| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1301 | | 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) | |
| 88.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1301 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "thundered" | | 1 | "footsteps" | | 2 | "measured" |
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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 | 125 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 125 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 137 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1300 | | ratio | 0 | | matches | (empty) | |
| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | 0 | "Somewhere in the crowd, Voss called out, and a reply rose from farther inside." |
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| 56.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1231 | | uniqueNames | 9 | | maxNameDensity | 1.87 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 23 | | Frith | 1 | | Street | 1 | | Voss | 15 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Voss" | | 4 | "Market" |
| | places | | | globalScore | 0.566 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | 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 | 1300 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 137 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 21.31 | | std | 18.43 | | cv | 0.865 | | sampleLengths | | 0 | 43 | | 1 | 29 | | 2 | 7 | | 3 | 2 | | 4 | 9 | | 5 | 20 | | 6 | 42 | | 7 | 35 | | 8 | 30 | | 9 | 2 | | 10 | 59 | | 11 | 14 | | 12 | 4 | | 13 | 10 | | 14 | 37 | | 15 | 33 | | 16 | 24 | | 17 | 6 | | 18 | 7 | | 19 | 47 | | 20 | 45 | | 21 | 9 | | 22 | 6 | | 23 | 7 | | 24 | 7 | | 25 | 53 | | 26 | 47 | | 27 | 7 | | 28 | 32 | | 29 | 8 | | 30 | 4 | | 31 | 10 | | 32 | 32 | | 33 | 1 | | 34 | 2 | | 35 | 43 | | 36 | 15 | | 37 | 15 | | 38 | 10 | | 39 | 1 | | 40 | 45 | | 41 | 17 | | 42 | 40 | | 43 | 9 | | 44 | 32 | | 45 | 28 | | 46 | 24 | | 47 | 4 | | 48 | 1 | | 49 | 22 |
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| 99.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 125 | | matches | | 0 | "been bricked" | | 1 | "been carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 215 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 137 | | ratio | 0.015 | | matches | | 0 | "CAMDEN MARKET—CLOSED FOR REPAIRS." | | 1 | "The surviving letters spelled CAM—." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1235 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.016194331983805668 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0032388663967611335 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 137 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 137 | | mean | 9.49 | | std | 6.17 | | cv | 0.65 | | sampleLengths | | 0 | 19 | | 1 | 24 | | 2 | 14 | | 3 | 15 | | 4 | 7 | | 5 | 2 | | 6 | 9 | | 7 | 11 | | 8 | 9 | | 9 | 9 | | 10 | 11 | | 11 | 22 | | 12 | 3 | | 13 | 4 | | 14 | 28 | | 15 | 6 | | 16 | 4 | | 17 | 14 | | 18 | 6 | | 19 | 2 | | 20 | 6 | | 21 | 23 | | 22 | 6 | | 23 | 2 | | 24 | 4 | | 25 | 18 | | 26 | 8 | | 27 | 6 | | 28 | 4 | | 29 | 6 | | 30 | 4 | | 31 | 4 | | 32 | 16 | | 33 | 17 | | 34 | 8 | | 35 | 12 | | 36 | 13 | | 37 | 8 | | 38 | 8 | | 39 | 8 | | 40 | 6 | | 41 | 2 | | 42 | 5 | | 43 | 4 | | 44 | 7 | | 45 | 21 | | 46 | 6 | | 47 | 9 | | 48 | 13 | | 49 | 5 |
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| 45.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.291970802919708 | | totalSentences | 137 | | uniqueOpeners | 40 | |
| 56.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 118 | | matches | | 0 | "Then he ran again." | | 1 | "Somewhere in the crowd, Voss" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 118 | | matches | | 0 | "He checked both ends of" | | 1 | "She shoved off the wall" | | 2 | "she snapped into the radio" | | 3 | "He was quick." | | 4 | "He knew the streets." | | 5 | "He landed, stumbled, and looked" | | 6 | "He had appeared in the" | | 7 | "His mouth moved around a" | | 8 | "She caught it before it" | | 9 | "He veered between two parked" | | 10 | "Her lungs pulled cold air" | | 11 | "She pressed the radio button" | | 12 | "Her coat dragged at her" | | 13 | "He looked over his shoulder." | | 14 | "He reached the far end" | | 15 | "She forced her breath into" | | 16 | "She caught the edge with" | | 17 | "She took the stairs two" | | 18 | "She thumbed her radio." | | 19 | "She tried again, shifting the" |
| | ratio | 0.246 | |
| 23.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 103 | | totalSentences | 118 | | matches | | 0 | "The green neon above the" | | 1 | "Detective Harlow Quinn watched the" | | 2 | "He checked both ends of" | | 3 | "Quinn saw his eyes find" | | 4 | "She shoved off the wall" | | 5 | "she snapped into the radio" | | 6 | "The man shouldered through a" | | 7 | "Someone swore as he clipped" | | 8 | "Quinn ducked under an umbrella," | | 9 | "He was quick." | | 10 | "He knew the streets." | | 11 | "Quinn took the gap at" | | 12 | "Brick scraped her shoulder." | | 13 | "The alley spat her into" | | 14 | "The suspect vaulted a low" | | 15 | "He landed, stumbled, and looked" | | 16 | "The hard white flash of" | | 17 | "Quinn knew him from the" | | 18 | "He had appeared in the" | | 19 | "Voss lifted one hand as" |
| | ratio | 0.873 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 1 | | matches | | 0 | "Their surface gleamed under her torch as if someone had polished them." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 14 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.5 | | effectiveRatio | 0.143 | |