| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.211 | | leniency | 0.421 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1778 | | 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) | |
| 60.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1778 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "etched" | | 1 | "measured" | | 2 | "crystal" | | 3 | "standard" | | 4 | "silence" | | 5 | "spectral" | | 6 | "stark" | | 7 | "predator" | | 8 | "resolved" | | 9 | "pulse" | | 10 | "depths" | | 11 | "gloom" |
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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 | 126 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 126 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 141 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1767 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1250 | | uniqueNames | 10 | | maxNameDensity | 1.28 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 16 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Kowalski | 1 | | Eva | 15 | | Shade | 2 | | One | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" |
| | places | | | globalScore | 0.86 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.566 | | wordCount | 1767 | | matches | | 0 | "not north but toward a crack in the far wall" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 141 | | matches | | |
| 90.56% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 46.5 | | std | 21.71 | | cv | 0.467 | | sampleLengths | | 0 | 71 | | 1 | 62 | | 2 | 75 | | 3 | 37 | | 4 | 19 | | 5 | 19 | | 6 | 48 | | 7 | 40 | | 8 | 3 | | 9 | 50 | | 10 | 32 | | 11 | 8 | | 12 | 58 | | 13 | 41 | | 14 | 30 | | 15 | 39 | | 16 | 63 | | 17 | 58 | | 18 | 48 | | 19 | 59 | | 20 | 39 | | 21 | 48 | | 22 | 63 | | 23 | 100 | | 24 | 23 | | 25 | 43 | | 26 | 61 | | 27 | 58 | | 28 | 76 | | 29 | 52 | | 30 | 72 | | 31 | 30 | | 32 | 35 | | 33 | 62 | | 34 | 28 | | 35 | 34 | | 36 | 81 | | 37 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 205 | | matches | (empty) | |
| 1.01% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 141 | | ratio | 0.05 | | matches | | 0 | "Blood had soaked into the papers scattered around him—ledgers filled with ink that shifted under the light." | | 1 | "One illustration showed an identical compass.annotation in tight script noted its creator—a Shade artisan known for tools that pointed only when the user already stood near danger." | | 2 | "Marks gouged the brick just inside—three parallel lines, deep, tapering." | | 3 | "Someone had carried him in—cloaked in a dying glamour that wore his own boots—set the scene with the ledgers and powder crates, then fled as the glamour collapsed." | | 4 | "Under the skin a tattoo glowed faint green for one second before fading—sigil matching the book cover." | | 5 | "He had needed a later visitor—someone who would touch the brass and follow the needle into the tunnels." | | 6 | "The second interpretive layer Eva had offered—glamour carriage, living ink, rift residual—locked every piece into place." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1266 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.02527646129541864 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0039494470774091624 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 141 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 141 | | mean | 12.53 | | std | 10.38 | | cv | 0.828 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 20 | | 3 | 20 | | 4 | 15 | | 5 | 18 | | 6 | 7 | | 7 | 5 | | 8 | 17 | | 9 | 5 | | 10 | 16 | | 11 | 2 | | 12 | 2 | | 13 | 18 | | 14 | 7 | | 15 | 5 | | 16 | 20 | | 17 | 17 | | 18 | 20 | | 19 | 19 | | 20 | 2 | | 21 | 5 | | 22 | 12 | | 23 | 13 | | 24 | 22 | | 25 | 13 | | 26 | 4 | | 27 | 14 | | 28 | 8 | | 29 | 4 | | 30 | 10 | | 31 | 3 | | 32 | 4 | | 33 | 6 | | 34 | 5 | | 35 | 35 | | 36 | 4 | | 37 | 2 | | 38 | 15 | | 39 | 4 | | 40 | 7 | | 41 | 8 | | 42 | 36 | | 43 | 22 | | 44 | 4 | | 45 | 2 | | 46 | 8 | | 47 | 27 | | 48 | 10 | | 49 | 8 |
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| 64.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.41134751773049644 | | totalSentences | 141 | | uniqueOpeners | 58 | |
| 30.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 110 | | matches | | 0 | "Only a scatter of coins" |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 110 | | matches | | 0 | "Her worn leather watch caught" | | 1 | "She adjusted the strap without" | | 2 | "Its needle pointed not north" | | 3 | "She tucked a curl of" | | 4 | "Her sharp jaw set hard." | | 5 | "She pointed with two fingers" | | 6 | "She pushed it back again." | | 7 | "She watched Eva's face." | | 8 | "She set the book down" | | 9 | "She turned a page of" | | 10 | "She turned back." | | 11 | "Her voice stayed level." | | 12 | "She kept the compass in" | | 13 | "She stepped toward it." | | 14 | "She measured them against her" | | 15 | "She recovered, spoke low so" | | 16 | "He had died elsewhere, throat" | | 17 | "She turned the brass casing" | | 18 | "She moved back to the" | | 19 | "Her watch face turned." |
| | ratio | 0.245 | |
| 64.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 110 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The air hung thick with" | | 2 | "Her worn leather watch caught" | | 3 | "She adjusted the strap without" | | 4 | "Bone tokens clinked in the" | | 5 | "A body lay half-sprawled over" | | 6 | "Blood had soaked into the" | | 7 | "Quinn crouched beside the corpse." | | 8 | "Military precision guided her gloved" | | 9 | "The casing bore a patina" | | 10 | "Its needle pointed not north" | | 11 | "Eva Kowalski stood three metres" | | 12 | "She tucked a curl of" | | 13 | "Her sharp jaw set hard." | | 14 | "Eva stepped closer, boots careful" | | 15 | "She pointed with two fingers" | | 16 | "Quinn circled the body." | | 17 | "Boot marks in the dust" | | 18 | "The leaving prints matched the" | | 19 | "That did not fit." |
| | ratio | 0.791 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 110 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 1 | | matches | | 0 | "The edges of the stain sky-edged in places, as if something had licked them clean of scent markers." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |