| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 388 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 388 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "glinting" | | 3 | "silence" | | 4 | "jaw clenched" | | 5 | "flicked" | | 6 | "charm" | | 7 | "unspoken" | | 8 | "pumping" | | 9 | "gloom" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 38 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 38 | | filterMatches | | | hedgeMatches | (empty) | |
| 88.46% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 46 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 20 | | ratio | 0.022 | | matches | | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 389 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 324 | | uniqueNames | 12 | | maxNameDensity | 0.62 | | worstName | "Tomás" | | maxWindowNameDensity | 1 | | worstWindowName | "Tomás" | | discoveredNames | | Quinn | 1 | | Silence | 1 | | Tomás | 2 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | Raven | 1 | | Nest | 1 | | London | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Raven" | | 6 | "Market" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 28 | | 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 | 389 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 46 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 17.68 | | std | 12.86 | | cv | 0.728 | | sampleLengths | | 0 | 46 | | 1 | 16 | | 2 | 42 | | 3 | 27 | | 4 | 14 | | 5 | 3 | | 6 | 26 | | 7 | 5 | | 8 | 12 | | 9 | 36 | | 10 | 10 | | 11 | 11 | | 12 | 7 | | 13 | 31 | | 14 | 32 | | 15 | 14 | | 16 | 13 | | 17 | 3 | | 18 | 22 | | 19 | 5 | | 20 | 13 | | 21 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 38 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 64 | | matches | (empty) | |
| 80.75% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 46 | | ratio | 0.022 | | matches | | 0 | "The narrow passage opened into a courtyard; abandoned, save for the skeletal remains of a delivery truck." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 173 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.017341040462427744 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.011560693641618497 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 46 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 46 | | mean | 8.46 | | std | 4.64 | | cv | 0.549 | | sampleLengths | | 0 | 15 | | 1 | 20 | | 2 | 11 | | 3 | 12 | | 4 | 4 | | 5 | 1 | | 6 | 6 | | 7 | 6 | | 8 | 17 | | 9 | 12 | | 10 | 16 | | 11 | 11 | | 12 | 14 | | 13 | 3 | | 14 | 10 | | 15 | 9 | | 16 | 3 | | 17 | 4 | | 18 | 5 | | 19 | 3 | | 20 | 9 | | 21 | 7 | | 22 | 11 | | 23 | 2 | | 24 | 2 | | 25 | 14 | | 26 | 10 | | 27 | 9 | | 28 | 2 | | 29 | 7 | | 30 | 7 | | 31 | 14 | | 32 | 10 | | 33 | 10 | | 34 | 10 | | 35 | 12 | | 36 | 3 | | 37 | 11 | | 38 | 13 | | 39 | 3 | | 40 | 7 | | 41 | 9 | | 42 | 6 | | 43 | 5 | | 44 | 13 | | 45 | 1 |
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| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.6739130434782609 | | totalSentences | 46 | | uniqueOpeners | 31 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 33 | | matches | | 0 | "Then he bolted, leaping over" |
| | ratio | 0.03 | |
| 74.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 33 | | matches | | 0 | "she shouted, voice cracking against" | | 1 | "He turned, slipping into an" | | 2 | "She followed, jaw clenched with" | | 3 | "His Saint Christopher medallion danced" | | 4 | "His accent sliced the air," | | 5 | "He shrugged, the motion almost" | | 6 | "His eyes flicked to the" | | 7 | "His jaw tightened." | | 8 | "He sighed, rubbing the scar" | | 9 | "She swore, pumping her legs" | | 10 | "He paused then, deliberating in" | | 11 | "Her answer came swift, a" |
| | ratio | 0.364 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 23 | | totalSentences | 33 | | matches | | 0 | "Harlow Quinn's footsteps echoed against" | | 1 | "The suspect's silhouette darted ahead," | | 2 | "she shouted, voice cracking against" | | 3 | "He turned, slipping into an" | | 4 | "She followed, jaw clenched with" | | 5 | "The narrow passage opened into" | | 6 | "Rust ate at its edges," | | 7 | "His Saint Christopher medallion danced" | | 8 | "His accent sliced the air," | | 9 | "He shrugged, the motion almost" | | 10 | "His eyes flicked to the" | | 11 | "A escape route?" | | 12 | "His jaw tightened." | | 13 | "A magnet for those who" | | 14 | "He sighed, rubbing the scar" | | 15 | "A beat passed, heavy with" | | 16 | "She swore, pumping her legs" | | 17 | "The air grew colder, damper," | | 18 | "A black market sprawled before" | | 19 | "The Veil Market." |
| | ratio | 0.697 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 33 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 16 | | technicalSentenceCount | 1 | | matches | | 0 | "Blame it on the green neon sign that gave the place its seedy charm." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 2 | | matches | | 0 | "she shouted, voice cracking against the storm's fury" | | 1 | "Her answer came, a prayer whispered to the universe:" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she continued (continue)" | | 2 | "Tomás hissed (hiss)" |
| | dialogueSentences | 14 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 1 | | effectiveRatio | 0.429 | |