| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1630 | | 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) | |
| 87.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1630 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "flicked" | | 1 | "pulse" | | 2 | "weight" |
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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 | 135 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 135 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 178 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1630 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1397 | | uniqueNames | 11 | | maxNameDensity | 1.22 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 17 | | Raven | 1 | | Nest | 1 | | Christopher | 1 | | Camden | 1 | | Town | 1 | | Veil | 2 | | Market | 1 | | Information | 1 | | Herrera | 13 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Christopher" | | 5 | "Market" | | 6 | "Herrera" |
| | places | | | globalScore | 0.892 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | 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 | 1630 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 178 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 91 | | mean | 17.91 | | std | 21.34 | | cv | 1.191 | | sampleLengths | | 0 | 93 | | 1 | 57 | | 2 | 5 | | 3 | 1 | | 4 | 50 | | 5 | 20 | | 6 | 10 | | 7 | 6 | | 8 | 13 | | 9 | 49 | | 10 | 42 | | 11 | 3 | | 12 | 6 | | 13 | 75 | | 14 | 8 | | 15 | 60 | | 16 | 3 | | 17 | 31 | | 18 | 17 | | 19 | 1 | | 20 | 16 | | 21 | 4 | | 22 | 5 | | 23 | 14 | | 24 | 8 | | 25 | 4 | | 26 | 5 | | 27 | 6 | | 28 | 23 | | 29 | 2 | | 30 | 48 | | 31 | 1 | | 32 | 3 | | 33 | 2 | | 34 | 3 | | 35 | 88 | | 36 | 13 | | 37 | 4 | | 38 | 3 | | 39 | 7 | | 40 | 5 | | 41 | 9 | | 42 | 4 | | 43 | 7 | | 44 | 54 | | 45 | 3 | | 46 | 5 | | 47 | 21 | | 48 | 48 | | 49 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 135 | | matches | | 0 | "been closed" | | 1 | "been used" |
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| 80.95% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 224 | | matches | | 0 | "was running" | | 1 | "was descending" | | 2 | "was already eating" | | 3 | "was running" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 178 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1403 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.023521026372059873 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0014255167498218105 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 178 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 178 | | mean | 9.16 | | std | 6.25 | | cv | 0.683 | | sampleLengths | | 0 | 11 | | 1 | 27 | | 2 | 14 | | 3 | 22 | | 4 | 10 | | 5 | 9 | | 6 | 10 | | 7 | 2 | | 8 | 4 | | 9 | 6 | | 10 | 35 | | 11 | 5 | | 12 | 1 | | 13 | 7 | | 14 | 15 | | 15 | 6 | | 16 | 22 | | 17 | 4 | | 18 | 16 | | 19 | 10 | | 20 | 6 | | 21 | 10 | | 22 | 3 | | 23 | 13 | | 24 | 12 | | 25 | 6 | | 26 | 7 | | 27 | 11 | | 28 | 7 | | 29 | 18 | | 30 | 6 | | 31 | 11 | | 32 | 3 | | 33 | 2 | | 34 | 4 | | 35 | 9 | | 36 | 25 | | 37 | 17 | | 38 | 9 | | 39 | 15 | | 40 | 8 | | 41 | 13 | | 42 | 12 | | 43 | 6 | | 44 | 13 | | 45 | 16 | | 46 | 3 | | 47 | 11 | | 48 | 20 | | 49 | 10 |
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| 44.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3089887640449438 | | totalSentences | 178 | | uniqueOpeners | 55 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 130 | | matches | (empty) | | ratio | 0 | |
| 93.85% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 130 | | matches | | 0 | "He was two streets away," | | 1 | "She saw the flash of" | | 2 | "His curls clung to his" | | 3 | "His left sleeve rode high" | | 4 | "He broke left into an" | | 5 | "She sidestepped a bin and" | | 6 | "He looked back once." | | 7 | "He took a corner so" | | 8 | "He stayed up." | | 9 | "Her coat snagged on a" | | 10 | "She tore free, saw a" | | 11 | "He stumbled sideways, then vanished" | | 12 | "She hit the stairs three" | | 13 | "It was bone-white, no bigger" | | 14 | "His shaved head gleamed under" | | 15 | "He went through." | | 16 | "She twisted, and her palm" | | 17 | "He stepped back a half" | | 18 | "He moved through the crowd" | | 19 | "He tightened his grip." |
| | ratio | 0.315 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 122 | | totalSentences | 130 | | matches | | 0 | "Rain came in grey slabs" | | 1 | "Detective Harlow Quinn stood under" | | 2 | "The old maps and black-and-white" | | 3 | "The man who had slipped" | | 4 | "He was two streets away," | | 5 | "She saw the flash of" | | 6 | "A trinket for travellers." | | 7 | "His curls clung to his" | | 8 | "His left sleeve rode high" | | 9 | "Quinn dropped into the chase." | | 10 | "He broke left into an" | | 11 | "The sound of her boots" | | 12 | "Trash bags burst against her" | | 13 | "She sidestepped a bin and" | | 14 | "He looked back once." | | 15 | "He took a corner so" | | 16 | "He stayed up." | | 17 | "The street beyond narrowed into" | | 18 | "Steam vented from a drain" | | 19 | "Quinn went into the steam" |
| | ratio | 0.938 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 130 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 2 | | matches | | 0 | "His left sleeve rode high when he reached for a railing, and the pale line of the scar ran from wrist to elbow, old and straight, as if a blade had tried to mea…" | | 1 | "It was bone-white, no bigger than a match head, carved with a line that looked too much like a vein." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |