| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 48 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.056 | | effectiveRatio | 0.042 | |
| 95.27% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1058 | | 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) | |
| 81.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1058 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "traced" | | 1 | "etched" | | 2 | "pulse" | | 3 | "blown wide" |
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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 | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1058 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 660 | | uniqueNames | 7 | | maxNameDensity | 1.52 | | worstName | "Davies" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Davies" | | discoveredNames | | Camden | 1 | | Davies | 10 | | Quinn | 7 | | Together | 1 | | Hackney | 1 | | Morris | 2 | | Past | 1 |
| | persons | | 0 | "Davies" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | | globalScore | 0.742 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | 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 | 1058 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 102 | | matches | | 0 | "arriving that he" | | 1 | "seen that look" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 23 | | std | 20.16 | | cv | 0.877 | | sampleLengths | | 0 | 20 | | 1 | 40 | | 2 | 22 | | 3 | 2 | | 4 | 24 | | 5 | 28 | | 6 | 4 | | 7 | 4 | | 8 | 5 | | 9 | 29 | | 10 | 7 | | 11 | 32 | | 12 | 33 | | 13 | 34 | | 14 | 11 | | 15 | 37 | | 16 | 6 | | 17 | 15 | | 18 | 1 | | 19 | 73 | | 20 | 10 | | 21 | 32 | | 22 | 50 | | 23 | 7 | | 24 | 1 | | 25 | 6 | | 26 | 51 | | 27 | 52 | | 28 | 11 | | 29 | 1 | | 30 | 5 | | 31 | 50 | | 32 | 6 | | 33 | 31 | | 34 | 4 | | 35 | 68 | | 36 | 13 | | 37 | 64 | | 38 | 18 | | 39 | 14 | | 40 | 64 | | 41 | 10 | | 42 | 33 | | 43 | 4 | | 44 | 22 | | 45 | 4 |
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| 80.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 72 | | matches | | 0 | "were curled" | | 1 | "were furred" | | 2 | "been bored" | | 3 | "been etched" | | 4 | "was filmed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 110 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 102 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 661 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.024205748865355523 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0045385779122541605 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 10.37 | | std | 7.99 | | cv | 0.771 | | sampleLengths | | 0 | 20 | | 1 | 7 | | 2 | 12 | | 3 | 11 | | 4 | 2 | | 5 | 1 | | 6 | 7 | | 7 | 9 | | 8 | 13 | | 9 | 2 | | 10 | 6 | | 11 | 18 | | 12 | 3 | | 13 | 13 | | 14 | 12 | | 15 | 4 | | 16 | 4 | | 17 | 5 | | 18 | 5 | | 19 | 4 | | 20 | 20 | | 21 | 7 | | 22 | 7 | | 23 | 25 | | 24 | 27 | | 25 | 6 | | 26 | 5 | | 27 | 20 | | 28 | 2 | | 29 | 7 | | 30 | 6 | | 31 | 5 | | 32 | 31 | | 33 | 6 | | 34 | 4 | | 35 | 2 | | 36 | 4 | | 37 | 11 | | 38 | 1 | | 39 | 37 | | 40 | 11 | | 41 | 25 | | 42 | 3 | | 43 | 7 | | 44 | 22 | | 45 | 10 | | 46 | 7 | | 47 | 18 | | 48 | 3 | | 49 | 2 |
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| 75.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5 | | totalSentences | 102 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 66.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 60 | | matches | | 0 | "Her torch cut a clean" | | 1 | "She moved the torch along" | | 2 | "They were curled, half-closed, like" | | 3 | "She had a face on." | | 4 | "She supposed she did." | | 5 | "She stood and swept the" | | 6 | "She traced the air above" | | 7 | "She shone the torch along" | | 8 | "She moved back to the" | | 9 | "She angled the light" | | 10 | "She turned the man's curled" | | 11 | "She'd seen its like once" | | 12 | "Her thumb went cold against" | | 13 | "He drew out a scrap" | | 14 | "Her pulse ticked faster, the" | | 15 | "She slipped it into an" | | 16 | "She rose, knees protesting, and" | | 17 | "They carried on." | | 18 | "Her voice came flat" | | 19 | "He followed her torch beam" |
| | ratio | 0.383 | |
| 51.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 60 | | matches | | 0 | "The body lay where the" | | 1 | "Quinn crouched beside it without" | | 2 | "Her torch cut a clean" | | 3 | "A bruise blooming purple along" | | 4 | "Sergeant Davies said behind her" | | 5 | "Davies clicked his pen" | | 6 | "Quinn didn't answer." | | 7 | "She moved the torch along" | | 8 | "They were curled, half-closed, like" | | 9 | "She had a face on." | | 10 | "She supposed she did." | | 11 | "Davies sighed and flipped his" | | 12 | "She stood and swept the" | | 13 | "The tiles were furred with" | | 14 | "Quinn knelt at the edge" | | 15 | "She traced the air above" | | 16 | "Davies leaned in, frowning." | | 17 | "She shone the torch along" | | 18 | "The grime sat smooth and" | | 19 | "Davies straightened slowly." |
| | ratio | 0.817 | |
| 83.33% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 60 | | matches | | | ratio | 0.017 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "They were curled, half-closed, like a hand that had been holding something." | | 1 | "The scorch radiated outward from a single point, a rough circle no bigger than a coin, as though something small and burning had pressed against him." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn agreed (agree)" | | 1 | "She rose knees protesting (protest)" |
| | dialogueSentences | 48 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.25 | | effectiveRatio | 0.083 | |