| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva said quietly [quietly]" | | 1 | "she said slowly [slowly]" |
| | dialogueSentences | 43 | | tagDensity | 0.349 | | leniency | 0.698 | | rawRatio | 0.133 | | effectiveRatio | 0.093 | |
| 95.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1155 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 65.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1155 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "weight" | | 1 | "gloom" | | 2 | "traced" | | 3 | "echoed" | | 4 | "flicked" | | 5 | "perfect" | | 6 | "chill" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1155 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 709 | | uniqueNames | 6 | | maxNameDensity | 1.55 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Patel | 4 | | Quinn | 11 | | Eva | 9 | | Morris | 1 |
| | persons | | 0 | "Camden" | | 1 | "Patel" | | 2 | "Quinn" | | 3 | "Eva" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.724 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1155 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 22.65 | | std | 19.08 | | cv | 0.842 | | sampleLengths | | 0 | 7 | | 1 | 36 | | 2 | 41 | | 3 | 26 | | 4 | 7 | | 5 | 7 | | 6 | 37 | | 7 | 7 | | 8 | 37 | | 9 | 20 | | 10 | 6 | | 11 | 10 | | 12 | 6 | | 13 | 48 | | 14 | 6 | | 15 | 1 | | 16 | 49 | | 17 | 6 | | 18 | 27 | | 19 | 11 | | 20 | 7 | | 21 | 50 | | 22 | 10 | | 23 | 23 | | 24 | 9 | | 25 | 43 | | 26 | 43 | | 27 | 22 | | 28 | 2 | | 29 | 17 | | 30 | 31 | | 31 | 54 | | 32 | 8 | | 33 | 7 | | 34 | 64 | | 35 | 27 | | 36 | 13 | | 37 | 5 | | 38 | 4 | | 39 | 5 | | 40 | 26 | | 41 | 26 | | 42 | 13 | | 43 | 33 | | 44 | 29 | | 45 | 92 | | 46 | 51 | | 47 | 13 | | 48 | 19 | | 49 | 8 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 78 | | matches | | |
| 94.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 126 | | matches | | 0 | "was saying" | | 1 | "was watching" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 106 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 709 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.036671368124118475 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004231311706629055 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 10.9 | | std | 8.15 | | cv | 0.748 | | sampleLengths | | 0 | 7 | | 1 | 8 | | 2 | 9 | | 3 | 6 | | 4 | 13 | | 5 | 14 | | 6 | 1 | | 7 | 3 | | 8 | 23 | | 9 | 12 | | 10 | 14 | | 11 | 7 | | 12 | 7 | | 13 | 3 | | 14 | 18 | | 15 | 10 | | 16 | 6 | | 17 | 7 | | 18 | 9 | | 19 | 28 | | 20 | 5 | | 21 | 12 | | 22 | 1 | | 23 | 2 | | 24 | 6 | | 25 | 10 | | 26 | 6 | | 27 | 4 | | 28 | 12 | | 29 | 12 | | 30 | 5 | | 31 | 15 | | 32 | 6 | | 33 | 1 | | 34 | 19 | | 35 | 30 | | 36 | 3 | | 37 | 3 | | 38 | 8 | | 39 | 19 | | 40 | 11 | | 41 | 7 | | 42 | 3 | | 43 | 22 | | 44 | 14 | | 45 | 11 | | 46 | 10 | | 47 | 8 | | 48 | 15 | | 49 | 9 |
| |
| 81.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5094339622641509 | | totalSentences | 106 | | uniqueOpeners | 54 | |
| 49.02% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 68 | | matches | | 0 | "Then she crouched once more" |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 68 | | matches | | 0 | "She shifted her weight to" | | 1 | "Her torch cut a white" | | 2 | "She tilted her torch." | | 3 | "She traced the air above" | | 4 | "She rose, knees protesting" | | 5 | "She looked too young and" | | 6 | "She held up a laminated" | | 7 | "She crouched by the body" | | 8 | "They always did." | | 9 | "She let her torch wander." | | 10 | "She lifted the dead man's" | | 11 | "Her worn leather watch slid" | | 12 | "It was an arc." | | 13 | "She looked up, and behind" | | 14 | "she said slowly" |
| | ratio | 0.221 | |
| 48.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 68 | | matches | | 0 | "Quinn ignored the warning and" | | 1 | "The wood gave under her" | | 2 | "She shifted her weight to" | | 3 | "The abandoned Tube station beneath" | | 4 | "Her torch cut a white" | | 5 | "Quinn said nothing." | | 6 | "The platform stretched before her," | | 7 | "A woman's painted face advertised" | | 8 | "The body lay near the" | | 9 | "Patel continued, reading from his" | | 10 | "Quinn crouched beside the corpse." | | 11 | "The man lay on his" | | 12 | "That was the first wrong" | | 13 | "She tilted her torch." | | 14 | "The wound gaped at the" | | 15 | "None on the tiles around" | | 16 | "She traced the air above" | | 17 | "Patel crouched, frowning." | | 18 | "She rose, knees protesting" | | 19 | "A new voice echoed from" |
| | ratio | 0.824 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 2 | | matches | | 0 | "The rigor had set hours past, which meant Patel's midnight estimate was close enough." | | 1 | "The skin around it showed faint bruising, as if the man had clutched something hard right up until the end." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 80.23% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 3 | | fancyTags | | 0 | "Patel continued (continue)" | | 1 | "She rose knees protesting (protest)" | | 2 | "she repeated (repeat)" |
| | dialogueSentences | 43 | | tagDensity | 0.233 | | leniency | 0.465 | | rawRatio | 0.3 | | effectiveRatio | 0.14 | |