| 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 | 1613 | | 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) | |
| 93.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1613 | | 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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 151 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 217 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1613 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.62% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1269 | | uniqueNames | 5 | | maxNameDensity | 2.13 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 27 | | Herrera | 21 | | Raven | 2 | | Nest | 2 | | London | 1 |
| | persons | | | places | | | globalScore | 0.436 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 106 | | 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 | 1613 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 217 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 152 | | mean | 10.61 | | std | 10.84 | | cv | 1.022 | | sampleLengths | | 0 | 18 | | 1 | 8 | | 2 | 5 | | 3 | 36 | | 4 | 30 | | 5 | 3 | | 6 | 2 | | 7 | 1 | | 8 | 16 | | 9 | 2 | | 10 | 1 | | 11 | 42 | | 12 | 9 | | 13 | 4 | | 14 | 7 | | 15 | 4 | | 16 | 29 | | 17 | 5 | | 18 | 2 | | 19 | 31 | | 20 | 5 | | 21 | 12 | | 22 | 1 | | 23 | 3 | | 24 | 9 | | 25 | 4 | | 26 | 10 | | 27 | 19 | | 28 | 33 | | 29 | 2 | | 30 | 11 | | 31 | 7 | | 32 | 5 | | 33 | 4 | | 34 | 8 | | 35 | 5 | | 36 | 4 | | 37 | 1 | | 38 | 8 | | 39 | 1 | | 40 | 7 | | 41 | 1 | | 42 | 25 | | 43 | 28 | | 44 | 3 | | 45 | 8 | | 46 | 4 | | 47 | 5 | | 48 | 44 | | 49 | 40 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 151 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 224 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 217 | | ratio | 0.005 | | matches | | 0 | "Traders had built counters across the old platform; lamps illuminated jars, bundled roots, surgical instruments and packets tied with black string." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1269 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.015760441292356184 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0007880220646178094 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 217 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 217 | | mean | 7.43 | | std | 4.95 | | cv | 0.666 | | sampleLengths | | 0 | 18 | | 1 | 8 | | 2 | 5 | | 3 | 8 | | 4 | 14 | | 5 | 14 | | 6 | 2 | | 7 | 16 | | 8 | 12 | | 9 | 3 | | 10 | 2 | | 11 | 1 | | 12 | 6 | | 13 | 10 | | 14 | 2 | | 15 | 1 | | 16 | 10 | | 17 | 10 | | 18 | 3 | | 19 | 19 | | 20 | 4 | | 21 | 5 | | 22 | 4 | | 23 | 7 | | 24 | 4 | | 25 | 29 | | 26 | 5 | | 27 | 2 | | 28 | 5 | | 29 | 20 | | 30 | 6 | | 31 | 5 | | 32 | 12 | | 33 | 1 | | 34 | 3 | | 35 | 9 | | 36 | 4 | | 37 | 10 | | 38 | 11 | | 39 | 8 | | 40 | 4 | | 41 | 14 | | 42 | 15 | | 43 | 2 | | 44 | 5 | | 45 | 6 | | 46 | 7 | | 47 | 5 | | 48 | 4 | | 49 | 8 |
| |
| 55.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3456221198156682 | | totalSentences | 217 | | uniqueOpeners | 75 | |
| 47.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 139 | | matches | | 0 | "Then he turned and disappeared" | | 1 | "Then she wedged the door" |
| | ratio | 0.014 | |
| 99.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 139 | | matches | | 0 | "He cut between the delivery" | | 1 | "She pressed her radio." | | 2 | "She repeated it while running." | | 3 | "Her umbrella tipped into the" | | 4 | "His head turned." | | 5 | "He ducked into a passage" | | 6 | "She pressed against the brick," | | 7 | "He looked back at her." | | 8 | "His chest heaved beneath his" | | 9 | "He raised his bleeding hand." | | 10 | "He stamped on the bottom" | | 11 | "He slipped through the opening" | | 12 | "Her radio hissed." | | 13 | "She stepped through the gate." | | 14 | "She crossed beneath a fire" | | 15 | "She seized its bottom edge" | | 16 | "His shoes scraped concrete beyond" | | 17 | "He had rain caught in" | | 18 | "She rolled to one knee." | | 19 | "She lunged for his wrist." |
| | ratio | 0.302 | |
| 42.73% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 139 | | matches | | 0 | "The man came through the" | | 1 | "Quinn stepped out from beneath" | | 2 | "Rain struck the pavement hard" | | 3 | "The man looked at Quinn’s" | | 4 | "Blood ran from his left" | | 5 | "Quinn glanced through the shattered" | | 6 | "A pair of shoes stuck" | | 7 | "He cut between the delivery" | | 8 | "Quinn followed, splashing through water" | | 9 | "A horn blared." | | 10 | "Herrera slapped the bonnet of" | | 11 | "Quinn went behind it." | | 12 | "The driver lowered his window." | | 13 | "She pressed her radio." | | 14 | "Static cracked against her ear." | | 15 | "She repeated it while running." | | 16 | "Her umbrella tipped into the" | | 17 | "Quinn gained six paces while" | | 18 | "His head turned." | | 19 | "He ducked into a passage" |
| | ratio | 0.835 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 139 | | matches | | 0 | "By the time she freed" | | 1 | "Now he had a case" | | 2 | "If she left the doorway" |
| | ratio | 0.022 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |