| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn said slowly [slowly]" |
| | dialogueSentences | 7 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0.25 | | effectiveRatio | 0.25 | |
| 71.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 349 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 349 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "echoed" | | 1 | "traced" | | 2 | "jaw clenched" | | 3 | "glint" | | 4 | "standard" | | 5 | "processed" | | 6 | "etched" | | 7 | "magnetic" | | 8 | "disrupted" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 29 | | matches | (empty) | |
| 44.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 29 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 32 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 347 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 18.42% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 304 | | uniqueNames | 8 | | maxNameDensity | 2.63 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 2 | | Harlow | 1 | | Quinn | 8 | | Eva | 4 | | Kowalski | 1 | | Morris | 1 | | Veil | 1 | | Compass | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.184 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 21 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 5.764 | | wordCount | 347 | | matches | | 0 | "Not one I immediately recognize, but definitely not random" | | 1 | "not toward magnetic north, but toward something else entirely" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 32 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 28.92 | | std | 14.76 | | cv | 0.51 | | sampleLengths | | 0 | 40 | | 1 | 50 | | 2 | 8 | | 3 | 41 | | 4 | 31 | | 5 | 6 | | 6 | 32 | | 7 | 38 | | 8 | 45 | | 9 | 12 | | 10 | 12 | | 11 | 32 |
| |
| 81.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 29 | | matches | | 0 | "was pulled" | | 1 | "been disrupted" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 52 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 32 | | ratio | 0.063 | | matches | | 0 | "Eva stepped closer, tucking a stray curl behind her left ear—a nervous habit Quinn had noticed before." | | 1 | "Something fundamental had been disrupted here—something that went far beyond a simple metropolitan police investigation." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 309 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.025889967637540454 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.019417475728155338 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 32 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 32 | | mean | 10.84 | | std | 5.89 | | cv | 0.543 | | sampleLengths | | 0 | 10 | | 1 | 22 | | 2 | 3 | | 3 | 4 | | 4 | 1 | | 5 | 11 | | 6 | 2 | | 7 | 1 | | 8 | 17 | | 9 | 19 | | 10 | 8 | | 11 | 11 | | 12 | 18 | | 13 | 12 | | 14 | 8 | | 15 | 12 | | 16 | 11 | | 17 | 6 | | 18 | 17 | | 19 | 15 | | 20 | 6 | | 21 | 16 | | 22 | 16 | | 23 | 11 | | 24 | 21 | | 25 | 13 | | 26 | 9 | | 27 | 3 | | 28 | 6 | | 29 | 6 | | 30 | 17 | | 31 | 15 |
| |
| 90.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5625 | | totalSentences | 32 | | uniqueOpeners | 18 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 25 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 25 | | matches | | 0 | "Her worn leather watch caught" | | 1 | "Her curly red hair was" | | 2 | "she said, voice tight with" | | 3 | "Her expertise in occult studies" | | 4 | "They'd worked together before, though" | | 5 | "She pulled out a small" |
| | ratio | 0.24 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 23 | | totalSentences | 25 | | matches | | 0 | "The abandoned Tube station swallowed" | | 1 | "Detective Harlow Quinn's boots echoed" | | 2 | "Something was off." | | 3 | "The crime scene felt..." | | 4 | "Blood spatter traced an impossible" | | 5 | "Quinn's sharp jaw clenched as" | | 6 | "Her worn leather watch caught" | | 7 | "Eva Kowalski stood nearby, her" | | 8 | "Her curly red hair was" | | 9 | "she said, voice tight with" | | 10 | "Quinn knew Eva wasn't just" | | 11 | "Her expertise in occult studies" | | 12 | "They'd worked together before, though" | | 13 | "Quinn's brown eyes narrowed" | | 14 | "Eva stepped closer, tucking a" | | 15 | "Quinn's military-trained mind processed the" | | 16 | "She pulled out a small" | | 17 | "The Veil Compass, its face" | | 18 | "The needle twitched, pointing not" | | 19 | "Quinn said slowly" |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 25 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 17 | | technicalSentenceCount | 2 | | matches | | 0 | "Her curly red hair was pulled back, freckled hands clutching a leather satchel that bulged with research materials." | | 1 | "Something fundamental had been disrupted here—something that went far beyond a simple metropolitan police investigation." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, voice tight with academic precision" |
| |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 7 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.333 | | effectiveRatio | 0.286 | |