| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.29% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1180 | | totalAiIsmAdverbs | 3 | | 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) | |
| 57.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1180 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "flickered" | | 1 | "wavering" | | 2 | "unspoken" | | 3 | "etched" | | 4 | "standard" | | 5 | "navigating" |
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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 | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1177 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1177 | | uniqueNames | 18 | | maxNameDensity | 1.02 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Eva" | | discoveredNames | | Camden | 2 | | Tube | 3 | | Northern | 1 | | Line | 1 | | Morris | 4 | | Eva | 8 | | Oxford | 1 | | Converse | 1 | | Harlow | 12 | | Veil | 3 | | Market | 1 | | British | 1 | | Museum | 1 | | Shade | 2 | | Kael | 1 | | Voss | 7 | | Compasses | 1 | | Compass | 1 |
| | persons | | 0 | "Camden" | | 1 | "Line" | | 2 | "Morris" | | 3 | "Eva" | | 4 | "Converse" | | 5 | "Harlow" | | 6 | "Museum" | | 7 | "Voss" | | 8 | "Compass" |
| | places | | 0 | "Oxford" | | 1 | "Market" | | 2 | "British" |
| | globalScore | 0.99 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | 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 | 1177 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 98 | | matches | | 0 | "ignore that connection" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 47.08 | | std | 31.11 | | cv | 0.661 | | sampleLengths | | 0 | 111 | | 1 | 33 | | 2 | 100 | | 3 | 60 | | 4 | 79 | | 5 | 7 | | 6 | 24 | | 7 | 66 | | 8 | 5 | | 9 | 55 | | 10 | 75 | | 11 | 36 | | 12 | 7 | | 13 | 27 | | 14 | 25 | | 15 | 37 | | 16 | 7 | | 17 | 75 | | 18 | 22 | | 19 | 44 | | 20 | 70 | | 21 | 37 | | 22 | 20 | | 23 | 47 | | 24 | 108 |
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| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 98 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 218 | | matches | (empty) | |
| 55.39% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 98 | | ratio | 0.031 | | matches | | 0 | "Verdigris patina coated the casing, and etchings circled the face—except they were backwards." | | 1 | "Eva’s round glasses slipped down her freckled nose, and she tucked a strand of curly red hair behind her left ear—her nervous habit, the one they’d mocked each other for back at Oxford dorm." | | 2 | "Found his wallet in his satchel, along with three more Veil Compasses—all with forward protective sigils." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1188 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.025252525252525252 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.010101010101010102 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 12.01 | | std | 7.34 | | cv | 0.611 | | sampleLengths | | 0 | 21 | | 1 | 23 | | 2 | 20 | | 3 | 20 | | 4 | 27 | | 5 | 24 | | 6 | 6 | | 7 | 3 | | 8 | 17 | | 9 | 18 | | 10 | 15 | | 11 | 11 | | 12 | 13 | | 13 | 5 | | 14 | 21 | | 15 | 18 | | 16 | 17 | | 17 | 4 | | 18 | 19 | | 19 | 2 | | 20 | 6 | | 21 | 22 | | 22 | 34 | | 23 | 17 | | 24 | 7 | | 25 | 15 | | 26 | 9 | | 27 | 6 | | 28 | 24 | | 29 | 24 | | 30 | 12 | | 31 | 5 | | 32 | 8 | | 33 | 28 | | 34 | 19 | | 35 | 8 | | 36 | 1 | | 37 | 7 | | 38 | 3 | | 39 | 16 | | 40 | 4 | | 41 | 18 | | 42 | 6 | | 43 | 3 | | 44 | 9 | | 45 | 4 | | 46 | 3 | | 47 | 9 | | 48 | 20 | | 49 | 7 |
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| 49.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3469387755102041 | | totalSentences | 98 | | uniqueOpeners | 34 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 94 | | matches | | 0 | "Only this one’s reversed." | | 1 | "Also, we didn’t find a" | | 2 | "Only for a buyer who" |
| | ratio | 0.032 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 94 | | matches | | 0 | "It was 3:17 a.m., and" | | 1 | "She tucked a strand of" | | 2 | "She’d know those sigils anywhere." | | 3 | "She flipped open her wallet," | | 4 | "Her sharp jaw tightened." | | 5 | "She’d spent three years dismissing" | | 6 | "She stood, tucking the scrap" | | 7 | "Her leather satchel bulged with" | | 8 | "You didn’t call me about" | | 9 | "She nodded at the compass" | | 10 | "She’d called Eva at 2:45" | | 11 | "She’d spent three years trying" | | 12 | "He held up a pair" | | 13 | "She’d missed that." | | 14 | "She turned it over, her" | | 15 | "She’d initially written this off" | | 16 | "She’d assumed it was broken," | | 17 | "She walked over to the" | | 18 | "She grabbed it, pulling it" | | 19 | "She reached in, her fingers" |
| | ratio | 0.255 | |
| 98.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 94 | | matches | | 0 | "Harlow’s leather watch ticking steadily" | | 1 | "It was 3:17 a.m., and" | | 2 | "The Northern Line’s distant rumble" | | 3 | "Fairy lights strung between rusted" | | 4 | "A yellow plastic barrier cordoned" | | 5 | "She tucked a strand of" | | 6 | "This was not a teen" | | 7 | "This felt wrong." | | 8 | "Harlow stepped over the barrier," | | 9 | "The figure’s face was pale," | | 10 | "A small brass compass rested" | | 11 | "Harlow plucked it up, turning" | | 12 | "Verdigris patina coated the casing," | | 13 | "She’d know those sigils anywhere." | | 14 | "She flipped open her wallet," | | 15 | "The curves of the sigils" | | 16 | "Her sharp jaw tightened." | | 17 | "She’d spent three years dismissing" | | 18 | "A soft cough made her" | | 19 | "She stood, tucking the scrap" |
| | ratio | 0.723 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 94 | | matches | | 0 | "To trap the victim’s spirit" | | 1 | "To use their magic for" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 3 | | matches | | 0 | "Eva had spent weeks digging through the British Museum’s restricted archives for Harlow, tracking every mention of the hidden supernatural black market that shi…" | | 1 | "The same group that killed Morris was here, in this market, using the same twisted sigils to cover up their murder." | | 2 | "It added up to a cult, a smuggling ring, a group of people who hunted supernatural beings for profit." |
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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 | |