| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn said softly [softly]" |
| | dialogueSentences | 28 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0.1 | | effectiveRatio | 0.071 | |
| 89.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 957 | | 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) | |
| 68.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 957 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "standard" | | 1 | "pulsed" | | 2 | "trembled" | | 3 | "echoed" |
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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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 151 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 169 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 14 | | totalWords | 945 | | ratio | 0.015 | | matches | | 0 | "Why this one?" | | 1 | "exhaled" | | 2 | "Melted." | | 3 | "unmaking" | | 4 | "Intact." | | 5 | "Voynich? No. No, something newer." | | 6 | "up." | | 7 | "Ash." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 53.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 774 | | uniqueNames | 13 | | maxNameDensity | 1.94 | | worstName | "Eva" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Market | 2 | | Quinn | 12 | | Underground | 1 | | Pupils | 1 | | Eva | 15 | | Veil | 2 | | Compass | 2 | | Victorian | 1 | | Ticket | 1 | | Hall | 1 | | Like | 3 |
| | persons | | 0 | "Market" | | 1 | "Quinn" | | 2 | "Underground" | | 3 | "Pupils" | | 4 | "Eva" | | 5 | "Victorian" |
| | places | | | globalScore | 0.531 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like this before it bled" |
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| 94.18% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.058 | | wordCount | 945 | | matches | | 0 | "Not forced violently, but opened carefully, like someone had used a crowbar" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 169 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 35 | | std | 25.45 | | cv | 0.727 | | sampleLengths | | 0 | 49 | | 1 | 64 | | 2 | 48 | | 3 | 36 | | 4 | 39 | | 5 | 54 | | 6 | 52 | | 7 | 50 | | 8 | 59 | | 9 | 27 | | 10 | 3 | | 11 | 101 | | 12 | 6 | | 13 | 64 | | 14 | 5 | | 15 | 40 | | 16 | 18 | | 17 | 34 | | 18 | 79 | | 19 | 15 | | 20 | 43 | | 21 | 16 | | 22 | 3 | | 23 | 22 | | 24 | 6 | | 25 | 5 | | 26 | 7 |
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| 89.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 151 | | matches | | 0 | "was boarded" | | 1 | "been pried" | | 2 | "been invited" | | 3 | "been torn" | | 4 | "been scrubbed" | | 5 | "were frozen" | | 6 | "been forced" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 157 | | matches | | 0 | "was falling" | | 1 | "was breathing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 169 | | ratio | 0.059 | | matches | | 0 | "The Tube station exit was boarded up—plywood warped against the frame, padlocks rusted, the usual Camden graffiti tagging the barriers." | | 1 | "The air hit her like a wet slap—thick with the stench of wet stone and something else." | | 2 | "The tilework twisted in places—wrong angles, faded sigils peeking through grime." | | 3 | "Clothing designer labels—nothing stolen here." | | 4 | "But the residue remained—a faint silver shimmer under the station’s dim emergency lights." | | 5 | "Her watch—leather, tarnished at the clasp—clicked as she tapped it." | | 6 | "Walls lined with peeling posters—some old enough to be Victorian, advertising medicines no one took anymore." | | 7 | "Doors were warped—had been forced open, but not by human hands." | | 8 | "\"—it’s supposed to be carried away after passing.\" Quinn finished." | | 9 | "Pointing now not down—not into the tracks—but *up.* Toward the ceiling." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 695 | | adjectiveStacks | 1 | | stackExamples | | 0 | "thick leather-bound tome." |
| | adverbCount | 24 | | adverbRatio | 0.034532374100719423 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.014388489208633094 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 169 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 169 | | mean | 5.59 | | std | 4.22 | | cv | 0.755 | | sampleLengths | | 0 | 20 | | 1 | 5 | | 2 | 6 | | 3 | 12 | | 4 | 6 | | 5 | 3 | | 6 | 9 | | 7 | 17 | | 8 | 1 | | 9 | 1 | | 10 | 18 | | 11 | 2 | | 12 | 2 | | 13 | 11 | | 14 | 13 | | 15 | 12 | | 16 | 17 | | 17 | 3 | | 18 | 3 | | 19 | 4 | | 20 | 13 | | 21 | 12 | | 22 | 2 | | 23 | 3 | | 24 | 2 | | 25 | 4 | | 26 | 8 | | 27 | 3 | | 28 | 9 | | 29 | 8 | | 30 | 7 | | 31 | 3 | | 32 | 4 | | 33 | 11 | | 34 | 3 | | 35 | 3 | | 36 | 3 | | 37 | 13 | | 38 | 14 | | 39 | 3 | | 40 | 2 | | 41 | 3 | | 42 | 1 | | 43 | 1 | | 44 | 4 | | 45 | 3 | | 46 | 3 | | 47 | 5 | | 48 | 2 | | 49 | 8 |
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| 65.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.4378698224852071 | | totalSentences | 169 | | uniqueOpeners | 74 | |
| 93.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 107 | | matches | | 0 | "Maybe early twenties." | | 1 | "Too close to the body." | | 2 | "Just raw violation." |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 107 | | matches | | 0 | "She stepped between the planks" | | 1 | "She clutched her leather satchel" | | 2 | "Her curly red hair had" | | 3 | "She didn’t turn." | | 4 | "Her voice was steady, but" | | 5 | "Her green eyes were wide" | | 6 | "She pulled the Veil Compass" | | 7 | "It pointed straight down." | | 8 | "She gestured at the body" | | 9 | "Her watch—leather, tarnished at the" | | 10 | "She checked her watch against" | | 11 | "She pulled out a thick" |
| | ratio | 0.112 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 107 | | matches | | 0 | "The Tube station exit was" | | 1 | "The boards had been pried" | | 2 | "Quinn didn’t knock." | | 3 | "She stepped between the planks" | | 4 | "The air hit her like" | | 5 | "The scent of ozone after" | | 6 | "Eva stood halfway down the" | | 7 | "She clutched her leather satchel" | | 8 | "Her curly red hair had" | | 9 | "She didn’t turn." | | 10 | "Her voice was steady, but" | | 11 | "Quinn stepped over a discarded" | | 12 | "Eva finally looked over." | | 13 | "Her green eyes were wide" | | 14 | "Eva gestured down the platform" | | 15 | "Quinn moved forward." | | 16 | "The platform wasn’t stationery." | | 17 | "The tilework twisted in places—wrong" | | 18 | "These were older." | | 19 | "Vestiges of a time when" |
| | ratio | 0.654 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 107 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 19 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 28 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.25 | | effectiveRatio | 0.071 | |