| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.321 | | leniency | 0.643 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 532 | | 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) | |
| 15.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 532 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "shattered" | | 1 | "grave" | | 2 | "pulsed" | | 3 | "shimmered" | | 4 | "tracing" | | 5 | "flickered" | | 6 | "trembled" | | 7 | "echoed" | | 8 | "stomach" |
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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 | 64 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 64 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 528 | | ratio | 0.004 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 401 | | uniqueNames | 10 | | maxNameDensity | 3.24 | | worstName | "Eva" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Veil | 1 | | Compass | 1 | | Eva | 13 | | Harlow | 11 | | Quinn | 1 | | Tube | 1 | | Runes | 1 | | Elephant | 1 | | Castle | 1 | | Morris | 1 |
| | persons | | 0 | "Compass" | | 1 | "Eva" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Runes" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0.167 | |
| 69.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed thicker suddenly" |
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| 10.61% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.894 | | wordCount | 528 | | matches | | 0 | "Not from fire—from something with heat but no flame" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 13.89 | | std | 9.26 | | cv | 0.666 | | sampleLengths | | 0 | 18 | | 1 | 23 | | 2 | 32 | | 3 | 4 | | 4 | 11 | | 5 | 32 | | 6 | 9 | | 7 | 30 | | 8 | 22 | | 9 | 4 | | 10 | 34 | | 11 | 2 | | 12 | 15 | | 13 | 12 | | 14 | 14 | | 15 | 24 | | 16 | 11 | | 17 | 16 | | 18 | 2 | | 19 | 11 | | 20 | 9 | | 21 | 15 | | 22 | 10 | | 23 | 6 | | 24 | 31 | | 25 | 2 | | 26 | 27 | | 27 | 9 | | 28 | 17 | | 29 | 10 | | 30 | 13 | | 31 | 15 | | 32 | 3 | | 33 | 4 | | 34 | 7 | | 35 | 6 | | 36 | 9 | | 37 | 9 |
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| 99.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 64 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 70 | | matches | (empty) | |
| 5.16% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 83 | | ratio | 0.048 | | matches | | 0 | "The abandoned Tube station reeked of burnt sage and something metallic—iron, maybe, or worse." | | 1 | "The stall’s contents were scattered—herbs, bones, a ledger with entries in no language she recognised." | | 2 | "Not from fire—from something with heat but no flame." | | 3 | "The needle spun, then snapped—pointing directly at Harlow." |
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| 89.63% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 405 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.05185185185185185 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.012345679012345678 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 6.36 | | std | 4.28 | | cv | 0.673 | | sampleLengths | | 0 | 18 | | 1 | 12 | | 2 | 11 | | 3 | 14 | | 4 | 14 | | 5 | 4 | | 6 | 4 | | 7 | 4 | | 8 | 7 | | 9 | 17 | | 10 | 13 | | 11 | 2 | | 12 | 7 | | 13 | 2 | | 14 | 6 | | 15 | 10 | | 16 | 4 | | 17 | 10 | | 18 | 15 | | 19 | 4 | | 20 | 3 | | 21 | 4 | | 22 | 2 | | 23 | 22 | | 24 | 5 | | 25 | 5 | | 26 | 2 | | 27 | 13 | | 28 | 2 | | 29 | 3 | | 30 | 8 | | 31 | 1 | | 32 | 14 | | 33 | 10 | | 34 | 4 | | 35 | 2 | | 36 | 1 | | 37 | 7 | | 38 | 9 | | 39 | 2 | | 40 | 6 | | 41 | 10 | | 42 | 2 | | 43 | 6 | | 44 | 5 | | 45 | 5 | | 46 | 4 | | 47 | 4 | | 48 | 11 | | 49 | 6 |
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| 74.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4819277108433735 | | totalSentences | 83 | | uniqueOpeners | 40 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 56 | | matches | | 0 | "Too many loose ends." | | 1 | "Too many cases that went" | | 2 | "Too precise for claws." | | 3 | "Then the ground shook." |
| | ratio | 0.071 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 56 | | matches | | 0 | "she muttered, tucking a loose" | | 1 | "Her sharp jaw tightened." | | 2 | "She flipped a page." | | 3 | "She crouched, tracing a finger" | | 4 | "She’d seen marks like that" | | 5 | "Her grip was ice." |
| | ratio | 0.107 | |
| 40.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 56 | | matches | | 0 | "The needle of the Veil" | | 1 | "she muttered, tucking a loose" | | 2 | "The motion left a smudge" | | 3 | "Detective Harlow Quinn stepped over" | | 4 | "The abandoned Tube station reeked" | | 5 | "Her sharp jaw tightened." | | 6 | "Eva didn’t look up." | | 7 | "Harlow nudged a toppled market" | | 8 | "Eva snapped the compass shut" | | 9 | "Harlow’s watch ticked against her" | | 10 | "The stall’s contents were scattered—herbs," | | 11 | "She flipped a page." | | 12 | "The ink shimmered." | | 13 | "She crouched, tracing a finger" | | 14 | "The dust came away black." | | 15 | "Eva stood, wiping her hands" | | 16 | "Harlow’s torch flickered." | | 17 | "The shadows between the pillars" | | 18 | "Eva nodded toward a uniformed" | | 19 | "Harlow studied the groove" |
| | ratio | 0.839 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 11 | | technicalSentenceCount | 1 | | matches | | 0 | "Too many cases that went cold with no logical explanation." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Eva snapped (snap)" |
| | dialogueSentences | 28 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 1 | | effectiveRatio | 0.143 | |