| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1379 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 23.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1379 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "measured" | | 1 | "shattered" | | 2 | "traced" | | 3 | "mechanical" | | 4 | "footsteps" | | 5 | "echoed" | | 6 | "familiar" | | 7 | "porcelain" | | 8 | "mosaic" | | 9 | "scanned" | | 10 | "shimmered" | | 11 | "vibrated" | | 12 | "etched" | | 13 | "standard" | | 14 | "grave" | | 15 | "flickered" | | 16 | "velvet" |
| |
| 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 | 225 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 225 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 225 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1379 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1379 | | uniqueNames | 17 | | maxNameDensity | 0.87 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | High | 1 | | Street | 1 | | Quinn | 12 | | Metropolitan | 2 | | Morris | 5 | | Police | 1 | | Herrera | 8 | | Saint | 2 | | Christopher | 2 | | Detective | 2 | | Town | 1 | | Veil | 2 | | Market | 2 | | Water | 4 | | You | 6 | | Pulled | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Market" | | 6 | "Water" | | 7 | "You" | | 8 | "Pulled" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Town" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | 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 | 1379 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 225 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 27.04 | | std | 26.16 | | cv | 0.968 | | sampleLengths | | 0 | 112 | | 1 | 44 | | 2 | 4 | | 3 | 16 | | 4 | 44 | | 5 | 9 | | 6 | 49 | | 7 | 6 | | 8 | 10 | | 9 | 27 | | 10 | 10 | | 11 | 15 | | 12 | 6 | | 13 | 30 | | 14 | 61 | | 15 | 33 | | 16 | 5 | | 17 | 30 | | 18 | 104 | | 19 | 35 | | 20 | 2 | | 21 | 3 | | 22 | 2 | | 23 | 57 | | 24 | 37 | | 25 | 5 | | 26 | 31 | | 27 | 66 | | 28 | 15 | | 29 | 9 | | 30 | 6 | | 31 | 51 | | 32 | 56 | | 33 | 14 | | 34 | 10 | | 35 | 3 | | 36 | 4 | | 37 | 8 | | 38 | 5 | | 39 | 3 | | 40 | 59 | | 41 | 2 | | 42 | 35 | | 43 | 72 | | 44 | 13 | | 45 | 14 | | 46 | 10 | | 47 | 16 | | 48 | 65 | | 49 | 39 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 225 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 268 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 225 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1381 | | adjectiveStacks | 2 | | stackExamples | | 0 | "pressed against damp brick." | | 1 | "steady, pressing against her" |
| | adverbCount | 20 | | adverbRatio | 0.01448225923244026 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002172338884866039 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 225 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 225 | | mean | 6.13 | | std | 3.89 | | cv | 0.634 | | sampleLengths | | 0 | 8 | | 1 | 10 | | 2 | 10 | | 3 | 11 | | 4 | 20 | | 5 | 7 | | 6 | 15 | | 7 | 21 | | 8 | 10 | | 9 | 8 | | 10 | 8 | | 11 | 8 | | 12 | 10 | | 13 | 10 | | 14 | 2 | | 15 | 2 | | 16 | 6 | | 17 | 5 | | 18 | 5 | | 19 | 4 | | 20 | 3 | | 21 | 14 | | 22 | 8 | | 23 | 6 | | 24 | 9 | | 25 | 3 | | 26 | 6 | | 27 | 7 | | 28 | 2 | | 29 | 12 | | 30 | 7 | | 31 | 8 | | 32 | 7 | | 33 | 4 | | 34 | 2 | | 35 | 6 | | 36 | 4 | | 37 | 6 | | 38 | 5 | | 39 | 11 | | 40 | 5 | | 41 | 6 | | 42 | 5 | | 43 | 5 | | 44 | 3 | | 45 | 6 | | 46 | 6 | | 47 | 4 | | 48 | 2 | | 49 | 10 |
| |
| 60.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.4177777777777778 | | totalSentences | 225 | | uniqueOpeners | 94 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 192 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 54 | | totalSentences | 192 | | matches | | 0 | "She kept her left wrist" | | 1 | "She would not let this" | | 2 | "He vaulted a rusted bicycle" | | 3 | "His shoulder clipped an overflowing" | | 4 | "He did not look back." | | 5 | "Her lungs burned." | | 6 | "She tracked his footing, anticipated" | | 7 | "He darted left into a" | | 8 | "You’re done, Herrera." | | 9 | "He spun, back pressed against" | | 10 | "His warm brown eyes locked" | | 11 | "You’re out of your jurisdiction," | | 12 | "I’m out of patience." | | 13 | "His boot heels scraped metal." | | 14 | "He dropped to one knee," | | 15 | "Her hand went to her" | | 16 | "He slid into the gap," | | 17 | "She gripped the top rung." | | 18 | "She lowered herself, boots finding" | | 19 | "Her watch ticked against her" |
| | ratio | 0.281 | |
| 53.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 156 | | totalSentences | 192 | | matches | | 0 | "Water cascaded from wrought iron" | | 1 | "Harlow Quinn’s boots struck the" | | 2 | "Rain dripped from her closely" | | 3 | "She kept her left wrist" | | 4 | "The worn leather strap of" | | 5 | "She would not let this" | | 6 | "The figure ahead cut through" | | 7 | "Spray caught the orange glow" | | 8 | "Olive skin flashed under a" | | 9 | "He vaulted a rusted bicycle" | | 10 | "His shoulder clipped an overflowing" | | 11 | "Glass shattered across the cobbles." | | 12 | "He did not look back." | | 13 | "Quinn closed the distance." | | 14 | "Her lungs burned." | | 15 | "She tracked his footing, anticipated" | | 16 | "He darted left into a" | | 17 | "Brick walls swallowed the street" | | 18 | "The rain fell harder, drumming" | | 19 | "You’re done, Herrera." |
| | ratio | 0.813 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 192 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 1 | | matches | | 0 | "A labyrinth of unknown faces, unregistered transactions, things that did not belong in any evidence locker." |
| |
| 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 | |