| 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 | 1366 | | 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) | |
| 96.34% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1366 | | totalAiIsms | 1 | | 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 | 136 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 136 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 173 | | 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 | 1366 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.34% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1103 | | uniqueNames | 15 | | maxNameDensity | 1.81 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Tomás" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Harlow | 20 | | Quinn | 1 | | Morris | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Rain | 3 | | Tomás | 6 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Morris" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Rain" | | 9 | "Tomás" |
| | places | | 0 | "Shaftesbury" | | 1 | "Avenue" | | 2 | "Camden" | | 3 | "High" | | 4 | "Street" |
| | globalScore | 0.593 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | 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 | 1366 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 173 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 80 | | mean | 17.08 | | std | 18.73 | | cv | 1.097 | | sampleLengths | | 0 | 65 | | 1 | 6 | | 2 | 58 | | 3 | 5 | | 4 | 6 | | 5 | 2 | | 6 | 9 | | 7 | 7 | | 8 | 5 | | 9 | 21 | | 10 | 5 | | 11 | 2 | | 12 | 38 | | 13 | 1 | | 14 | 41 | | 15 | 34 | | 16 | 56 | | 17 | 3 | | 18 | 8 | | 19 | 8 | | 20 | 5 | | 21 | 7 | | 22 | 4 | | 23 | 3 | | 24 | 65 | | 25 | 62 | | 26 | 54 | | 27 | 10 | | 28 | 2 | | 29 | 52 | | 30 | 29 | | 31 | 3 | | 32 | 68 | | 33 | 3 | | 34 | 1 | | 35 | 44 | | 36 | 6 | | 37 | 49 | | 38 | 7 | | 39 | 7 | | 40 | 3 | | 41 | 4 | | 42 | 3 | | 43 | 34 | | 44 | 15 | | 45 | 15 | | 46 | 6 | | 47 | 4 | | 48 | 17 | | 49 | 13 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 136 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 200 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 173 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1105 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.00904977375565611 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009049773755656109 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 173 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 173 | | mean | 7.9 | | std | 5.38 | | cv | 0.682 | | sampleLengths | | 0 | 13 | | 1 | 13 | | 2 | 29 | | 3 | 6 | | 4 | 4 | | 5 | 6 | | 6 | 8 | | 7 | 5 | | 8 | 19 | | 9 | 4 | | 10 | 22 | | 11 | 5 | | 12 | 6 | | 13 | 2 | | 14 | 9 | | 15 | 7 | | 16 | 5 | | 17 | 6 | | 18 | 9 | | 19 | 6 | | 20 | 5 | | 21 | 2 | | 22 | 6 | | 23 | 21 | | 24 | 11 | | 25 | 1 | | 26 | 11 | | 27 | 2 | | 28 | 12 | | 29 | 5 | | 30 | 11 | | 31 | 7 | | 32 | 3 | | 33 | 3 | | 34 | 21 | | 35 | 4 | | 36 | 7 | | 37 | 6 | | 38 | 18 | | 39 | 2 | | 40 | 8 | | 41 | 11 | | 42 | 3 | | 43 | 8 | | 44 | 8 | | 45 | 5 | | 46 | 7 | | 47 | 4 | | 48 | 3 | | 49 | 12 |
| |
| 50.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3352601156069364 | | totalSentences | 173 | | uniqueOpeners | 58 | |
| 53.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 124 | | matches | | 0 | "Only the slot, dark and" | | 1 | "Then he reached into his" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 124 | | matches | | 0 | "She left it alone." | | 1 | "His hood shadowed his face." | | 2 | "He carried a brown parcel" | | 3 | "She had seen it in" | | 4 | "She lifted the radio handset." | | 5 | "He looked straight at the" | | 6 | "He crossed the street, vaulted" | | 7 | "His boot kicked the starter." | | 8 | "She pulled out hard." | | 9 | "She corrected, felt the road" | | 10 | "She watched the bike's tail" | | 11 | "She ended the call and" | | 12 | "His voice vanished behind her." | | 13 | "Its rear wheel locked." | | 14 | "She left the engine running" | | 15 | "Her salt-and-pepper hair flattened to" | | 16 | "She drew her baton, not" | | 17 | "She reached the bottom of" | | 18 | "His face was young, frightened," | | 19 | "He mouthed a word she" |
| | ratio | 0.25 | |
| 0.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 114 | | totalSentences | 124 | | matches | | 0 | "The green neon sign of" | | 1 | "Light pooled on the wet" | | 2 | "Detective Harlow Quinn sat behind" | | 3 | "A cigarette burned in the" | | 4 | "She left it alone." | | 5 | "The door of the bar" | | 6 | "A man in a grey" | | 7 | "His hood shadowed his face." | | 8 | "He carried a brown parcel" | | 9 | "Harlow knew that turn." | | 10 | "She had seen it in" | | 11 | "She lifted the radio handset." | | 12 | "Harlow kept her eyes on" | | 13 | "The man stopped at the" | | 14 | "Rain ran from his hood" | | 15 | "He looked straight at the" | | 16 | "Harlow reached for the handle." | | 17 | "The parcel vanished under his" | | 18 | "He crossed the street, vaulted" | | 19 | "Harlow hit the door with" |
| | ratio | 0.919 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 124 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | 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 | |