| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.306 | | leniency | 0.612 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1300 | | 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) | |
| 88.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1300 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "crystalline" | | 1 | "weight" | | 2 | "perfect" |
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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 | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 124 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | 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) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.26% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 897 | | uniqueNames | 7 | | maxNameDensity | 1.11 | | worstName | "Cowley" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 6 | | Dust | 1 | | Cowley | 10 | | February | 2 | | Chalk | 1 | | Deptford | 1 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Dust" | | 2 | "Cowley" | | 3 | "Chalk" | | 4 | "Morris" |
| | places | | | globalScore | 0.943 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 124 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 20.97 | | std | 20.94 | | cv | 0.999 | | sampleLengths | | 0 | 14 | | 1 | 11 | | 2 | 44 | | 3 | 21 | | 4 | 2 | | 5 | 5 | | 6 | 44 | | 7 | 28 | | 8 | 30 | | 9 | 3 | | 10 | 1 | | 11 | 26 | | 12 | 59 | | 13 | 10 | | 14 | 4 | | 15 | 8 | | 16 | 10 | | 17 | 84 | | 18 | 16 | | 19 | 4 | | 20 | 5 | | 21 | 18 | | 22 | 41 | | 23 | 6 | | 24 | 4 | | 25 | 49 | | 26 | 5 | | 27 | 56 | | 28 | 14 | | 29 | 6 | | 30 | 83 | | 31 | 3 | | 32 | 56 | | 33 | 5 | | 34 | 42 | | 35 | 7 | | 36 | 21 | | 37 | 9 | | 38 | 4 | | 39 | 37 | | 40 | 20 | | 41 | 30 | | 42 | 2 | | 43 | 48 | | 44 | 1 | | 45 | 3 | | 46 | 13 | | 47 | 4 | | 48 | 66 | | 49 | 51 |
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| 93.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 90 | | matches | | 0 | "been pressed" | | 1 | "been drawn" | | 2 | "been explained" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 135 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 124 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 898 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.025612472160356347 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0011135857461024498 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 124 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 124 | | mean | 10.48 | | std | 9.67 | | cv | 0.922 | | sampleLengths | | 0 | 14 | | 1 | 7 | | 2 | 4 | | 3 | 3 | | 4 | 20 | | 5 | 5 | | 6 | 16 | | 7 | 4 | | 8 | 17 | | 9 | 2 | | 10 | 5 | | 11 | 22 | | 12 | 2 | | 13 | 3 | | 14 | 8 | | 15 | 9 | | 16 | 10 | | 17 | 18 | | 18 | 7 | | 19 | 23 | | 20 | 3 | | 21 | 1 | | 22 | 23 | | 23 | 3 | | 24 | 21 | | 25 | 6 | | 26 | 2 | | 27 | 30 | | 28 | 5 | | 29 | 5 | | 30 | 4 | | 31 | 8 | | 32 | 4 | | 33 | 6 | | 34 | 18 | | 35 | 5 | | 36 | 7 | | 37 | 20 | | 38 | 6 | | 39 | 4 | | 40 | 24 | | 41 | 8 | | 42 | 8 | | 43 | 4 | | 44 | 5 | | 45 | 12 | | 46 | 6 | | 47 | 2 | | 48 | 1 | | 49 | 16 |
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| 72.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.47580645161290325 | | totalSentences | 124 | | uniqueOpeners | 59 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 72 | | matches | | 0 | "Then the mouth." | | 1 | "Then it stopped dead, aimed" |
| | ratio | 0.028 | |
| 92.22% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 72 | | matches | | 0 | "She skipped it." | | 1 | "Her torch swung across white" | | 2 | "Her feet touched platform concrete." | | 3 | "He aimed his torch" | | 4 | "His hands rested at his" | | 5 | "She moved her torch in" | | 6 | "She stood, knees clicking, and" | | 7 | "Her breath came out white." | | 8 | "She had worked cold rooms" | | 9 | "She came back to the" | | 10 | "She looked at her own" | | 11 | "She lifted the hem with" | | 12 | "She took her time with" | | 13 | "She had seen strangulation and" | | 14 | "She eased the jaw with" | | 15 | "Her thumb went numb where" | | 16 | "She turned the disc in" | | 17 | "He stopped laughing." | | 18 | "Her torch found the track" | | 19 | "They filled the dust from" |
| | ratio | 0.319 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 72 | | matches | | 0 | "The ladder shifted under Quinn's" | | 1 | "Cowley called down" | | 2 | "She skipped it." | | 3 | "Her torch swung across white" | | 4 | "Her feet touched platform concrete." | | 5 | "He aimed his torch" | | 6 | "The body lay on the" | | 7 | "Fifties, maybe younger." | | 8 | "Wool coat, good quality, buttoned" | | 9 | "His hands rested at his" | | 10 | "Quinn crouched three feet away" | | 11 | "She moved her torch in" | | 12 | "Dust lay over everything, thick" | | 13 | "The dust had been pressed" | | 14 | "Cowley's torch beam dipped." | | 15 | "She stood, knees clicking, and" | | 16 | "Her breath came out white." | | 17 | "That bothered her more than" | | 18 | "She had worked cold rooms" | | 19 | "Frost glittered in the tile" |
| | ratio | 0.75 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "Skin drawn tight across the bones, lips pulled back from the teeth, and every visible vein standing black beneath the surface, as though the ink had been let ou…" | | 1 | "Her torch found the track bed, and the track bed answered her with something that ruined the whole shape of Cowley's textbook." | | 2 | "A small brass compass lay in his palm, green with verdigris, its face scratched with symbols that were not compass points." |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "She stood, knees clicking, and paced the platform's length" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.122 | | leniency | 0.245 | | rawRatio | 0 | | effectiveRatio | 0 | |