| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva glanced around [around]" | | 1 | "Quinn's torch revealed more [more]" |
| | dialogueSentences | 45 | | tagDensity | 0.356 | | leniency | 0.711 | | rawRatio | 0.125 | | effectiveRatio | 0.089 | |
| 87.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 780 | | 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) | |
| 16.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 780 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "traced" | | 1 | "gloom" | | 2 | "stark" | | 3 | "eyebrow" | | 4 | "tracing" | | 5 | "racing" | | 6 | "glint" | | 7 | "intricate" | | 8 | "footsteps" | | 9 | "echoing" | | 10 | "pulsed" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 53 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 53 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 81 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 780 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 410 | | uniqueNames | 6 | | maxNameDensity | 4.15 | | worstName | "Quinn" | | maxWindowNameDensity | 7.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Quinn | 17 | | Underground | 1 | | Eva | 12 | | Kowalski | 1 | | Market | 1 |
| | persons | | 0 | "Camden" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Market" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 71.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | glossingSentenceCount | 1 | | matches | | |
| 71.79% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.282 | | wordCount | 780 | | matches | | 0 | "Not the usual Underground bouquet of stale air and brake dust, but something sharper" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 21.08 | | std | 10.96 | | cv | 0.52 | | sampleLengths | | 0 | 28 | | 1 | 30 | | 2 | 18 | | 3 | 25 | | 4 | 41 | | 5 | 17 | | 6 | 30 | | 7 | 27 | | 8 | 16 | | 9 | 7 | | 10 | 31 | | 11 | 4 | | 12 | 11 | | 13 | 14 | | 14 | 17 | | 15 | 33 | | 16 | 29 | | 17 | 15 | | 18 | 2 | | 19 | 20 | | 20 | 25 | | 21 | 8 | | 22 | 41 | | 23 | 5 | | 24 | 24 | | 25 | 15 | | 26 | 36 | | 27 | 10 | | 28 | 40 | | 29 | 10 | | 30 | 29 | | 31 | 6 | | 32 | 21 | | 33 | 20 | | 34 | 14 | | 35 | 39 | | 36 | 22 |
| |
| 98.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 53 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 70 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 81 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 410 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.02926829268292683 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.00975609756097561 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 81 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 81 | | mean | 9.63 | | std | 5.69 | | cv | 0.591 | | sampleLengths | | 0 | 6 | | 1 | 4 | | 2 | 18 | | 3 | 8 | | 4 | 2 | | 5 | 2 | | 6 | 18 | | 7 | 14 | | 8 | 4 | | 9 | 5 | | 10 | 14 | | 11 | 1 | | 12 | 1 | | 13 | 4 | | 14 | 16 | | 15 | 7 | | 16 | 9 | | 17 | 9 | | 18 | 11 | | 19 | 6 | | 20 | 6 | | 21 | 24 | | 22 | 12 | | 23 | 15 | | 24 | 9 | | 25 | 7 | | 26 | 4 | | 27 | 3 | | 28 | 14 | | 29 | 17 | | 30 | 4 | | 31 | 6 | | 32 | 5 | | 33 | 7 | | 34 | 7 | | 35 | 11 | | 36 | 6 | | 37 | 16 | | 38 | 17 | | 39 | 10 | | 40 | 6 | | 41 | 13 | | 42 | 7 | | 43 | 8 | | 44 | 2 | | 45 | 6 | | 46 | 14 | | 47 | 14 | | 48 | 11 | | 49 | 5 |
| |
| 98.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.6419753086419753 | | totalSentences | 81 | | uniqueOpeners | 52 | |
| 70.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 47 | | matches | | 0 | "Once he retreated up the" |
| | ratio | 0.021 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 3 | | totalSentences | 47 | | matches | | 0 | "Her torch beam cut through" | | 1 | "She crouched, retrieving a small" | | 2 | "Its face bore intricate etchings," |
| | ratio | 0.064 | |
| 45.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 47 | | matches | | 0 | "The brass handle turned without" | | 1 | "Amateur work, no finesse." | | 2 | "The padlocked gate to Camden's" | | 3 | "Detective Quinn traced her fingers" | | 4 | "Bolt cutters would have left" | | 5 | "The station manager wrung his" | | 6 | "The scent hit her first." | | 7 | "Her torch beam cut through" | | 8 | "The spacing was too regular," | | 9 | "Quinn kept her voice neutral" | | 10 | "Eva Kowalski appeared at Quinn's" | | 11 | "The researcher pushed her round" | | 12 | "Eva's green eyes traced the" | | 13 | "Quinn raised an eyebrow." | | 14 | "Eva's fingers brushed the wall," | | 15 | "The station manager cleared his" | | 16 | "Quinn flashed her warrant card" | | 17 | "Eva tucked a strand of" | | 18 | "Quinn's torch caught a glint" | | 19 | "She crouched, retrieving a small" |
| | ratio | 0.83 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn muttered (mutter)" | | 1 | "Quinn's torch revealed more (reveal)" |
| | dialogueSentences | 45 | | tagDensity | 0.044 | | leniency | 0.089 | | rawRatio | 1 | | effectiveRatio | 0.089 | |