| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 749 | | 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) | |
| 26.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 749 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "weight" | | 1 | "familiar" | | 2 | "pulse" | | 3 | "flicker" | | 4 | "electric" | | 5 | "stomach" | | 6 | "pulsed" | | 7 | "whisper" | | 8 | "scanned" | | 9 | "flicked" |
| |
| 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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 94.02% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 10 | | markdownWords | 39 | | totalWords | 736 | | ratio | 0.053 | | matches | | 0 | "Red Lion" | | 1 | "robe" | | 2 | "“Detective Quinn.”" | | 3 | "“You shouldn’t be here,”" | | 4 | "“He’s gone,”" | | 5 | "“But you’re not.”" | | 6 | "“Someone who knows things.”" | | 7 | "“You’re looking for the wrong man.”" | | 8 | "“The one who didn’t lead you here. The one who sent you.”" | | 9 | "“Who sent me?”" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 695 | | uniqueNames | 4 | | maxNameDensity | 2.01 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | | persons | | | places | | | globalScore | 0.493 | | windowScore | 0.333 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "as if reading her mind" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 736 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 101 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 18.4 | | std | 17.63 | | cv | 0.958 | | sampleLengths | | 0 | 55 | | 1 | 3 | | 2 | 45 | | 3 | 8 | | 4 | 47 | | 5 | 45 | | 6 | 62 | | 7 | 11 | | 8 | 7 | | 9 | 29 | | 10 | 46 | | 11 | 3 | | 12 | 21 | | 13 | 67 | | 14 | 22 | | 15 | 15 | | 16 | 2 | | 17 | 35 | | 18 | 7 | | 19 | 9 | | 20 | 13 | | 21 | 6 | | 22 | 21 | | 23 | 8 | | 24 | 16 | | 25 | 12 | | 26 | 5 | | 27 | 16 | | 28 | 2 | | 29 | 13 | | 30 | 5 | | 31 | 25 | | 32 | 5 | | 33 | 5 | | 34 | 9 | | 35 | 6 | | 36 | 12 | | 37 | 6 | | 38 | 5 | | 39 | 7 |
| |
| 90.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 95 | | matches | | 0 | "was gone" | | 1 | "been buried" | | 2 | "being played" | | 3 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 123 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 101 | | ratio | 0.079 | | matches | | 0 | "The suspect—a wiry man in a long coat—ducked into the alley between two brick buildings, his movements sharp, desperate." | | 1 | "She’d seen those before—at the Veil Market, where things that shouldn’t exist changed hands." | | 2 | "She descended, her boots clanging against the metal, the stench of stagnant water and something fouler—rot, decay—hitting her like a fist." | | 3 | "Then—nothing." | | 4 | "Then she saw it—a faint glow, pulsing like a slow, sick pulse." | | 5 | "The tunnel opened into a cavernous space, the air thick with the scent of incense and something sharper—ozone, like before a storm." | | 6 | "A man in a long coat—no, a *robe*—haggling over a skull that pulsed faintly, veins of blue light threading through its bone." | | 7 | "Then—a whisper." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 708 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.03248587570621469 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00847457627118644 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 7.29 | | std | 5.66 | | cv | 0.776 | | sampleLengths | | 0 | 17 | | 1 | 19 | | 2 | 19 | | 3 | 3 | | 4 | 22 | | 5 | 11 | | 6 | 8 | | 7 | 3 | | 8 | 1 | | 9 | 8 | | 10 | 13 | | 11 | 12 | | 12 | 5 | | 13 | 17 | | 14 | 19 | | 15 | 3 | | 16 | 6 | | 17 | 3 | | 18 | 14 | | 19 | 3 | | 20 | 20 | | 21 | 3 | | 22 | 11 | | 23 | 4 | | 24 | 21 | | 25 | 7 | | 26 | 4 | | 27 | 4 | | 28 | 2 | | 29 | 1 | | 30 | 12 | | 31 | 4 | | 32 | 3 | | 33 | 10 | | 34 | 15 | | 35 | 14 | | 36 | 12 | | 37 | 3 | | 38 | 2 | | 39 | 3 | | 40 | 10 | | 41 | 2 | | 42 | 2 | | 43 | 3 | | 44 | 4 | | 45 | 22 | | 46 | 12 | | 47 | 4 | | 48 | 7 | | 49 | 22 |
| |
| 43.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.31683168316831684 | | totalSentences | 101 | | uniqueOpeners | 32 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 78 | | matches | | 0 | "Then she saw it—a faint" | | 1 | "Then she was gone, swallowed" |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 78 | | matches | | 0 | "She didn’t hesitate." | | 1 | "She ignored it." | | 2 | "He bolted left, nearly colliding" | | 3 | "He vanished around the corner" | | 4 | "She rounded the corner just" | | 5 | "Her pulse spiked." | | 6 | "She’d seen those before—at the" | | 7 | "She grabbed the ladder, her" | | 8 | "She descended, her boots clanging" | | 9 | "She chose left, her instincts" | | 10 | "She stepped forward, her boots" | | 11 | "Her stomach twisted." | | 12 | "She didn’t look down." | | 13 | "She wasn’t here for the" | | 14 | "She was here for the" | | 15 | "She moved deeper, her flashlight" | | 16 | "She scanned the crowd again." | | 17 | "She stepped closer, her voice" | | 18 | "She didn’t like being played." | | 19 | "He didn’t move." |
| | ratio | 0.282 | |
| 30.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 78 | | matches | | 0 | "The rain hammered down like" | | 1 | "Harlow Quinn’s boots splashed through" | | 2 | "The suspect—a wiry man in" | | 3 | "She didn’t hesitate." | | 4 | "Quinn lunged after him, her" | | 5 | "The alley reeked of damp" | | 6 | "A discarded hypodermic needle glinted" | | 7 | "She ignored it." | | 8 | "The man was fast, but" | | 9 | "He bolted left, nearly colliding" | | 10 | "Quinn skidded to a stop," | | 11 | "The suspect didn’t look back." | | 12 | "He vanished around the corner" | | 13 | "She rounded the corner just" | | 14 | "A bone token." | | 15 | "Her pulse spiked." | | 16 | "She’d seen those before—at the" | | 17 | "The grate rattled." | | 18 | "The man was already halfway" | | 19 | "Quinn didn’t think." |
| | ratio | 0.859 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 66.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 3 | | matches | | 0 | "Harlow Quinn’s boots splashed through puddles that reflected the sickly glow of streetlamps, her breath fogging in the cold." | | 1 | "She descended, her boots clanging against the metal, the stench of stagnant water and something fouler—rot, decay—hitting her like a fist." | | 2 | "Dark hair, sharp cheekbones, a silver ring on her thumb that glinted in the low light." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "the woman said, as if reading her mind" | | 1 | "She stepped, her voice dropping" |
| |
| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 10 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.333 | | effectiveRatio | 0.2 | |