| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 30 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.083 | | effectiveRatio | 0.067 | |
| 89.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 913 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 89.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 913 | | totalAiIsms | 2 | | found | | | highlights | | |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 34 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 34 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 52 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 85 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 918 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.59% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 16 | | wordCount | 552 | | uniqueNames | 5 | | maxNameDensity | 1.27 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Patel" | | discoveredNames | | Quinn | 7 | | Tube | 1 | | Town | 1 | | Patel | 6 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Patel" | | 2 | "Morris" |
| | places | | | globalScore | 0.866 | | windowScore | 1 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 26 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like one, except it was carved fro" |
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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 | 918 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 52 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 36.72 | | std | 31.57 | | cv | 0.86 | | sampleLengths | | 0 | 57 | | 1 | 28 | | 2 | 33 | | 3 | 3 | | 4 | 40 | | 5 | 40 | | 6 | 7 | | 7 | 15 | | 8 | 71 | | 9 | 3 | | 10 | 51 | | 11 | 85 | | 12 | 19 | | 13 | 28 | | 14 | 4 | | 15 | 53 | | 16 | 9 | | 17 | 10 | | 18 | 26 | | 19 | 105 | | 20 | 23 | | 21 | 14 | | 22 | 76 | | 23 | 5 | | 24 | 113 |
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| 74.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 34 | | matches | | 0 | "been closed" | | 1 | "was carved" | | 2 | "been drilled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 100 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 52 | | ratio | 0.058 | | matches | | 0 | "The tiled mural on the far wall, faded blue and cream, showed a scratch mark gouged so deep it exposed the brick beneath — five parallel lines, evenly spaced, curving in an arc no human hand could make." | | 1 | "A small object dropped free — a coin, or what looked like one, except it was carved from bone rather than struck from metal, etched with symbols she didn't recognise." | | 2 | "It hadn't rusted through — this was fresh damage, the exposed steel bright beneath a thin sheen of grime, meaning whatever forced it open had done so recently, and with considerable strength." |
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| 89.23% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 550 | | adjectiveStacks | 1 | | stackExamples | | 0 | "frozen half-open eyes." |
| | adverbCount | 25 | | adverbRatio | 0.045454545454545456 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00909090909090909 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 52 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 52 | | mean | 17.65 | | std | 14.42 | | cv | 0.817 | | sampleLengths | | 0 | 32 | | 1 | 25 | | 2 | 16 | | 3 | 12 | | 4 | 8 | | 5 | 25 | | 6 | 3 | | 7 | 34 | | 8 | 6 | | 9 | 8 | | 10 | 13 | | 11 | 12 | | 12 | 4 | | 13 | 3 | | 14 | 5 | | 15 | 2 | | 16 | 4 | | 17 | 11 | | 18 | 9 | | 19 | 24 | | 20 | 38 | | 21 | 3 | | 22 | 23 | | 23 | 28 | | 24 | 19 | | 25 | 18 | | 26 | 30 | | 27 | 18 | | 28 | 6 | | 29 | 13 | | 30 | 19 | | 31 | 9 | | 32 | 4 | | 33 | 30 | | 34 | 23 | | 35 | 9 | | 36 | 10 | | 37 | 4 | | 38 | 22 | | 39 | 12 | | 40 | 32 | | 41 | 27 | | 42 | 34 | | 43 | 8 | | 44 | 15 | | 45 | 14 | | 46 | 28 | | 47 | 48 | | 48 | 5 | | 49 | 14 |
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| 94.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.6923076923076923 | | totalSentences | 52 | | uniqueOpeners | 36 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 31 | | matches | (empty) | | ratio | 0 | |
| 78.06% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 31 | | matches | | 0 | "His skin had a strange" | | 1 | "It didn't melt, didn't even" | | 2 | "She reached out and touched" | | 3 | "She straightened, sweeping her torch" | | 4 | "She pulled a glove from" | | 5 | "She stood, joints protesting after" | | 6 | "He glanced toward the mural," | | 7 | "It hadn't rusted through —" | | 8 | "She thought of the frost" | | 9 | "She turned back to the" | | 10 | "She paused, staring once more" |
| | ratio | 0.355 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 30 | | totalSentences | 31 | | matches | | 0 | "Detective Quinn ducked under the" | | 1 | "Camden Town station had been" | | 2 | "DS Patel said, crouching by" | | 3 | "Quinn crouched opposite him, studying" | | 4 | "His skin had a strange" | | 5 | "Patel gestured at the tiled" | | 6 | "Quinn ran her torch along" | | 7 | "It didn't melt, didn't even" | | 8 | "She reached out and touched" | | 9 | "Powder, not water." | | 10 | "She straightened, sweeping her torch" | | 11 | "The old turnstiles stood rusted" | | 12 | "The tiled mural on the" | | 13 | "Patel flipped his notebook shut" | | 14 | "Quinn crouched again, closer to" | | 15 | "She pulled a glove from" | | 16 | "A small object dropped free" | | 17 | "A single hole had been" | | 18 | "Patel said, leaning in" | | 19 | "Quinn turned the object over" |
| | ratio | 0.968 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 31 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 16 | | technicalSentenceCount | 1 | | matches | | 0 | "The old turnstiles stood rusted in their tracks, one of them bent outward as though something had forced its way through rather than around." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0 | | effectiveRatio | 0 | |