| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 71 | | tagDensity | 0.127 | | leniency | 0.254 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2149 | | 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) | |
| 86.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2149 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "flickered" | | 1 | "calculating" | | 2 | "flicked" | | 3 | "weight" | | 4 | "footsteps" | | 5 | "footfall" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 200 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 200 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 262 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2149 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 29.96% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 74 | | wordCount | 1791 | | uniqueNames | 5 | | maxNameDensity | 2.4 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 43 | | Herrera | 28 | | Morris | 1 | | Tube | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" |
| | places | (empty) | | globalScore | 0.3 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 145 | | 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 | 2149 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 262 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 158 | | mean | 13.6 | | std | 13.79 | | cv | 1.014 | | sampleLengths | | 0 | 42 | | 1 | 21 | | 2 | 12 | | 3 | 7 | | 4 | 33 | | 5 | 2 | | 6 | 7 | | 7 | 2 | | 8 | 52 | | 9 | 3 | | 10 | 30 | | 11 | 32 | | 12 | 4 | | 13 | 3 | | 14 | 6 | | 15 | 7 | | 16 | 35 | | 17 | 28 | | 18 | 35 | | 19 | 17 | | 20 | 5 | | 21 | 3 | | 22 | 43 | | 23 | 7 | | 24 | 2 | | 25 | 54 | | 26 | 33 | | 27 | 31 | | 28 | 1 | | 29 | 36 | | 30 | 8 | | 31 | 5 | | 32 | 3 | | 33 | 3 | | 34 | 9 | | 35 | 3 | | 36 | 5 | | 37 | 2 | | 38 | 15 | | 39 | 39 | | 40 | 8 | | 41 | 3 | | 42 | 3 | | 43 | 7 | | 44 | 5 | | 45 | 6 | | 46 | 5 | | 47 | 7 | | 48 | 7 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 200 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 311 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 262 | | ratio | 0.004 | | matches | | 0 | "He grabbed for her coat; she drove her shoulder into his chest and sent him against the wall." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1794 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.01839464882943144 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0016722408026755853 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 262 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 262 | | mean | 8.2 | | std | 5.1 | | cv | 0.622 | | sampleLengths | | 0 | 19 | | 1 | 23 | | 2 | 2 | | 3 | 2 | | 4 | 17 | | 5 | 12 | | 6 | 7 | | 7 | 11 | | 8 | 10 | | 9 | 3 | | 10 | 9 | | 11 | 2 | | 12 | 7 | | 13 | 2 | | 14 | 9 | | 15 | 13 | | 16 | 14 | | 17 | 16 | | 18 | 3 | | 19 | 8 | | 20 | 16 | | 21 | 6 | | 22 | 6 | | 23 | 13 | | 24 | 7 | | 25 | 6 | | 26 | 4 | | 27 | 3 | | 28 | 6 | | 29 | 7 | | 30 | 10 | | 31 | 12 | | 32 | 13 | | 33 | 11 | | 34 | 7 | | 35 | 10 | | 36 | 5 | | 37 | 13 | | 38 | 7 | | 39 | 10 | | 40 | 17 | | 41 | 5 | | 42 | 3 | | 43 | 10 | | 44 | 14 | | 45 | 19 | | 46 | 7 | | 47 | 2 | | 48 | 6 | | 49 | 9 |
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| 47.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.2786259541984733 | | totalSentences | 262 | | uniqueOpeners | 73 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 191 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 54 | | totalSentences | 191 | | matches | | 0 | "He checked the street, raised" | | 1 | "He glanced back." | | 2 | "His warm brown eyes found" | | 3 | "She caught a glimpse of" | | 4 | "He vaulted a stack of" | | 5 | "She had already reached the" | | 6 | "She caught the woman by" | | 7 | "She dropped to one knee" | | 8 | "She came up in a" | | 9 | "He pointed the bat at" | | 10 | "He dropped on the other" | | 11 | "She caught the fence, found" | | 12 | "Her worn watch strap snagged" | | 13 | "She tore it free and" | | 14 | "He ran down a flight" | | 15 | "He spun when Quinn entered" | | 16 | "He held the parcel against" | | 17 | "His mouth tightened." | | 18 | "He set the parcel on" | | 19 | "He drew out a short" |
| | ratio | 0.283 | |
| 9.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 172 | | totalSentences | 191 | | matches | | 0 | "The man in the green" | | 1 | "Detective Harlow Quinn watched him" | | 2 | "He checked the street, raised" | | 3 | "Quinn put her notebook away" | | 4 | "A bus surged through the" | | 5 | "He glanced back." | | 6 | "His warm brown eyes found" | | 7 | "Quinn shoved through the bus" | | 8 | "A woman yanked a child" | | 9 | "Quinn hit the pavement at" | | 10 | "Herrera turned into a narrow" | | 11 | "She caught a glimpse of" | | 12 | "He vaulted a stack of" | | 13 | "Quinn took the gap beside" | | 14 | "The mirror snapped inward behind" | | 15 | "Herrera burst onto the high" | | 16 | "A taxi braked, its horn" | | 17 | "Quinn followed, one hand on" | | 18 | "The driver wound down his" | | 19 | "She had already reached the" |
| | ratio | 0.901 | |
| 52.36% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 191 | | matches | | 0 | "By the time it passed," | | 1 | "If that person existed, Quinn" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | 0 | "she continued (continue)" |
| | dialogueSentences | 71 | | tagDensity | 0.113 | | leniency | 0.225 | | rawRatio | 0.125 | | effectiveRatio | 0.028 | |