| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.385 | | leniency | 0.769 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1540 | | 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) | |
| 87.01% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1540 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "fluttered" | | 1 | "etched" | | 2 | "flicked" | | 3 | "trembled" |
| |
| 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 | 26 | | matches | (empty) | |
| 32.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 26 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 33 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 122 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1540 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1457 | | uniqueNames | 14 | | maxNameDensity | 1.17 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 17 | | Tube | 1 | | Camden | 1 | | Kowalski | 1 | | Eva | 9 | | Veil | 2 | | Market | 2 | | Daniel | 2 | | Reyes | 1 | | Rite | 1 | | Shade | 1 | | Dust | 4 | | Blood | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Market" | | 5 | "Daniel" | | 6 | "Reyes" | | 7 | "Dust" | | 8 | "Blood" |
| | places | | | globalScore | 0.917 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 19 | | 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 | 1540 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 33 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 73.33 | | std | 43.13 | | cv | 0.588 | | sampleLengths | | 0 | 105 | | 1 | 95 | | 2 | 106 | | 3 | 103 | | 4 | 7 | | 5 | 89 | | 6 | 88 | | 7 | 16 | | 8 | 14 | | 9 | 106 | | 10 | 109 | | 11 | 86 | | 12 | 12 | | 13 | 12 | | 14 | 110 | | 15 | 108 | | 16 | 122 | | 17 | 105 | | 18 | 112 | | 19 | 5 | | 20 | 30 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 26 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 199 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 149 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 0 | | adverbRatio | 0 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 33 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 33 | | mean | 46.67 | | std | 47 | | cv | 1.007 | | sampleLengths | | 0 | 24 | | 1 | 7 | | 2 | 5 | | 3 | 8 | | 4 | 61 | | 5 | 95 | | 6 | 106 | | 7 | 103 | | 8 | 7 | | 9 | 89 | | 10 | 88 | | 11 | 5 | | 12 | 8 | | 13 | 3 | | 14 | 6 | | 15 | 4 | | 16 | 4 | | 17 | 106 | | 18 | 109 | | 19 | 86 | | 20 | 6 | | 21 | 6 | | 22 | 5 | | 23 | 3 | | 24 | 4 | | 25 | 110 | | 26 | 108 | | 27 | 122 | | 28 | 105 | | 29 | 112 | | 30 | 5 | | 31 | 4 | | 32 | 26 |
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| 77.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5757575757575758 | | totalSentences | 33 | | uniqueOpeners | 19 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 25 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 2 | | totalSentences | 25 | | matches | | 0 | "Her sharp jaw set under" | | 1 | "She checked the worn leather" |
| | ratio | 0.08 | |
| 20.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 22 | | totalSentences | 25 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "Salt-and-pepper hair cropped short brushed" | | 2 | "Brown eyes swept the platform." | | 3 | "Her sharp jaw set under" | | 4 | "She checked the worn leather" | | 5 | "The station swallowed sound. Water" | | 6 | "A young man lay on" | | 7 | "Eva Kowalski stood at the" | | 8 | "Quinn glanced at Eva, then" | | 9 | "DC Daniel Reyes crouched by" | | 10 | "The compass found it." | | 11 | "The rift took" | | 12 | "Quinn turned the compass in" | | 13 | "Quinn opened the dead man's" | | 14 | "Eva dug in the satchel" | | 15 | "Rite for show." | | 16 | "Quinn crouched and studied the" | | 17 | "Quinn swept the beam along" | | 18 | "Quinn followed the third set" | | 19 | "Quinn picked up the compass" |
| | ratio | 0.88 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 25 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 3 | | matches | | 0 | "Quinn swept the beam along the platform. Dust lay thick across the tiles except where shoes had scuffed it. Daniel's boots had cut a fresh path from the cordon …" | | 1 | "Quinn followed the third set of prints back toward the tunnel mouth. The prints overlapped beside a drain grating set into the platform edge. Dark stains filled…" | | 2 | "Quinn looked at the tarpaulin. Dust lay unbroken across the cloth. The market had closed before the killing, because no trader had touched the crates after the …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |