| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.036 | | leniency | 0.071 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1590 | | totalAiIsmAdverbs | 1 | | 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) | |
| 74.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1590 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "scanning" | | 2 | "warmth" | | 3 | "measured" | | 4 | "velvet" | | 5 | "shattered" |
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| 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 | 129 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 156 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1590 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1427 | | uniqueNames | 12 | | maxNameDensity | 1.68 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Herrera | 1 | | Raven | 1 | | Nest | 1 | | Quinn | 24 | | Soho | 1 | | Tomás | 12 | | Saint | 1 | | Christopher | 1 | | Camden | 1 | | Underground | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Raven" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Market" |
| | places | | | globalScore | 0.659 | | windowScore | 0.5 | |
| 92.53% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like people keeping secrets in a r" | | 1 | "looked like a flea market that had been f" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1590 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 156 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 22.08 | | std | 25.77 | | cv | 1.167 | | sampleLengths | | 0 | 24 | | 1 | 82 | | 2 | 1 | | 3 | 29 | | 4 | 6 | | 5 | 3 | | 6 | 6 | | 7 | 87 | | 8 | 28 | | 9 | 11 | | 10 | 5 | | 11 | 86 | | 12 | 5 | | 13 | 10 | | 14 | 2 | | 15 | 73 | | 16 | 3 | | 17 | 3 | | 18 | 79 | | 19 | 80 | | 20 | 12 | | 21 | 4 | | 22 | 7 | | 23 | 7 | | 24 | 59 | | 25 | 6 | | 26 | 22 | | 27 | 56 | | 28 | 38 | | 29 | 1 | | 30 | 4 | | 31 | 3 | | 32 | 9 | | 33 | 30 | | 34 | 6 | | 35 | 91 | | 36 | 3 | | 37 | 58 | | 38 | 77 | | 39 | 10 | | 40 | 10 | | 41 | 41 | | 42 | 3 | | 43 | 6 | | 44 | 8 | | 45 | 4 | | 46 | 17 | | 47 | 14 | | 48 | 7 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 129 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 245 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 156 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1435 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.02857142857142857 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0027874564459930314 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 156 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 156 | | mean | 10.19 | | std | 7.96 | | cv | 0.781 | | sampleLengths | | 0 | 24 | | 1 | 27 | | 2 | 36 | | 3 | 19 | | 4 | 1 | | 5 | 5 | | 6 | 13 | | 7 | 11 | | 8 | 6 | | 9 | 3 | | 10 | 6 | | 11 | 20 | | 12 | 12 | | 13 | 14 | | 14 | 34 | | 15 | 3 | | 16 | 4 | | 17 | 23 | | 18 | 2 | | 19 | 3 | | 20 | 5 | | 21 | 6 | | 22 | 5 | | 23 | 34 | | 24 | 28 | | 25 | 5 | | 26 | 19 | | 27 | 5 | | 28 | 10 | | 29 | 2 | | 30 | 19 | | 31 | 6 | | 32 | 31 | | 33 | 5 | | 34 | 12 | | 35 | 3 | | 36 | 3 | | 37 | 12 | | 38 | 23 | | 39 | 13 | | 40 | 8 | | 41 | 14 | | 42 | 9 | | 43 | 12 | | 44 | 19 | | 45 | 22 | | 46 | 3 | | 47 | 4 | | 48 | 12 | | 49 | 1 |
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| 56.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3717948717948718 | | totalSentences | 156 | | uniqueOpeners | 58 | |
| 29.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 113 | | matches | | 0 | "Somewhere deeper in the tunnels," |
| | ratio | 0.009 | |
| 99.65% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 113 | | matches | | 0 | "He glanced over one shoulder." | | 1 | "He cut right and vanished" | | 2 | "She saw the token properly" | | 3 | "Her mouth tightened." | | 4 | "He knew what he carried." | | 5 | "He knew where he was" | | 6 | "He knew the city’s seams." | | 7 | "He knew where the cameras" | | 8 | "His voice carried back cleanly," | | 9 | "He laughed once, sharp and" | | 10 | "It snapped and flapped above" | | 11 | "He turned north." | | 12 | "He veered left into a" | | 13 | "Her breath stayed even." | | 14 | "Her left wrist burned where" | | 15 | "He cut through a gap" | | 16 | "It sounded like people keeping" | | 17 | "She descended fast, hand near" | | 18 | "He watched her with a" | | 19 | "His lips twitched." |
| | ratio | 0.301 | |
| 13.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 101 | | totalSentences | 113 | | matches | | 0 | "Tomás Herrera burst from the" | | 1 | "The alley spat them into" | | 2 | "A bookshelf hid the back" | | 3 | "Quinn hit the pavement hard," | | 4 | "He glanced over one shoulder." | | 5 | "Water ran from his lashes" | | 6 | "The Saint Christopher medallion at" | | 7 | "He cut right and vanished" | | 8 | "Quinn drove after him, shoulders" | | 9 | "A passing cab hissed at" | | 10 | "Tomás cleared a stack of" | | 11 | "The man swore." | | 12 | "Quinn did not slow." | | 13 | "She saw the token properly" | | 14 | "Her mouth tightened." | | 15 | "He knew what he carried." | | 16 | "He knew where he was" | | 17 | "That made the chase worse." | | 18 | "Tomás ducked under a hanging" | | 19 | "Quinn tracked him by movement" |
| | ratio | 0.894 | |
| 44.25% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 113 | | matches | | 0 | "By the time they reached" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 3 | | matches | | 0 | "Tomás cleared a stack of milk crates with one stride, landed running, and knocked a pedestrian into the wall with a shoulder that had once carried stretchers an…" | | 1 | "He veered left into a passage lined with boarded-up shops and a graffitied wall that read NO FUTURE in peeling silver paint." | | 2 | "He cut through a gap between a newsagent and a vape shop, pushed through a half-open metal gate, and dropped down a flight of steps that led beneath street leve…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "the stairs breathed (breathe)" |
| | dialogueSentences | 28 | | tagDensity | 0.036 | | leniency | 0.071 | | rawRatio | 1 | | effectiveRatio | 0.071 | |