| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 66 | | tagDensity | 0.379 | | leniency | 0.758 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 77.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1090 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "reluctantly" | | count | 1 |
|
| | highlights | | 0 | "quickly" | | 1 | "carefully" | | 2 | "very" | | 3 | "reluctantly" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 49.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1090 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "stomach" | | 1 | "shattered" | | 2 | "etched" | | 3 | "traced" | | 4 | "scanning" | | 5 | "pristine" | | 6 | "measured" | | 7 | "intensity" | | 8 | "echoed" | | 9 | "flickered" | | 10 | "echoing" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 69 | | matches | (empty) | |
| 80.75% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 69 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1088 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 537 | | uniqueNames | 8 | | maxNameDensity | 4.47 | | worstName | "Quinn" | | maxWindowNameDensity | 7.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 24 | | Tube | 1 | | Camden | 1 | | Finch | 19 | | Morris | 2 | | Thorne | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Finch" | | 4 | "Morris" | | 5 | "Thorne" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 28.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 2 | | matches | | 0 | "symbols that seemed to shift in the corner of her vision" | | 1 | "seemed deeper than they should" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1088 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 20.53 | | std | 11.08 | | cv | 0.54 | | sampleLengths | | 0 | 32 | | 1 | 34 | | 2 | 40 | | 3 | 26 | | 4 | 25 | | 5 | 25 | | 6 | 22 | | 7 | 19 | | 8 | 6 | | 9 | 27 | | 10 | 14 | | 11 | 20 | | 12 | 22 | | 13 | 32 | | 14 | 9 | | 15 | 19 | | 16 | 29 | | 17 | 13 | | 18 | 23 | | 19 | 6 | | 20 | 29 | | 21 | 14 | | 22 | 36 | | 23 | 6 | | 24 | 41 | | 25 | 2 | | 26 | 28 | | 27 | 20 | | 28 | 28 | | 29 | 20 | | 30 | 35 | | 31 | 5 | | 32 | 22 | | 33 | 11 | | 34 | 26 | | 35 | 8 | | 36 | 23 | | 37 | 6 | | 38 | 41 | | 39 | 6 | | 40 | 30 | | 41 | 8 | | 42 | 38 | | 43 | 3 | | 44 | 21 | | 45 | 6 | | 46 | 7 | | 47 | 34 | | 48 | 11 | | 49 | 16 |
| |
| 84.92% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 69 | | matches | | 0 | "was frozen" | | 1 | "were curled" | | 2 | "was carved" | | 3 | "was torn" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 113 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 108 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 538 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.03531598513011153 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.011152416356877323 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 10.07 | | std | 5.81 | | cv | 0.577 | | sampleLengths | | 0 | 21 | | 1 | 11 | | 2 | 14 | | 3 | 20 | | 4 | 13 | | 5 | 16 | | 6 | 11 | | 7 | 13 | | 8 | 13 | | 9 | 13 | | 10 | 12 | | 11 | 13 | | 12 | 2 | | 13 | 10 | | 14 | 12 | | 15 | 10 | | 16 | 9 | | 17 | 3 | | 18 | 7 | | 19 | 6 | | 20 | 5 | | 21 | 9 | | 22 | 13 | | 23 | 3 | | 24 | 11 | | 25 | 4 | | 26 | 10 | | 27 | 6 | | 28 | 2 | | 29 | 20 | | 30 | 8 | | 31 | 16 | | 32 | 8 | | 33 | 5 | | 34 | 4 | | 35 | 6 | | 36 | 13 | | 37 | 8 | | 38 | 14 | | 39 | 7 | | 40 | 2 | | 41 | 11 | | 42 | 3 | | 43 | 20 | | 44 | 4 | | 45 | 2 | | 46 | 8 | | 47 | 21 | | 48 | 14 | | 49 | 12 |
| |
| 76.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.48148148148148145 | | totalSentences | 108 | | uniqueOpeners | 52 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 64 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 64 | | matches | | 0 | "she asked, kneeling beside the" | | 1 | "His fingers were curled around" | | 2 | "Her colleague produced one quickly." | | 3 | "She'd seen similar markings at" | | 4 | "she called back" | | 5 | "Her eyes narrowed." | | 6 | "She pointed to the edge" | | 7 | "Her fingers disappeared into it" | | 8 | "They turned and sprinted for" |
| | ratio | 0.141 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 64 | | matches | | 0 | "The metal gate groaned as" | | 1 | "The abandoned Tube station beneath" | | 2 | "DS Finch stood beside her," | | 3 | "Quinn stepped forward, her military-precise" | | 4 | "The air tasted of ozone" | | 5 | "she asked, kneeling beside the" | | 6 | "Thorne's face was frozen in" | | 7 | "Finch circled the body" | | 8 | "Quinn's leather watch caught the" | | 9 | "Finch suggested, pointing to a" | | 10 | "Quinn's sharp jaw tightened as" | | 11 | "His fingers were curled around" | | 12 | "Her colleague produced one quickly." | | 13 | "Quinn carefully pried the object" | | 14 | "A small brass compass with" | | 15 | "Quinn held it up." | | 16 | "The needle spun wildly before" | | 17 | "Quinn stood, approaching the archway" | | 18 | "The stone was carved with" | | 19 | "She'd seen similar markings at" |
| | ratio | 0.922 | |
| 78.13% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 64 | | matches | | | ratio | 0.016 | |
| 35.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 3 | | matches | | 0 | "The metal gate groaned as Detective Harlow Quinn pushed it open, revealing a scene that made even her seasoned stomach clench." | | 1 | "The air tasted of ozone and something older, something that reminded her of Morris's final moments." | | 2 | "The shadows at the edge of their torchlight began to move, coalescing into a form that defied natural explanation." |
| |
| 65.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 3 | | matches | | 0 | "Finch offered, though his voice had lost some certainty" | | 1 | "Quinn approached, her hand outstretched" | | 2 | "Finch whispered, his hand moving to his weapon" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "Finch suggested (suggest)" | | 1 | "Finch whispered (whisper)" |
| | dialogueSentences | 66 | | tagDensity | 0.076 | | leniency | 0.152 | | rawRatio | 0.4 | | effectiveRatio | 0.061 | |