| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.431 | | leniency | 0.863 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.70% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1205 | | totalAiIsmAdverbs | 2 | | 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) | |
| 25.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1205 | | totalAiIsms | 18 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | word | "skipped a beat" | | count | 1 |
| | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | |
| | highlights | | 0 | "determined" | | 1 | "scanning" | | 2 | "glinting" | | 3 | "etched" | | 4 | "furrowing" | | 5 | "racing" | | 6 | "tracing" | | 7 | "silence" | | 8 | "skipped a beat" | | 9 | "weight" | | 10 | "familiar" | | 11 | "facade" | | 12 | "looming" | | 13 | "raced" | | 14 | "resolve" | | 15 | "delved" | | 16 | "gleaming" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "The air was thick with" |
| |
| 92.95% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 78 | | matches | | 0 | "filled with fear" | | 1 | "d with fear" | | 2 | "g with determination" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1203 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 795 | | uniqueNames | 14 | | maxNameDensity | 3.27 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 26 | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 1 | | Harper | 10 | | Morris | 1 | | Aurora | 1 | | British | 2 | | Museum | 2 | | Eva | 11 | | Compass | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Harper" | | 4 | "Morris" | | 5 | "Aurora" | | 6 | "Eva" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | 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 | 1203 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 106 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 24.06 | | std | 13.45 | | cv | 0.559 | | sampleLengths | | 0 | 71 | | 1 | 38 | | 2 | 15 | | 3 | 22 | | 4 | 55 | | 5 | 7 | | 6 | 27 | | 7 | 23 | | 8 | 14 | | 9 | 15 | | 10 | 30 | | 11 | 19 | | 12 | 13 | | 13 | 20 | | 14 | 45 | | 15 | 19 | | 16 | 12 | | 17 | 22 | | 18 | 34 | | 19 | 35 | | 20 | 8 | | 21 | 24 | | 22 | 16 | | 23 | 36 | | 24 | 19 | | 25 | 44 | | 26 | 30 | | 27 | 7 | | 28 | 16 | | 29 | 16 | | 30 | 11 | | 31 | 19 | | 32 | 55 | | 33 | 38 | | 34 | 15 | | 35 | 7 | | 36 | 19 | | 37 | 18 | | 38 | 25 | | 39 | 10 | | 40 | 25 | | 41 | 11 | | 42 | 24 | | 43 | 27 | | 44 | 21 | | 45 | 15 | | 46 | 31 | | 47 | 43 | | 48 | 22 | | 49 | 15 |
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| 78.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 78 | | matches | | 0 | "were torn" | | 1 | "was etched" | | 2 | "were covered" | | 3 | "were covered" | | 4 | "were drawn" | | 5 | "were connected" |
| |
| 12.21% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 142 | | matches | | 0 | "was racing" | | 1 | "was closing" | | 2 | "was waiting" | | 3 | "were falling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 106 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 797 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, leather-bound book" |
| | adverbCount | 15 | | adverbRatio | 0.018820577164366373 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006273525721455458 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 11.35 | | std | 4.82 | | cv | 0.425 | | sampleLengths | | 0 | 22 | | 1 | 15 | | 2 | 15 | | 3 | 19 | | 4 | 9 | | 5 | 14 | | 6 | 15 | | 7 | 10 | | 8 | 5 | | 9 | 10 | | 10 | 12 | | 11 | 17 | | 12 | 12 | | 13 | 13 | | 14 | 13 | | 15 | 7 | | 16 | 15 | | 17 | 12 | | 18 | 7 | | 19 | 16 | | 20 | 3 | | 21 | 11 | | 22 | 6 | | 23 | 9 | | 24 | 8 | | 25 | 12 | | 26 | 10 | | 27 | 13 | | 28 | 6 | | 29 | 13 | | 30 | 12 | | 31 | 8 | | 32 | 12 | | 33 | 14 | | 34 | 19 | | 35 | 11 | | 36 | 8 | | 37 | 12 | | 38 | 11 | | 39 | 11 | | 40 | 6 | | 41 | 14 | | 42 | 14 | | 43 | 13 | | 44 | 14 | | 45 | 8 | | 46 | 8 | | 47 | 7 | | 48 | 17 | | 49 | 12 |
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| 63.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.3867924528301887 | | totalSentences | 106 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 98.95% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 76 | | matches | | 0 | "She adjusted the collar of" | | 1 | "It was DS Harper, a" | | 2 | "He stood near the entrance," | | 3 | "he said, pointing to a" | | 4 | "His clothes were torn, and" | | 5 | "She knelt beside him, her" | | 6 | "she asked, her voice steady" | | 7 | "It was etched with strange" | | 8 | "They were covered in cuts" | | 9 | "They moved through the abandoned" | | 10 | "she muttered, tracing the symbol" | | 11 | "He had heard the stories" | | 12 | "They continued their search, their" | | 13 | "She picked it up, brushing" | | 14 | "She hadn't seen her in" | | 15 | "They made their way back" | | 16 | "It all pointed to something" | | 17 | "It rang twice before a" | | 18 | "They drove through the quiet" | | 19 | "She couldn't shake the feeling" |
| | ratio | 0.303 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 76 | | matches | | 0 | "Detective Harlow Quinn stepped out" | | 1 | "The air was thick with" | | 2 | "She adjusted the collar of" | | 3 | "The Veil Market had moved" | | 4 | "It was DS Harper, a" | | 5 | "He stood near the entrance," | | 6 | "Harlow approached, her eyes scanning" | | 7 | "he said, pointing to a" | | 8 | "Harlow followed the beam of" | | 9 | "His clothes were torn, and" | | 10 | "She knelt beside him, her" | | 11 | "The man's eyes were wide" | | 12 | "she asked, her voice steady" | | 13 | "Harper said, holding up a" | | 14 | "It was etched with strange" | | 15 | "Harlow took the compass, her" | | 16 | "Harper looked puzzled." | | 17 | "Harlow said, standing up" | | 18 | "Harper shone his flashlight on" | | 19 | "They were covered in cuts" |
| | ratio | 0.921 | |
| 65.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 76 | | matches | | 0 | "Now, standing in the eerie" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 1 | | matches | | 0 | "Eva was Aurora's childhood friend, an occult researcher who worked at the British Museum." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 11 | | matches | | 0 | "she asked, her voice steady" | | 1 | "Harlow said, her mind racing" | | 2 | "Harper asked, his skepticism evident" | | 3 | "Harlow said, her voice firm" | | 4 | "Harlow said, her voice tight" | | 5 | "Harlow said, her tone leaving no room for argument" | | 6 | "Eva said, and the line went dead" | | 7 | "Harlow asked, her voice urgent" | | 8 | "Eva said, her voice trembling" | | 9 | "Harlow said, her voice steady" | | 10 | "Harlow said, her eyes gleaming with determination" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 22 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Harper added (add)" |
| | dialogueSentences | 51 | | tagDensity | 0.431 | | leniency | 0.863 | | rawRatio | 0.091 | | effectiveRatio | 0.078 | |