| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 88.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1250 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "loosely" | | 1 | "slightly" | | 2 | "slowly" |
| |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 52.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1250 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "tracing" | | 1 | "unreadable" | | 2 | "chill" | | 3 | "scanned" | | 4 | "silence" | | 5 | "trembled" | | 6 | "tension" | | 7 | "pulsed" | | 8 | "etched" | | 9 | "weight" | | 10 | "echoed" |
| |
| 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 | 203 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 203 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 203 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1250 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 82 | | wordCount | 1250 | | uniqueNames | 12 | | maxNameDensity | 2.24 | | worstName | "Aurora" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | London | 2 | | Carter | 1 | | Blackwood | 1 | | Aurora | 28 | | Cardiff | 2 | | Eva | 23 | | Europe | 1 | | Rory | 1 | | Silas | 10 | | You | 11 |
| | persons | | 0 | "Carter" | | 1 | "Blackwood" | | 2 | "Aurora" | | 3 | "Eva" | | 4 | "Rory" | | 5 | "Silas" | | 6 | "You" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "Europe" |
| | globalScore | 0.38 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like veins under skin" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1250 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 203 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 116 | | mean | 10.78 | | std | 11.77 | | cv | 1.092 | | sampleLengths | | 0 | 91 | | 1 | 45 | | 2 | 22 | | 3 | 45 | | 4 | 13 | | 5 | 19 | | 6 | 3 | | 7 | 35 | | 8 | 3 | | 9 | 13 | | 10 | 5 | | 11 | 28 | | 12 | 9 | | 13 | 1 | | 14 | 25 | | 15 | 9 | | 16 | 7 | | 17 | 24 | | 18 | 4 | | 19 | 15 | | 20 | 7 | | 21 | 14 | | 22 | 40 | | 23 | 10 | | 24 | 4 | | 25 | 1 | | 26 | 13 | | 27 | 11 | | 28 | 9 | | 29 | 10 | | 30 | 5 | | 31 | 17 | | 32 | 4 | | 33 | 3 | | 34 | 27 | | 35 | 11 | | 36 | 3 | | 37 | 4 | | 38 | 8 | | 39 | 8 | | 40 | 11 | | 41 | 8 | | 42 | 3 | | 43 | 28 | | 44 | 6 | | 45 | 15 | | 46 | 3 | | 47 | 12 | | 48 | 1 | | 49 | 6 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 203 | | matches | | 0 | "were scuffed" | | 1 | "got involved" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 246 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 203 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1254 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.019936204146730464 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004784688995215311 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 203 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 203 | | mean | 6.16 | | std | 4.06 | | cv | 0.659 | | sampleLengths | | 0 | 15 | | 1 | 18 | | 2 | 24 | | 3 | 18 | | 4 | 16 | | 5 | 13 | | 6 | 14 | | 7 | 18 | | 8 | 6 | | 9 | 16 | | 10 | 4 | | 11 | 18 | | 12 | 14 | | 13 | 9 | | 14 | 7 | | 15 | 6 | | 16 | 5 | | 17 | 3 | | 18 | 11 | | 19 | 3 | | 20 | 9 | | 21 | 5 | | 22 | 8 | | 23 | 13 | | 24 | 3 | | 25 | 7 | | 26 | 6 | | 27 | 5 | | 28 | 6 | | 29 | 16 | | 30 | 6 | | 31 | 4 | | 32 | 5 | | 33 | 1 | | 34 | 8 | | 35 | 4 | | 36 | 13 | | 37 | 4 | | 38 | 5 | | 39 | 7 | | 40 | 7 | | 41 | 7 | | 42 | 10 | | 43 | 3 | | 44 | 1 | | 45 | 2 | | 46 | 13 | | 47 | 2 | | 48 | 5 | | 49 | 5 |
| |
| 41.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.21182266009852216 | | totalSentences | 203 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 174 | | matches | (empty) | | ratio | 0 | |
| 40.69% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 78 | | totalSentences | 174 | | matches | | 0 | "His hazel eyes tracked the" | | 1 | "He moved with a slight" | | 2 | "She wore a trench coat" | | 3 | "Her hair, once a vibrant" | | 4 | "She scanned the room, her" | | 5 | "She didn't smile." | | 6 | "You look different." | | 7 | "She stopped two paces away." | | 8 | "You left without a word." | | 9 | "She pulled out a pack" | | 10 | "She shoved the pack back" | | 11 | "I had to go." | | 12 | "She watched Eva's hands." | | 13 | "They trembled slightly, a fine" | | 14 | "You brought me here." | | 15 | "You said London was safe." | | 16 | "I said it was a" | | 17 | "She faced the maps on" | | 18 | "You disappeared six months ago." | | 19 | "I called the number you" |
| | ratio | 0.448 | |
| 5.98% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 158 | | totalSentences | 174 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Maps lined the walls, their" | | 2 | "Aurora Carter leaned against the" | | 3 | "The scar on her left" | | 4 | "Silas Blackwood wiped a tumbler" | | 5 | "His hazel eyes tracked the" | | 6 | "He moved with a slight" | | 7 | "The bell above the entrance" | | 8 | "A gust of cold wind" | | 9 | "A woman stepped inside." | | 10 | "She wore a trench coat" | | 11 | "Her hair, once a vibrant" | | 12 | "She scanned the room, her" | | 13 | "Eva pulled the door shut" | | 14 | "The silence in the bar" | | 15 | "Aurora pushed off the counter." | | 16 | "She didn't smile." | | 17 | "The distance between them stretched" | | 18 | "You look different." | | 19 | "Eva walked forward, her boots" |
| | ratio | 0.908 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 174 | | matches | | 0 | "If you're in trouble." | | 1 | "If you are." | | 2 | "If I tell you, they" | | 3 | "Because the walls are closing." | | 4 | "To a place she can't" |
| | ratio | 0.029 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "Maps lined the walls, their ink faded by decades of London damp, while black-and-white photographs captured strangers who had long since turned to dust." |
| |
| 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 | |