| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow crouched again [again]" |
| | dialogueSentences | 22 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0.083 | | effectiveRatio | 0.083 | |
| 73.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1325 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "very" | | 1 | "completely" | | 2 | "gently" | | 3 | "slightly" | | 4 | "lazily" | | 5 | "softly" |
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| 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) | |
| 73.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1325 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "shattered" | | 1 | "scanning" | | 2 | "chaotic" | | 3 | "intricate" | | 4 | "etched" | | 5 | "whisper" | | 6 | "chill" |
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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 | 70 | | matches | (empty) | |
| 61.22% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 70 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 80 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1325 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 46.91% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 970 | | uniqueNames | 17 | | maxNameDensity | 2.06 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 20 | | Quinn | 2 | | Camden | 1 | | Tube | 2 | | Veil | 2 | | Market | 2 | | Kowalski | 1 | | Eva | 11 | | Victorian | 1 | | Metropolitan | 1 | | Police | 1 | | Morris | 2 | | British | 1 | | Museum | 1 | | Ancient | 1 | | History | 1 | | Oxford | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Police" | | 6 | "Morris" | | 7 | "Museum" |
| | places | | | globalScore | 0.469 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | 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 | 1325 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 80 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 57.61 | | std | 37.92 | | cv | 0.658 | | sampleLengths | | 0 | 141 | | 1 | 113 | | 2 | 11 | | 3 | 60 | | 4 | 21 | | 5 | 37 | | 6 | 56 | | 7 | 58 | | 8 | 108 | | 9 | 46 | | 10 | 22 | | 11 | 85 | | 12 | 37 | | 13 | 14 | | 14 | 25 | | 15 | 50 | | 16 | 5 | | 17 | 57 | | 18 | 65 | | 19 | 123 | | 20 | 32 | | 21 | 46 | | 22 | 113 |
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| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 70 | | matches | | 0 | "were dismantled" | | 1 | "was curled" |
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| 72.61% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 157 | | matches | | 0 | "was not pointing" | | 1 | "was not pointing" | | 2 | "was spinning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 80 | | ratio | 0.013 | | matches | | 0 | "The evidence had not just failed to add up; it had been meticulously curated to lie." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 978 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.028629856850715747 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.016359918200409 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 80 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 80 | | mean | 16.56 | | std | 10.51 | | cv | 0.634 | | sampleLengths | | 0 | 24 | | 1 | 12 | | 2 | 25 | | 3 | 28 | | 4 | 10 | | 5 | 5 | | 6 | 14 | | 7 | 23 | | 8 | 10 | | 9 | 15 | | 10 | 6 | | 11 | 13 | | 12 | 9 | | 13 | 4 | | 14 | 16 | | 15 | 20 | | 16 | 5 | | 17 | 15 | | 18 | 11 | | 19 | 5 | | 20 | 14 | | 21 | 10 | | 22 | 16 | | 23 | 15 | | 24 | 17 | | 25 | 4 | | 26 | 16 | | 27 | 21 | | 28 | 9 | | 29 | 23 | | 30 | 24 | | 31 | 13 | | 32 | 45 | | 33 | 4 | | 34 | 41 | | 35 | 24 | | 36 | 23 | | 37 | 16 | | 38 | 10 | | 39 | 36 | | 40 | 9 | | 41 | 13 | | 42 | 23 | | 43 | 7 | | 44 | 15 | | 45 | 22 | | 46 | 18 | | 47 | 7 | | 48 | 10 | | 49 | 20 |
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| 60.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3875 | | totalSentences | 80 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 69 | | matches | | 0 | "She moved with military precision," | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "It was too neat." | | 3 | "He had not been fighting." | | 4 | "He had been standing still," | | 5 | "She recognized the nervous rustle" | | 6 | "She clutched her worn leather" | | 7 | "She still did not understand" | | 8 | "It was not pointing toward" | | 9 | "It was spinning lazily, completely" | | 10 | "She looked down at the" | | 11 | "She had spent three years" | | 12 | "She did not know where" |
| | ratio | 0.188 | |
| 25.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 69 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "The air down here was" | | 2 | "This was the Veil Market," | | 3 | "The underground market moved locations" | | 4 | "Harlow checked the worn leather" | | 5 | "She moved with military precision," | | 6 | "Her closely cropped salt-and-pepper hair" | | 7 | "The victim lay sprawled near" | | 8 | "A slashed throat, empty pockets," | | 9 | "Harlow crouched, her brown eyes" | | 10 | "It was too neat." | | 11 | "A slashed throat in a" | | 12 | "This blood had pooled heavily" | | 13 | "He had not been fighting." | | 14 | "He had been standing still," | | 15 | "a voice said" | | 16 | "Harlow did not look up." | | 17 | "She recognized the nervous rustle" | | 18 | "Eva Kowalski stepped into the" | | 19 | "The twenty-six-year-old occult researcher adjusted" |
| | ratio | 0.87 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 69 | | matches | | 0 | "Whoever had done this understood" | | 1 | "Now, the Veil Market had" |
| | ratio | 0.029 | |
| 76.41% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 4 | | matches | | 0 | "Eighteen years of decorated service on the Metropolitan Police had taught her to trust physical evidence over fairy tales, but three years ago, she had lost her…" | | 1 | "She still did not understand the supernatural mechanics of the unexplained circumstances that took Morris, but she knew better than to dismiss Eva's expertise." | | 2 | "The casing was heavily oxidized, coated in a thick patina of verdigris that flaked slightly against her blue nitrile glove." | | 3 | "She had spent three years trying to understand the forces that took her partner, chasing whispers and dead ends through a city that kept its darkest secrets bur…" |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow said, her voice a low, steady rasp" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 4 | | fancyTags | | 0 | "Eva explained (explain)" | | 1 | "Harlow murmured (murmur)" | | 2 | "Harlow noted (note)" | | 3 | "Harlow confirmed (confirm)" |
| | dialogueSentences | 22 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0.4 | | effectiveRatio | 0.364 | |