| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 34 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 185 | | tagDensity | 0.184 | | leniency | 0.368 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2446 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 95.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2446 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 158 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 158 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 309 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2446 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 45 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 70 | | wordCount | 1420 | | uniqueNames | 9 | | maxNameDensity | 1.97 | | worstName | "Morgan" | | maxWindowNameDensity | 4 | | worstWindowName | "Morgan" | | discoveredNames | | Cardiff | 1 | | University | 1 | | Morgan | 28 | | Price | 1 | | Raven | 1 | | Nest | 1 | | Silas | 12 | | Aurora | 24 | | London | 1 |
| | persons | | 0 | "Morgan" | | 1 | "Price" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Aurora" |
| | places | | 0 | "Cardiff" | | 1 | "University" | | 2 | "London" |
| | globalScore | 0.514 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | 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 | 2446 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 309 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 208 | | mean | 11.76 | | std | 12.64 | | cv | 1.075 | | sampleLengths | | 0 | 12 | | 1 | 52 | | 2 | 10 | | 3 | 7 | | 4 | 1 | | 5 | 71 | | 6 | 16 | | 7 | 6 | | 8 | 13 | | 9 | 56 | | 10 | 7 | | 11 | 14 | | 12 | 3 | | 13 | 4 | | 14 | 4 | | 15 | 17 | | 16 | 6 | | 17 | 5 | | 18 | 7 | | 19 | 12 | | 20 | 20 | | 21 | 39 | | 22 | 4 | | 23 | 20 | | 24 | 6 | | 25 | 5 | | 26 | 18 | | 27 | 2 | | 28 | 13 | | 29 | 14 | | 30 | 25 | | 31 | 6 | | 32 | 4 | | 33 | 17 | | 34 | 9 | | 35 | 4 | | 36 | 3 | | 37 | 12 | | 38 | 5 | | 39 | 6 | | 40 | 3 | | 41 | 51 | | 42 | 8 | | 43 | 1 | | 44 | 2 | | 45 | 1 | | 46 | 3 | | 47 | 8 | | 48 | 2 | | 49 | 22 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 158 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 254 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 309 | | ratio | 0.006 | | matches | | 0 | "The green neon sign over the entrance had laid a stripe across the wet pavement outside; inside, old maps and black-and-white photographs crowded the walls." | | 1 | "It burnt her fingers; she held it above the plate until she could eat it." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1425 | | adjectiveStacks | 1 | | stackExamples | | 0 | "outside; inside, old maps" |
| | adverbCount | 30 | | adverbRatio | 0.021052631578947368 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 309 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 309 | | mean | 7.92 | | std | 5.46 | | cv | 0.69 | | sampleLengths | | 0 | 12 | | 1 | 15 | | 2 | 13 | | 3 | 8 | | 4 | 16 | | 5 | 10 | | 6 | 7 | | 7 | 1 | | 8 | 5 | | 9 | 14 | | 10 | 28 | | 11 | 13 | | 12 | 11 | | 13 | 13 | | 14 | 3 | | 15 | 6 | | 16 | 13 | | 17 | 5 | | 18 | 10 | | 19 | 25 | | 20 | 16 | | 21 | 7 | | 22 | 11 | | 23 | 3 | | 24 | 3 | | 25 | 4 | | 26 | 4 | | 27 | 4 | | 28 | 13 | | 29 | 4 | | 30 | 2 | | 31 | 5 | | 32 | 2 | | 33 | 5 | | 34 | 8 | | 35 | 4 | | 36 | 7 | | 37 | 13 | | 38 | 10 | | 39 | 22 | | 40 | 7 | | 41 | 4 | | 42 | 8 | | 43 | 12 | | 44 | 6 | | 45 | 5 | | 46 | 9 | | 47 | 9 | | 48 | 2 | | 49 | 13 |
| |
| 43.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.2297734627831715 | | totalSentences | 309 | | uniqueOpeners | 71 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 133 | | matches | (empty) | | ratio | 0 | |
| 87.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 133 | | matches | | 0 | "He looked up when the" | | 1 | "He set the pen down." | | 2 | "His hair had gone almost" | | 3 | "His gaze dropped to the" | | 4 | "He touched the napkin with" | | 5 | "He folded both hands under" | | 6 | "He tipped his head towards" | | 7 | "She looked at the napkin" | | 8 | "He shifted the coins." | | 9 | "He turned his mug by" | | 10 | "He would grin until someone" | | 11 | "She couldn’t picture him retired." | | 12 | "He lifted his mug." | | 13 | "He nodded once, as if" | | 14 | "He took his tea this" | | 15 | "He would have performed the" | | 16 | "She would have preferred one." | | 17 | "She set the napkin back." | | 18 | "She had stood at the" | | 19 | "She felt the start of" |
| | ratio | 0.331 | |
| 23.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 133 | | matches | | 0 | "The man in the corner" | | 1 | "Aurora counted seven from the" | | 2 | "Another carried three phone numbers," | | 3 | "The man leaned over a" | | 4 | "He looked up when the" | | 5 | "He set the pen down." | | 6 | "His hair had gone almost" | | 7 | "The man at the table" | | 8 | "A thin pale line ran" | | 9 | "His gaze dropped to the" | | 10 | "He touched the napkin with" | | 11 | "Aurora crossed the Raven’s Nest." | | 12 | "Rain followed her in, darkening" | | 13 | "The green neon sign over" | | 14 | "Silas stood behind the bar" | | 15 | "Aurora told him" | | 16 | "Silas glanced at the bag" | | 17 | "Silas took the bag." | | 18 | "Morgan raised one hand." | | 19 | "Silas told him" |
| | ratio | 0.872 | |
| 37.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 133 | | matches | | 0 | "Now he examined the brown" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 1 | | matches | | 0 | "At university, Morgan Price had worn his dark curls past his collar and talked through lectures as if the rest of the room had come to hear him." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 34 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 24 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 185 | | tagDensity | 0.13 | | leniency | 0.259 | | rawRatio | 0 | | effectiveRatio | 0 | |