| 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 | 1797 | | 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) | |
| 88.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1797 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "sense of" | | 1 | "weight" | | 2 | "stark" |
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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 | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 145 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 79 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1801 | | 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 | 36 | | wordCount | 1224 | | uniqueNames | 8 | | maxNameDensity | 1.23 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 15 | | Camden | 1 | | Tube | 1 | | Farah | 1 | | Noor | 9 | | Kowalski | 1 | | Eva | 7 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tube" | | 3 | "Farah" | | 4 | "Noor" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | | | globalScore | 0.887 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | 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 | 1801 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 29.52 | | std | 27.33 | | cv | 0.926 | | sampleLengths | | 0 | 98 | | 1 | 5 | | 2 | 13 | | 3 | 49 | | 4 | 10 | | 5 | 77 | | 6 | 63 | | 7 | 18 | | 8 | 60 | | 9 | 65 | | 10 | 5 | | 11 | 4 | | 12 | 28 | | 13 | 50 | | 14 | 47 | | 15 | 78 | | 16 | 5 | | 17 | 45 | | 18 | 83 | | 19 | 9 | | 20 | 67 | | 21 | 17 | | 22 | 5 | | 23 | 3 | | 24 | 39 | | 25 | 4 | | 26 | 2 | | 27 | 45 | | 28 | 12 | | 29 | 23 | | 30 | 14 | | 31 | 9 | | 32 | 75 | | 33 | 18 | | 34 | 8 | | 35 | 6 | | 36 | 4 | | 37 | 21 | | 38 | 80 | | 39 | 6 | | 40 | 5 | | 41 | 8 | | 42 | 5 | | 43 | 11 | | 44 | 93 | | 45 | 8 | | 46 | 65 | | 47 | 7 | | 48 | 25 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 145 | | ratio | 0.014 | | matches | | 0 | "From the stair — the only gate, the constable had said, bone checked at the top — the prints belonged to police and to the smaller pair." | | 1 | "It had weight, and the wrong kind — light, chalky at a chip on the rim." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1227 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.017114914425427872 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004889975550122249 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 145 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 145 | | mean | 12.42 | | std | 12.19 | | cv | 0.981 | | sampleLengths | | 0 | 16 | | 1 | 43 | | 2 | 12 | | 3 | 27 | | 4 | 5 | | 5 | 13 | | 6 | 3 | | 7 | 6 | | 8 | 20 | | 9 | 1 | | 10 | 14 | | 11 | 5 | | 12 | 10 | | 13 | 20 | | 14 | 31 | | 15 | 13 | | 16 | 4 | | 17 | 1 | | 18 | 8 | | 19 | 18 | | 20 | 2 | | 21 | 25 | | 22 | 18 | | 23 | 18 | | 24 | 60 | | 25 | 2 | | 26 | 10 | | 27 | 5 | | 28 | 2 | | 29 | 5 | | 30 | 11 | | 31 | 7 | | 32 | 23 | | 33 | 5 | | 34 | 4 | | 35 | 28 | | 36 | 12 | | 37 | 24 | | 38 | 4 | | 39 | 10 | | 40 | 22 | | 41 | 3 | | 42 | 2 | | 43 | 5 | | 44 | 15 | | 45 | 78 | | 46 | 5 | | 47 | 45 | | 48 | 10 | | 49 | 8 |
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| 53.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3611111111111111 | | totalSentences | 144 | | uniqueOpeners | 52 | |
| 31.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 105 | | matches | | | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 105 | | matches | | 0 | "She checked the worn leather" | | 1 | "She walked the platform with" | | 2 | "He lay on his back" | | 3 | "She tipped her head until" | | 4 | "She turned it again." | | 5 | "She stood a clear head" | | 6 | "She studied the hand that" | | 7 | "She turned the wrist." | | 8 | "She went back to the" | | 9 | "She followed that smaller pair" | | 10 | "It had weight, and the" | | 11 | "Her hand rose, stopped, and" | | 12 | "She looked at Eva's cuffs," | | 13 | "She held it beside the" | | 14 | "She turned the empty one" | | 15 | "She set it on the" | | 16 | "She returned to the compass" | | 17 | "It shivered and swung back" | | 18 | "Her gloved fingers ran the" | | 19 | "She opened the stitch with" |
| | ratio | 0.21 | |
| 74.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 105 | | matches | | 0 | "Detective Harlow Quinn came down" | | 1 | "Oxblood tile curved over her" | | 2 | "Oil lanterns, not the dead" | | 3 | "A constable lifted the tape." | | 4 | "Quinn ducked under." | | 5 | "The tape brushed her cropped" | | 6 | "She checked the worn leather" | | 7 | "The market had no right" | | 8 | "She walked the platform with" | | 9 | "A pale skin of dust" | | 10 | "The soles were clean." | | 11 | "A shine still held along" | | 12 | "He lay on his back" | | 13 | "A bone token sat on" | | 14 | "DC Farah Noor stood at" | | 15 | "The vial under the man's" | | 16 | "She tipped her head until" | | 17 | "Noor tapped the page." | | 18 | "Quinn eased the compass from" | | 19 | "The needle lagged, slid, and" |
| | ratio | 0.771 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |