minutes," | | 1 | "She walks over, her sharp" | | 2 | "His hands are dusted with" | | 3 | "He picks up the syringe," | | 4 | "She kneels back down, pries" | | 5 | "She doesn’t want Torres to" | | 6 | "She nods at the victim’s" | | 7 | "He pulls out a small," | | 8 | "He turns it over in" | | 9 | "He trails off, and Harlow’s" | | 10 | "She knows what he’s going" | | 11 | "She was there, standing outside" | | 12 | "She can feel the hair" | | 13 | "She pulls her own weapon," |
| | ratio | 0.35 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 40 | | matches | | 0 | "Harlow’s boot scuffs a shard" | | 1 | "The worn leather watch on" | | 2 | "The fluid pooled under the" | | 3 | "She’s been here twenty minutes," | | 4 | "DC Mike Torres leans against" | | 5 | "Harlow stands, brushing flecks of" | | 6 | "She walks over, her sharp" | | 7 | "The victim is a middle-aged" | | 8 | "His hands are dusted with" | | 9 | "Torres kicks a discarded plastic" | | 10 | "He picks up the syringe," | | 11 | "The liquid inside glows a" | | 12 | "She kneels back down, pries" | | 13 | "A small brass compass clatters" | | 14 | "Eva had shown her those" | | 15 | "Harlow tucks the compass into" | | 16 | "She doesn’t want Torres to" | | 17 | "She nods at the victim’s" | | 18 | "Torres raises an eyebrow, but" | | 19 | "He pulls out a small," |
| | ratio | 1 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 3 | | matches | | 0 | "She’s been here twenty minutes, and every time she blinks, she swears she sees a flicker of something out of the corner of her eye: a shadow that doesn’t match …" | | 1 | "She pulls her own weapon, her finger resting on the trigger, her body tensed into the military precision she’s spent 18 years honing." | | 2 | "Harlow’s leather watch ticks-tocks on her wrist, the second hand moving forward slow and steady, as she prepares to do what she couldn’t do three years ago: cat…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |