| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 1 | | adverbTags | | 0 | "he answered finally [finally]" |
| | dialogueSentences | 65 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.038 | | effectiveRatio | 0.031 | |
| 92.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1362 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 37.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1362 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "gloom" | | 1 | "silence" | | 2 | "shattered" | | 3 | "weight" | | 4 | "maw" | | 5 | "scanned" | | 6 | "trembled" | | 7 | "echoed" | | 8 | "vibrated" | | 9 | "scanning" | | 10 | "flickered" | | 11 | "shimmered" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "knuckles turned white" | | count | 1 |
| | 2 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "knuckles turned white" | | 2 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 164 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 164 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 202 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1362 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1072 | | uniqueNames | 7 | | maxNameDensity | 3.08 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 33 | | Tube | 1 | | Tomás | 8 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0 | | windowScore | 0.167 | |
| 91.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like they hadn't seen a coin in ye" | | 1 | "looked like a carriage from another centu" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1362 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 202 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 102 | | mean | 13.35 | | std | 10.7 | | cv | 0.801 | | sampleLengths | | 0 | 55 | | 1 | 8 | | 2 | 13 | | 3 | 32 | | 4 | 56 | | 5 | 18 | | 6 | 10 | | 7 | 9 | | 8 | 7 | | 9 | 15 | | 10 | 5 | | 11 | 13 | | 12 | 18 | | 13 | 27 | | 14 | 20 | | 15 | 3 | | 16 | 11 | | 17 | 19 | | 18 | 21 | | 19 | 10 | | 20 | 7 | | 21 | 19 | | 22 | 25 | | 23 | 6 | | 24 | 28 | | 25 | 27 | | 26 | 15 | | 27 | 13 | | 28 | 5 | | 29 | 11 | | 30 | 12 | | 31 | 16 | | 32 | 6 | | 33 | 16 | | 34 | 25 | | 35 | 4 | | 36 | 10 | | 37 | 16 | | 38 | 2 | | 39 | 12 | | 40 | 14 | | 41 | 12 | | 42 | 8 | | 43 | 3 | | 44 | 26 | | 45 | 3 | | 46 | 3 | | 47 | 2 | | 48 | 18 | | 49 | 14 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 164 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 207 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 202 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1074 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.01675977653631285 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0074487895716946 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 202 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 202 | | mean | 6.74 | | std | 3.86 | | cv | 0.572 | | sampleLengths | | 0 | 8 | | 1 | 13 | | 2 | 15 | | 3 | 19 | | 4 | 3 | | 5 | 5 | | 6 | 7 | | 7 | 6 | | 8 | 4 | | 9 | 7 | | 10 | 12 | | 11 | 9 | | 12 | 11 | | 13 | 5 | | 14 | 3 | | 15 | 11 | | 16 | 3 | | 17 | 23 | | 18 | 10 | | 19 | 8 | | 20 | 3 | | 21 | 7 | | 22 | 3 | | 23 | 6 | | 24 | 7 | | 25 | 4 | | 26 | 11 | | 27 | 5 | | 28 | 4 | | 29 | 6 | | 30 | 3 | | 31 | 3 | | 32 | 15 | | 33 | 11 | | 34 | 10 | | 35 | 6 | | 36 | 4 | | 37 | 4 | | 38 | 12 | | 39 | 3 | | 40 | 11 | | 41 | 2 | | 42 | 9 | | 43 | 3 | | 44 | 5 | | 45 | 14 | | 46 | 7 | | 47 | 6 | | 48 | 4 | | 49 | 7 |
| |
| 42.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.25742574257425743 | | totalSentences | 202 | | uniqueOpeners | 52 | |
| 22.99% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 145 | | matches | | | ratio | 0.007 | |
| 82.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 145 | | matches | | 0 | "She kept her gaze fixed" | | 1 | "Her radio crackled." | | 2 | "She ignored the silence and" | | 3 | "Her breath rasped in her" | | 4 | "Her jaw tightened." | | 5 | "She didn't stop." | | 6 | "She checked her watch on" | | 7 | "He stopped at the mouth" | | 8 | "Her voice cut through the" | | 9 | "He smirked, holding his hands" | | 10 | "She shifted her weight." | | 11 | "Her leather jacket gripped her" | | 12 | "He gestured to the turnstiles" | | 13 | "They looked like they hadn't" | | 14 | "She scanned the perimeter." | | 15 | "His voice was staticky, barely" | | 16 | "You found it." | | 17 | "Her voice dropped an octave." | | 18 | "He raised his hand, palm" | | 19 | "His voice trembled slightly." |
| | ratio | 0.345 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 140 | | totalSentences | 145 | | matches | | 0 | "The water slicked the cobblestones," | | 1 | "Detective Harlow Quinn sprinted through" | | 2 | "She kept her gaze fixed" | | 3 | "Her radio crackled." | | 4 | "Static hissed between the words." | | 5 | "Quinn tapped the mic on" | | 6 | "Dispatch didn't answer immediately." | | 7 | "The signal died in the" | | 8 | "She ignored the silence and" | | 9 | "Her breath rasped in her" | | 10 | "The suspect broke into a" | | 11 | "Quinn followed, her shoulders squared." | | 12 | "Her jaw tightened." | | 13 | "The salt-and-pepper hair glued to" | | 14 | "She didn't stop." | | 15 | "She checked her watch on" | | 16 | "He stopped at the mouth" | | 17 | "The glass doors stood shattered," | | 18 | "Her voice cut through the" | | 19 | "The man turned." |
| | ratio | 0.966 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 145 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 22 | | fancyCount | 10 | | fancyTags | | 0 | "Quinn shouted (shout)" | | 1 | "Quinn shouted (shout)" | | 2 | "Tomás warned (warn)" | | 3 | "Quinn demanded (demand)" | | 4 | "The man laughed (laugh)" | | 5 | "she pressed (press)" | | 6 | "Tomás yelled (yell)" | | 7 | "Quinn ordered (order)" | | 8 | "he whispered (whisper)" | | 9 | "he agreed (agree)" |
| | dialogueSentences | 65 | | tagDensity | 0.338 | | leniency | 0.677 | | rawRatio | 0.455 | | effectiveRatio | 0.308 | |