| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 845 | | 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) | |
| 76.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 845 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "pulse" | | 2 | "could feel" | | 3 | "warmth" |
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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 | 56 | | matches | (empty) | |
| 66.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 56 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 10 | | totalWords | 845 | | ratio | 0.012 | | matches | | 0 | "unexplained circumstances" | | 1 | "Entry to the Market. Do not enter without." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 819 | | uniqueNames | 12 | | maxNameDensity | 0.49 | | worstName | "Herrera" | | maxWindowNameDensity | 1 | | worstWindowName | "Camden" | | discoveredNames | | Harlow | 1 | | Quinn | 2 | | Camden | 2 | | High | 1 | | Street | 1 | | Herrera | 4 | | Lock | 1 | | Place | 1 | | Morris | 2 | | Hackney | 2 | | London | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Lock" | | 4 | "Place" | | 5 | "Hackney" | | 6 | "London" | | 7 | "Market" |
| | globalScore | 1 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 1 | | matches | | 0 | "dark that seemed to swallow the light rather than reflect it" |
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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 | 845 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 42.25 | | std | 31.13 | | cv | 0.737 | | sampleLengths | | 0 | 61 | | 1 | 75 | | 2 | 45 | | 3 | 55 | | 4 | 67 | | 5 | 133 | | 6 | 26 | | 7 | 41 | | 8 | 9 | | 9 | 59 | | 10 | 39 | | 11 | 20 | | 12 | 12 | | 13 | 16 | | 14 | 3 | | 15 | 6 | | 16 | 61 | | 17 | 75 | | 18 | 22 | | 19 | 20 |
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| 92.73% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 56 | | matches | | 0 | "been trained" | | 1 | "been meant" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 123 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 821 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.024360535931790498 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.00243605359317905 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 14.32 | | std | 10.31 | | cv | 0.72 | | sampleLengths | | 0 | 27 | | 1 | 4 | | 2 | 30 | | 3 | 16 | | 4 | 27 | | 5 | 13 | | 6 | 6 | | 7 | 1 | | 8 | 12 | | 9 | 15 | | 10 | 4 | | 11 | 26 | | 12 | 11 | | 13 | 7 | | 14 | 3 | | 15 | 34 | | 16 | 15 | | 17 | 14 | | 18 | 12 | | 19 | 26 | | 20 | 8 | | 21 | 4 | | 22 | 23 | | 23 | 30 | | 24 | 9 | | 25 | 11 | | 26 | 48 | | 27 | 17 | | 28 | 9 | | 29 | 5 | | 30 | 8 | | 31 | 28 | | 32 | 9 | | 33 | 4 | | 34 | 22 | | 35 | 3 | | 36 | 5 | | 37 | 25 | | 38 | 3 | | 39 | 32 | | 40 | 4 | | 41 | 8 | | 42 | 12 | | 43 | 3 | | 44 | 9 | | 45 | 4 | | 46 | 12 | | 47 | 3 | | 48 | 6 | | 49 | 5 |
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| 74.01% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4745762711864407 | | totalSentences | 59 | | uniqueOpeners | 28 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 55 | | matches | | 0 | "Somewhere below, faint and distant," | | 1 | "Then she took hold of" |
| | ratio | 0.036 | |
| 16.36% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 55 | | matches | | 0 | "She didn't feel it." | | 1 | "Her eyes were on the" | | 2 | "Her left wrist ached where" | | 3 | "She checked it out of" | | 4 | "Her voice carried, flat and" | | 5 | "He did not stop." | | 6 | "He glanced back once, his" | | 7 | "She followed, boots striking the" | | 8 | "Her radio hissed in her" | | 9 | "She ignored it." | | 10 | "She drew her weapon and" | | 11 | "Her pulse was steady." | | 12 | "It always was, the way" | | 13 | "She had read it so" | | 14 | "She had spent three years" | | 15 | "She stepped over the railing." | | 16 | "She picked it up." | | 17 | "It was small, smooth, and" | | 18 | "It was warm." | | 19 | "She had read about them" |
| | ratio | 0.509 | |
| 78.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 55 | | matches | | 0 | "The rain had been falling" | | 1 | "She didn't feel it." | | 2 | "Her eyes were on the" | | 3 | "Tomás Herrera ran like a" | | 4 | "Her left wrist ached where" | | 5 | "She checked it out of" | | 6 | "Her voice carried, flat and" | | 7 | "He did not stop." | | 8 | "He glanced back once, his" | | 9 | "She followed, boots striking the" | | 10 | "Her radio hissed in her" | | 11 | "She ignored it." | | 12 | "Backup was twelve minutes out" | | 13 | "The lane was narrow, lined" | | 14 | "Halfway along, Herrera vaulted a" | | 15 | "Quinn reached the railing and" | | 16 | "She drew her weapon and" | | 17 | "Her pulse was steady." | | 18 | "It always was, the way" | | 19 | "The coroner's report had used" |
| | ratio | 0.764 | |
| 90.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 55 | | matches | | 0 | "Even at this distance she" |
| | ratio | 0.018 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "Her eyes were on the man ahead, a dark shape cutting across Camden High Street, weaving between a bus shelter and a knot of smokers who scattered from his path." | | 1 | "What the report had not explained was the smell that clung to the basement walls, sweet and metallic, like wet coins in a jar of honey, or the way the security …" |
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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 | |